Evaluation of Sustainable Agriculture Systems in Central Mexico
Item Type text; Electronic Dissertation
Authors Fernandez-Reynoso, Demetrio Salvador
Publisher The University of Arizona.
Rights Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Download date 10/10/2021 11:49:27
Link to Item http://hdl.handle.net/10150/195783 1
EVALUATION OF SUSTAINABLE AGRICULTURE SYSTEMS IN
CENTRAL MEXICO
by
Demetrio Salvador Fernández-Reynoso
______
A Dissertation Submitted to the Faculty of the
SCHOOL OF NATURAL RESOURCES
In Partial Fulfillment of the Requirements
For the Degree of
DOCTOR OF PHILOSOPHY
WITH A MAJOR IN WATERSHED MANAGEMENT
In the Graduate College
THE UNIVERSITY OF ARIZONA
2008 2 THE UNIVERSITY OF ARIZONA GRADUATE COLLEGE
As members of the Dissertation Committee, we certify that we have read the dissertation prepared by Demetrio Salvador Fernández-Reynoso entitled Evaluation of Sustainable
Agriculture Systems in Central Mexico and recommend that it be accepted as fulfilling the dissertation requirement for the Degree of Doctor of Philosophy
______Date: 03/07/2008 D. Phillip Guertin, PhD
______Date: 03/07/2008 Vicente L. Lopes, PhD
______Date: 03/07/2008 Richard H. Hawkins, PhD
______Date: 03/07/2008 George Zaimes, PhD
______Date: 03/07/2008 Craig Wissler, PhD
Final approval and acceptance of this dissertation is contingent upon the candidate’s submission of the final copies of the dissertation to the Graduate College.
I hereby certify that I have read this dissertation prepared under my direction and recommend that it be accepted as fulfilling the dissertation requirement.
______Date: 03/07/2008 Dissertation Director: Vicente L. Lopes, PhD
______Date: 03/07/2008 Dissertation Co-Director: D. Phillip Guertin, PhD 3
STATEMENT BY AUTHOR
This dissertation has been submitted in partial fulfillment of requirements for an advanced degree at the University of Arizona and is deposited in the University Library to be made available to borrowers under rules of the Library.
Brief quotations from this thesis are allowable without special permission, provided that accurate acknowledgement of source is made. Requests for permission for extended quotation from or reproduction of this manuscript in whole or part may be granted by the head of the major department of the Dean of the Graduate College when in his or her judgment the proposed use of the material is in the interests of scholarship. In all other instances, however, permission must be obtained from the author.
SIGNED: Demetrio Salvador Fernández-Reynoso 4
ACKNOWLEDGEMENTS
First I gratefully thank to my thesis director, Dr. Vicente Lopes, for his great insights, perspectives, patience and guidance throughout my doctoral work. He always encouraged me to grow as a modeler, researcher, and independent thinker. I appreciate his guidance to understand my research area better and to write up this dissertation.
I am also very grateful with my co-director Dr. Phillip Guertin for providing patient and helpful advice during busy semesters. He encouraged me to analyze and find a suitable solution of problems during this study. He is a great mentor that sets high standards for his students and guides them to meet their goals.
I sincerely thank my academic advisor, Dr. George Zaimes, for the generous time and commitment that he put into this dissertation. He carefully read through and commented on the several revisions of this document.
My sincere thank to Dr. Richard Hawkins for his valuable comments to improve this study. He was helpful and encouraging on guiding me to bring this work to a success.
I am grateful to Dr. Craig Wissler for serving on both the oral comprehensive and defense examination committee. His example, friendship, and advice taught me the way in academia. Thanks for his help, guidance, teaching, and having the patience to guide me with the paper work.
I would like to express thanks to the people of the School of Natural Resources for allowing me to use their facilities, and helping me find my way through my studies; especially to Cheryl Craddock, Andrew Honoman, and Mickey Reed. I would like also to thank my UofA colleagues Lenom Cajuste, Francisco Delgado, Soaring Hawk, Erick Sanchez, Mikhail Beznosov, Jesus Gastelum, and Edgar Uribe for their friendship and honest encourage.
I am thankful to the following Mexican Institutions: the Consejo Nacional de Ciencia y Tecnología (CONACYT) and Colegio de Postgraduados (CP) for the financial support during my doctoral program at the University of Arizona. I am also grateful to the people of the Secretaría de Agricultura, Ganadería, Desarrollo Rural, Pesca y Alimentación (SAGARPA); the Comisión Nacional del Agua (CNA); the Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias (INIFAP); and the Servicio Meteorológico Nacional (SMN), for the data accessibility used in this dissertation. 5
DEDICATION
I dedicate this work to my loving wife Virginia and our daughter Claudia both deserve my thanks and respect for their love, continued patience, and support during my studies in Arizona.
Also I dedicate this effort to my parents for their love, encourage, patience, and support.
Thanks my lord Jesus Christ. 6
TABLE OF CONTENTS
LIST OF TABLES...... 8
LIST OF FIGURES ...... 9
ABSTRACT ...... 11
I. INTRODUCTION...... 13 A. Problem Statement ...... 15 B. Research Objectives, Hypothesis and Assumptions ...... 16 1. General Objectives ...... 16 2. Specific Objectives...... 16 3. Hypothesis ...... 17 4. Assumptions ...... 17
II. LITERATURE REVIEW ...... 19 A. Soil Erosion and Crop Productivity ...... 20 B. Fertility and Soil Erosion ...... 21 C. Soil and Water Conservation Practices...... 23 1. Soil and Water Conservation in Central Mexico...... 24 2. Soil Conservation Practices...... 25 D. Soil Erosion and Crop Productivity ...... 30 E. Crop Productivity and Soil Erosion Modeling...... 31 F. Models for Crop Productivity and Soil Erosion...... 33 1. Empirical Models ...... 33 2. Process-Based Models...... 34 3. Physically-Based Models ...... 35 G. The EPIC Model ...... 40 1. Hydrological Component ...... 42 2. Weather Component...... 49 3. Erosion Component ...... 52 4. Crop Growth Component ...... 53 5. Crop Tillage Component ...... 58 6. Sensitivity Analysis ...... 59 H. GIS and Agricultural Environmental Modeling ...... 60 I. Conclusions from Literature Review...... 60
III. METHODOLOGY ...... 62 A. Site Description - Texcoco District...... 62 B. Design of the Study...... 64 7
TABLE OF CONTENTS — Continued 1. Database Sources...... 64 2. Study Development ...... 65 3. Calibration Process...... 66 4. Validation Process ...... 80 5. Crop Management Analysis ...... 102
IV. ANALYSIS OF RESULTS...... 112 A. Calibration...... 112 B. Model Validation...... 118 C. Crop Management Analysis...... 121 1. Current Management (CM) ...... 122 2. Recommended Management (RM) ...... 128 3. Sustainable Best Management Practices (BMPs)...... 132 D. Institutional Implications ...... 135
V. CONCLUSIONS AND RECOMMENDATIONS...... 140
APPENDIX A: PLOT CALIBRATION ...... 143 A1. Calibrated parcels evaluating grain productivity...... 144 A2. Calibrated plots evaluating soil erosion and runoff...... 154 A3. Soil parameters by layer for plot calibration...... 156
APPENDIX B: MODEL VALIDATION ...... 163 B1. Climatic data for the meteorological stations around the Texcoco District...... 164 B2. Average monthly wind velocity (WVL[1-12]) for the meteorological stations around the Texcoco District (m/s)...... 179 B3. Profile of Physical and Chemical Parameters by Soil Type...... 180 B4. Hydrological Response Units (HRU) used for Model Validation...... 183 B5. Hydrological balance, corn yield and soil erosion by HRU...... 190 B6. Land owners in the Texcoco district with soil erosion over 20 t/ha...... 198
APPENDIX C: BEST MANAGEMENT PRACTICES EVALUATION ..200 C1. BMP evaluation for conventional till (CT)...... 201 C2. BMP evaluation for minimum till (MT)...... 203 C3. BMP evaluation for non till (NT)...... 205
REFERENCES ...... 206 8
LIST OF TABLES
Table 1. Brief list of data needed to run the EPIC model by class...... 42 Table 2. Location and physical characteristics of the experimental plots...... 68 Table 3. Runoff curve numbers for hydrologic soil-cover complex for tillage, residue, and mechanical management (USDA-SCS, 1985)...... 73 Table 4. Crop management strategies for fertility, soil, and water conservation...... 75 Table 5. Meteorological observatories around Texcoco’s District with rainfall intensity data...... 76 Table 6. Monthly maximum rainfall in 30 min (WI) for period of record TP24 at Central Mexico...... 77 Table 7. Monthly average daily solar radiation (OBSL) at Central Mexico (Ortíz, 1987)...... 77 Table 8. Soil types for the agriculture area into the Texcoco district...... 90 Sandy clay loam...... 90 Table 9. Regression coefficients to evaluate the average monthly maximum (OBMX) and minimum (OBMN) air temperature in Texcoco District...... 97 Table 10. Meteorological stations used on model validation and soil conservation scenarios...... 99 Table 11. Corn’s current management (CM) for the Texcoco district...... 106 Table 12. Corn recommended management (RM) for Texcoco district...... 108 Table 13. Studied agronomic and mechanical practices for soil and water conservation...... 109 Table 14. Soils selected for BMPs analysis...... 110 Table 15. Corn’s current management (CM) applied to the selected HRU under conventional (CT), minimum (MT), and no (NT) till...... 111 Table 16. General categories for analysis regression interpretation...... 112 Table 17. The BMPs in terms of runoff and percolation ...... 133 9
LIST OF FIGURES
Figure 1. Mexico’s corn importations since 1977 (SAGARPA-SIAP, 2004b)...... 20 Figure 2. Main Mexican States producers of corn (80%), 1990-2003 (SAGARPA-SIAP, 2004a)...... 22 Figure 3. Texcoco’s district localization map, according to the national, state and the basin of Mexico boundaries (Source: INEGI digital databases scale 1:250,000)..... 63 Figure 4. Flow diagram for model calibration, validation, and analysis of crop management scenarios...... 67 Figure 5. Location of the experimental plots used for Epic’s calibration...... 70 Figure 6. Evaluated CN values for corn plots under different management strategies into the region. Data source: Zazueta (1984), Solano (1982), and Tapia (1999)...... 72 Figure 7. Meteorological observatories around the study area...... 78 Figure 8. Classification procedure, for corn detection pixels, using Landsat-ETM image (adapted from Tapia, 1999)...... 83 Figure 9. Map of land-use/cover for the Texcoco district...... 84 Figure 10. Map of FAO’s Soil Units for the Texcoco district...... 87 Figure 11. Map of FAO’s Soil Phases for the Texcoco district...... 88 Figure 12. Map of FAO’s soil types for the Texcoco district (see Table 8 for legend description)...... 89 Figure 13. Irrigated, rainfed, and terraced plots into Texcoco’s district...... 93 Figure 14. Topographic features of the Texcoco District and the hydrological network for the basin of Mexico. Source: INEGI’s (3” arc) digital elevation model...... 94 Figure 15. Meteorological stations around Texcoco’s district. Source: SMN...... 98 Figure 16. Daily Rainfall during 2002 for the meteorological stations used for model validation (Table 10)...... 103 Figure 17. Daily Rainfall during 2003 for the meteorological stations used for model validation (Table 10)...... 104 Figure 18. Hydrological Response Units (HRU) and meteorological stations (2002 and 2003) used to validate EPIC at district level...... 105 Figure 19. Observed and simulated corn yield in dry lands under Conventional Till (CT)...... 114 Figure 20. Observed and simulated corn yield for Conventional (CT) and Minimum Till (MT)...... 115 Figure 21. Observed and simulated annual runoff for corn plots under Conventional (CT), Minimum (MT), and No Till (NT)...... 116 Figure 22. Observed and simulated annual sediment yield for corn plots under Conventional (CT), Minimum (MT), and No Till (NT)...... 117 Figure 23. Observed and simulated (using the weather generator) historical corn yield by municipio...... 119 Figure 24. Observed and simulated corn yield for 2002 and 2003 by municipio...... 119 Figure 25. Municipio observed and simulated corn yield (the correlation line corresponds to 76 % of data observations, the red points)...... 121 10
LIST OF FIGURES — Continued
Figure 26. Mean corn yield under the Current Management by HRU...... 123 Figure 27. Mean soil erosion under the Current Management (CM) by HRU...... 124 Figure 28. HRU’s water balance for the current corn management (CM) (1st simulated year)...... 126 Figure 29. Change of soil erosion rate for CM in a hundred years...... 126 Figure 30. Relationship between soil erosion and corn productivity (under CM)...... 127 Figure 31. Relationship soil erosion/slope 1st year of simulation (under CM)...... 127 Figure 32. Relation between the differences of soil erosion and corn yield simulated in hundred years (under CM)...... 127 Figure 33. Effect of crop management on plant evaporation and corn yield (1st simulated year)...... 127 Figure 34. Crop’s management impact on runoff and corn yield (1st simulated year on the rainfed HRU)...... 129 Figure 35. Crop’s management impact on subsurface flow and corn yield (1st simulated year on the rainfed HRU)...... 129 Figure 36. Relationship between percolation and corn yield (1st simulated year of the current management)...... 129 Figure 37. Decrement of corn yield between the 1st and the 100th year of simulation. .. 129 Figure 38. Soil Erosion Increment between the 1st and the 100th year of simulation according to USLE’s C factor...... 131 Figure 39. Relationship between soil erosion increment and the USLE’s C factor...... 131 Figure 40. Hydrological and productive evaluation of the BMPs on main soils types (over 5% slope) from the Texcoco’s district...... 134 11
ABSTRACT
In Mexico, corn (Zea mays L.) is the most important crop (59% of its agriculture land) and the primary source of sediment yield. This study looks for alternatives to maintain corn productivity by means of sustainable soil and water conservation practices at central Mexico. In order to understand broad tendencies between soil erosion and crop productivity in the region, the EPIC (Erosion Productivity Impact Calculator) model was applied in the Texcoco’s district as follows:
1) Calibrate the model using 352 experimental corn plots established between
1972 and 1992 in 36 rural communities.
2) Validate the model on a spatial basis, using GIS tools, by means of historic
corn yields.
3) Identify the most vulnerable areas where corn productivity is being affected
by soil erosion.
4) Analyze the relationship between soil erosion and crop productivity, over a
100 years of simulation, comparing the Current Management (CM) and the
Recommended Management (RM) by governmental institutions.
5) Evaluate the most feasible soil and water conservation practices for the region.
From the calibration process, it was concluded that the EPIC model, under a wide range of environmental conditions, simulates very good corn yield (r2 between 0.88 and
0.90), annual runoff (r2=0.98), and annual sediment production (r2=0.96). 12
Base on the official environmental inputs available in the region, the EPIC model can assess only a moderately strong relationship (r2=0.58) between the official historical crop records and the simulated ones.
Comparison between CM and RM shows that the average crop yield in the region can be increased by 32.6% if RM were followed. Under the CM, the loss of soil fertility in the district reduces corn productivity by 3% over a hundred years. At least 50.0% of the region’s agricultural area needs soil conservation practices, mainly on areas with slopes over 5%. If it is decided to grow corn under conventional till in such areas it is recommended to construct bench terraces in order to maintain soil erosion below 20 t/ha/yr. Corn under no till, besides control erosion, can also increase grain productivity by at least 40% (0.6 t/ha) by combining contouring, mulching, and manures. 13
I. INTRODUCTION
Humans will always dependent on agriculture and the first step to prevent food scarcity is to preserve agricultural land from losses due to urbanization, development, soil erosion, chemical pollution, and salinization. The soil’s profile constantly losses soil particles as result of the natural erosive process. Even in the best managed soils, soil erosion rates can be greater than the soil’s formation rate. This tendency becomes more accentuated as soil tillage and the capacity to mix soil by mechanization increases
(Pimentel, 1993). In today’s agriculture, mechanization have allowed disturbing deeper soil particles and increasing its vulnerability to erosion. Thus, soils start its erosive process with no effect on crop yield until the soil becomes shallower than the crop’s root depth. Then further erosion can lead rapidly to desertification and drought vulnerability.
This means that beyond this threshold, in this case the root depth, the soil's productivity suddenly declines if soil erosion is not quickly controlled (Meadows et al., 2004). In most cases, soil degenerative processes have been offset or reduced through irrigation and extra fertilization, rather than through soil conservation technology adoption (Pimentel and Pimentel, 1996).
Therefore, the central task of sustainable agriculture is to preserve the agricultural lands from losses. Thus, with an increasing percentage of the land’s surface degraded, growing population, and droughts recurrence, it is very important to study and improve farming methods, through the use of soil and water conservation technologies such as terracing, contour plowing, composting, cover cropping, alley cropping, agroforestry, 14 polyculture, and crop rotation. Besides reducing soil degradation and improving soil’s water retention capacity, these technologies allow the same levels of productivity as conventional agriculture, with fewer fertilizers, machinery or groundwater use (Meadows et al., 2004; Parrott and Marsden, 2002; Rifkin and Howard, 1989).
The main goal of this study is to look for alternatives to maintain corn (Zea mays
L.) productivity through sustainable soil and water conservation practices in the agriculture district of Texcoco, Mexico. For this purpose, the EPIC (Erosion Productivity
Impact Calculator) model was used to simulate the relationship between soil erosion and corn yield in terms of crop management over a 100 years period. This study is an effort to continue the effort stated by Licona et al. (2006) to apply the model EPIC at district scale.
Another objective of this study is to analyze how crop productivity could increase in the region if the official recommended management (RM) replaces the current management
(CM). To achieve these objectives, the present study was divided in four sections: 1) identify, in the long run, the most vulnerable areas for soil erosion based on the CM, 2) analyze the relationship between crop productivity and soil erosion for the CM, 3) explore the relationship between corn productivity and soil erosion for the recommended management (RM), and 4) evaluate the most feasible soil and water conservation technologies, in terms of crop yield sustainability and regional food security.
The literature review describes some relevant studies related to soil erosion, soil fertility, soil conservation technologies and the sustainability of crop productivity. It shows how increasing amounts of fertilizers and irrigation has been used worldwide to counterbalance the effect of soil degradation. In addition, it describes the most common 15
crop productivity models and some issues associated with crop productivity and soil
erosion modeling. Finally, the main characteristics of the EPIC model are included with
some considerations about the calibration and verification processes.
A. Problem Statement
In Mexico, corn (maize) grows in 59% of its agriculture land, provides the
primary food base for human consumption, and is an important grain source for cattle
feed. Besides being the most important crop in the country, corn farming is the principal
source of soil erosion (Landa et al., 1997). Usually, the previously productive lands, after
decades of corn cultivation, have been eroded or became more vulnerable to droughts
(Simon, 1997). Simon also claims that declines on corn yield in Mexico have been
roughly balanced by increasing corn imports, use of fertilizers, and a constant expansion
of farming land. From on analysis in Central Mexico through gauging (Figueroa, 1975)
and use of hydrological models, such as SWRRB1 (Fernández et al., 1999), WEPP2
(Tapia, 1999) and SWAT3 (Torres et al., 2004), it has been concluded that corn is the
most common land use and its practice the primary source of sediment yield in the
region’s watersheds.
1 SWRRB: Simulator for Water Resources in Rural Basins. 2 WEPP: Water Erosion Prediction Project. 3 SWAT: Soil and Water Assessment Tool. 16
B. Research Objectives, Hypothesis and Assumptions
1. General Objectives
The main goals of this dissertation are the following:
x Simulate corn agricultural systems in Central Mexico using the EPIC model and
GIS procedures.
x Analyze the effect of corn management and soil and water conservation practices
on soil degradation, and ways to improve regional food security and diminishing
farmer's operational costs.
2. Specific Objectives
This document has the next specific goals:
x Calibrate and validate EPIC’s plant, management, and environmental parameters
on a spatial and temporal basis.
x For the corn’s CM, based on average climatic conditions, identify the most
susceptible areas and their corresponding soil types where corn productivity is
being affected by soil erosion.
x Identify through a 100 yr simulation, for irrigated and rainfed areas, the
relationship between crop productivity and soil erosion for CM and RM.
x Evaluate the most feasible –from calibrated plots— soil and water conservation
technologies, in terms of crop yield sustainability. 17
3. Hypothesis
For this study were assumed the following hypothesis:
x The application of the EPIC model in Mexico will generate satisfactory results on
a regional basis, using available geographic information from public institutions.
x The EPIC model can be satisfactorily validated on a municipio4 basis through
mean parameters and observed crop yields between their boundaries.
x Soil and water conservation practices can reduce soil degradation and maintain
crop productivity.
4. Assumptions
In order to achieve the general and specific objectives were considered the following
assumptions:
x The data from experimental plots, established in the Texcoco district, are
appropriate to calibrate EPIC on a regional basis.
x The soil and climatic spatial information, available in Texcoco’s district, are
appropriate to validate the EPIC model on a spatial basis.
x The mean corn yields reported by local agricultural institutions, on a municipio
basis, are appropriate to validate the EPIC model on a spatial basis.
x Continuous Hydrological Response Units (HRU), based on soil classification,
produce similar crop yields and soil degradation.
4 In U.S. municipality is generally associated with an urban area. However, in Mexico, a Municipio is an administrative entity composed of a clearly defined territory; like the U.S. County (Condado in Spanish). Where, a Municipio is the third-level administrative division, a Distrito (District) is the second-level of political division, and the first-level political-administrative division is the Estado, or State. 18 x Due to the high concentration of meteorological stations in the district and the
small size of each HRU (compared to the district area), it is assumed that the
average weather and climatic condition over a HRU is similar to the records of the
closest station. x For the purposes of this study, the CM’s dates and crop inputs are considered as
representative of the mean management conditions followed by corn producers. 19
II. LITERATURE REVIEW
Corn is the most important crop in Mexico; it is grown on 59% of its agriculture
land and it contributes 9.2% of the human daily requirements of energy and 14% of the
protein (SAGARPA-SIAP, 2004b)5. FIRA6 (1998) reports that the corn national average
yield, for Mexico, has increased from 1.8 t/ha in 1980 to 2.4 t/ha in 2000. FIRA also
claims that this increase in the corn’s national yield since 1980 is explained by 71.1%
productivity improvements while the land expansion only justifies 21.6% of such
increase. On the other hand, nearly a third of the Mexico’s 20 million hectares of
farmland have been severely eroded while 86% is suffering the consequences of erosion
in some degree (Becerra, 1998).
In Mexico, grains are the main agriculture product and corn constitutes roughly
50% of grain imports (FIRA, 1998). Although the country is self-sufficient in corn
production for human consumption (white grain), there is a deficit of corn for animal feed
(yellow grain). It is estimated that humans consume annually about 10 million metric
tons7 and animal feeding 7.9 million tons. Today Mexico imports around a third of its
corn consumption (Figure 1) and 60% of its fertilizers (Vega and Ramírez, 2004;
Zahniser and Coyle, 2004).
5 SIAP, Spanish acronym for the Statistical Information Service that belongs to SAGARPA (Mexican Ministry of Agriculture, Ranching, Rural Development, Fisheries and Feeding) 6 Spanish acronym for the Agriculture-Related Established Trusts (FIRA) of the Central Bank of Mexico. www.fira.gob.mx 7 In this work, the units tons (t) will be referred as metric tons. 20
7.0
6.0
5.0
4.0
3.0
2.0
1.0 Importations (|Million of metric tons) 0.0 1975 1980 1985 1990 1995 2000 2005 Year Figure 1. Mexico’s corn importations since 1977 (SAGARPA-SIAP, 2004b).
A. Soil Erosion and Crop Productivity
Soils require between 200 and 1000 years to form 2.5 cm of topsoil under cropland conditions (Morgan, 2005), but the energy transmitted from rainfall, runoff, and wind can easily erode this slowly formed soil. The dominant form of soil erosion is when raindrops hit exposed soil with an explosive effect, launching soil particles into the air and these particles are transported by runoff (Pimentel et al., 1995). Thus, raindrop splash and sheet erosion are the dominant forms of soil erosion and material removed by soil erosion is commonly up to fivefold richer in organic matter (OM) than the soil left behind
(Pimentel and Krummel, 1987).
Soil erosion reduces soil productivity mainly through the loss of the available water-holding capacity, by restricting soil depth, removing OM and finer soil particles, and increasing soil bulk density (Kong et al., 2002; Pimentel and Krummel, 1987). When soil erosion occurs, the amount of surface runoff increases, less water enters the soil matrix and less moisture is available for crops. For example, in Zimbabwe a runoff rate 21 between 20 to 30% of total rainfall can result in important water scarcity for crops
(Pimentel et al., 1995).
Morgan (2005) affirms that corn is a soil-depleting crop because in Zimbabwe,
Malaysia, and India, under conventional tillage, on 4.5-11% slopes and clean weeding, the annual erosion results in soil losses between 10 and 120 t/ha. Pimentel et al. (1995) reports that corn yields on some severely eroded soils have been reduced between 12-
21% in Kentucky, 0-24% in Illinois and Indiana, 25-65% in the southern Piedmont
(Georgia), and 21% in Michigan. These authors also state that in several areas of the
Philippines, soil erosion has caused declines in corn productivity as severe as 80% over the last 15 years. Pimentel (1989) reports that corn yields decrease less than 1% for each centimeter of soil loss.
Central Mexico grows 58% of the national corn production (Figure 2), which is cultivated during the spring-summer period, i.e., during the rainfall season. This implies that during the stages of crop growth, corn canopy does not protect the soil’s top layer from raindrop kinetic energy because of the high percentage of bare ground (Landa et al.,
1997).
B. Fertility and Soil Erosion
It is impossible to produce the same amount of grain on the same parcel in perpetuity. Even with recycling crop residues, as a crop is harvested, soil fertility is depleted and it will need to be fertilized to maintain its productivity (Rifkin and Howard, 22
1989). This loss of productivity, which is exacerbated by soil erosion, can be offset by fertilization and irrigation.
3.0
2.5
2.0
1.5 Central Mexico
1.0
0.5 Average surface (million of hectares) of (million surface Average
0.0 Puebla S-S Jalisco S-S Mexico S-S Oaxaca S-S Hidalgo S-S Sonora F-W Sonora Sinaloa F-W Guerrero S-S S-S Veracruz Chiapas S-S ChiapasF-W Veracruz F-W Veracruz Michoacan S-S Michoacan Guanajuato S-S Guanajuato TamaulipasF-W Producers of maize during Spring-Summer (S-S) and Fall-Winter (F-W) seasons Figure 2. Main Mexican States producers of corn (80%), 1990- 2003 (SAGARPA-SIAP, 2004a).
Pimentel (1989) found between 15 to 30% reductions in crop yields result from moderate to severe erosion. Besides the soil erosion problem, Lu and Stocking (2000) found that the decline in soil productivity is also caused by an insufficient return of OM.
These authors claim that an enhancement in OM inputs compensates the damage of soil erosion; because it reduces soil’s detachment and increases infiltration and rainfall retention. However, Lu and Stocking (2000) state that recovering degraded soils by increasing OM, without soil erosion control practices, relieves temporarily the symptoms of soil degradation and might be unprofitable in the long term. Due to the increasing use of fertilizers and irrigation to offset soil degradation and drought problems, Pimentel et al.
(1989) consider that soil and water conservation practices are the best investment in the long term to reduce the mounting cost associated with fertilization and irrigation. 23
C. Soil and Water Conservation Practices
The goal of soil conservation is to achieve the maximum sustained level of crop yield while maintaining soil loss below its natural rate of formation or under a rate of tolerance. An annual tolerance rate of 11.2 t/ha is generally accepted for the Universal
Soil Loss Equation (USLE) as appropriated but values as low as 2.2 t/ha are recommended for particularly sensitive areas of thin or highly erodible soils (Hudson,
1995; Renard et al., 1997). Usually to reduce raindrop impact, increase infiltration, reduce runoff volume, and decrease water velocities; soil and water conservation regulations always prefer agronomic instead of mechanical measures (engineering works) to reduce costs (Morgan, 2005).
Among the agronomic practices for soil and water conservation, that favor the abundance of soil biota, are those implemented to maintain the soil OM content at optimum levels. Practices like straw-mulching that may augment biota threefold, and the application of OM or manure may increase earthworm and microorganism biomass almost five times (Pimentel et al., 1995). Reicosky and Forcella (1998) point out that crop management residues and soil OM are essential measures to maintain soil productivity on circumstances of drought or during the El Niño events (Badan, 2003).
Leaving crop residue on the land after harvest and delaying plowing until the following crop season are alternative agronomic practices to control soil erosion because they minimize the period of bare soil (Morgan, 2005). 24
1. Soil and Water Conservation in Central Mexico
Soil and water conservation practices have been known in Central Mexico for centuries. In the Basin of Mexico, during the Aztecs times, soil erosion and deforestation were problems since the Teotihuacan collapse (Bower, 1993; Denevan, 1992; McAuliffe et al., 2001). With almost two millions inhabitants in this basin, pre-Columbian farmers harvested rainfall, developed along the lakeshore small irrigation systems and built terraced fields in the surrounding mountain slopes (Sanders et al., 1979). In addition, they created farmland artificially by dredging mud from the shallow lake bottoms of Texcoco and Xochimilco to construct highly productive artificial fields fertilized by compost, called chinampas or “floating gardens” (Hassig, 1993). The recurrent droughts also forced the inhabitants to terrace the hillsides and built small dams along the creek beds to limit flooding and recapture the topsoil washed away by the rains. They also cleared small plots to plant corn but leaving stands of trees in place to stabilize the hillsides
(Bower, 1993; Sanders et al., 1979).
In Central Mexico, corn varieties have been adapted to the soil and climatic conditions; that changes significantly because of the abrupt regional topography variations. The traditional strategy of growing corn, beans, and squash in the same field relies on highly refined environmental adaptation. While corn plant depletes nitrogen from the soil, beans replace it. Certainly, this traditional strategy, used in several areas of the country, has maintained productivity after thousands of years of intensive cultivation, which is a compelling evidence of the sustainability of traditional Mesoamerican agricultural systems (FIRA, 1998; Simon, 1997). 25
2. Soil Conservation Practices
Following are described the main conservation practices that potentially can be implemented into the region in order to reduce soil erosion, increase soil water-holding capacity, and improve soil fertility. a) Conventional Tillage
This practice involves plowing, secondary cultivation, and planting. Plow pulling applies an upward force to cut, loosen, invert and mix the soil between 0.1 and 0.2 m depth and produces a surface roughness of 12.0 - 16.0 cm. Secondary cultivation shapes the seed bed and removes weeds. Using the chisel bar the surface roughness is reduced between 3.0 and 4.0 cm (Morgan, 2005). b) Clover (trifolium spp)
Undersowing clover in corn offers sufficient protection of soil with no reduction in yield mainly when it provides a 70% ground cover at the times of erosion risk
(Morgan, 2005). Compared with corn’s conventional cultivation, Goeck and Geisler
(1989) found that clover (trifolium repens) reduced the soil loss from 3.4 to 0.4 t/ha. El-
Swaify et al. (1988) also found that the soil loss was reduced from 4.0 t/ha to 2.0 t/ha when corn was intercropped with rose clover (trifolium ortum). c) Strip-cropping
Strip-cropping alternates a row and cover crop and this practice has proved to be an effective way to provide resistance to soil particle detachment and transport by wind 26 and water. Singh et al. (1979) found that corn along with soybean on a 3.5% slope field, produced an annual soil loss of 9.5 t/ha compared with 15.7 t/ha for corn without this practice. d) Plant Density
According to Mohammed and Gumbs (1982) an increase from 41,500 to 62,000 plants per hectare, reduced the soil loss under corn from 32 to 21 t/ha. Even a planting density around 80,000 plants/ha has been recommended for Central Mexico. However,
Castelan et al. (2000) found that farmers in Mexico plant between 45,000 and 55,000 plants/ha and it is quite probable that model predictions for major planting rates do not apply to local conditions because they consider that higher planting rates affects grain yield negatively.
An increment of plant density on corn from 25,000 to 37,000 plants per hectare, but using a trash mulch at the higher density, reduced the annual soil loss from 12.3 to 0.7 t/ha and increased the crop yield from 5 t/ha to 10 t/ha, respectively (Hudson, 1995). The annual soil loss under corn descended from 22.6 to 13.9 t/ha when Bhardwaj et al, (1985) increased the row spacing from 45 to 90 cm and reduced the plant spacing from 40 to 20 cm. e) Mulching
This practice is a useful alternative in places where scarce rain avoids the establishment of a ground cover before the arrival of heavy rains. However, it must be considered, that as mulch decomposes, it competes for nitrogen with the main crop. 27
Mokhtaruddin and Maene, (1979) found that 3.0 t/ha of mulch on corn reduced soil loss from 7.5 t/ha to 0.5 t/ha. A grass mulch at 4.0 t/ha reduced annual soil loss under corn from 22.4 t/ha to 5.0 t/ha (Khybri, 1989). f) Organic Matter
Organic Matter (OM) enhances water infiltration, water retention capacity, soil fertility, porosity, cohesiveness, stability and stable aggregate structure (Reicosky and
Forcella, 1998). Evans (1980) argues that soil with less than 2 % organic carbon, equivalent to about 3.5 % organic content, can be considered erodible. The increase of
OM always requires large supply of organic material. For example (Jones, 1971), plowing the corn residue between 5 to 10 t/ha increased soil’s organic carbon content in
0.004 to 0.017 per cent respectively. Even applications of farmyard manure at 10 t/ha only maintain the existing level of organic content (Morgan, 2005). Pimentel and
Krummel (1987) state that a reduction in soil organic matter from 3% to 1.8% decreases corn yield by about 25%. Organic material may also be added as manure, composting, green manures, or crop residues as described below.
In the Texcoco District the farmers prefer manure over chemical fertilizer.
Chemical fertilizers are not broadly accepted because their effect lasts only a year; they require water, without which plants will “burn” or wither; and they believe chemical fertilizers damages their lands. Even though a minority of the farmers use manure, it is valued not only as a fertilizer, but also as mean to improve moisture retention, to soft clay soils and to recover saline and eroded soils on loamy sand hardpan known as tepetates
(Ortiz and Gutiérrez, 2001). 28 g) Manure and Composting
Manure and composting of organic matter allows to immobilize soil nitrogen, and compared with commercial fertilizer, livestock’s composting manure has five to seven times more nutrients (Pimentel et al., 1989). Bonsu (1985) found that a combination of cow dung (5 t/ha) with wheat or corn straw mulch (4 t/ha) enhanced the soils water- holding capacity. This combination also gave the lowest soil loss and the highest sorghum grain yields compared with a range of other practices on experimental plots in a humid tropical region. One concern is that composting may result in large nitrogen losses by ammonia volatilization, when the microorganisms are degrading the organic matter. h) Green Manures
These manures are normally made by legumes that cover the topsoil against soil erosion, when plowed, they can improve the soil stability and the return of nutrients stored in their foliage. Legumes, when used as cover crops, are also useful to reduce weed problems (Morgan, 2005; Pimentel et al., 1989). i) Crop Residues
Crop residues improve soil physical properties like soil water content, temperature regimes, aeration, and aggregation (Reicosky and Forcella, 1998). Duley and
Russe (1943) found that incorporating crop residue, on a bare soil, reduced corn annual soil loss from 35.7 t/ha to 9.9 t/ha. 29 j) No Tillage
Under corn, no tillage reduces soil erosion to levels similar to multiple cropping but generally not lower than surface mulching. The low percentage of crop residues on the surface during the first year generally makes this practice less effective. Besides, it is not recommended on compacted soils with easy surface sealing because the infiltration diminishes, the runoff increases, and the crops yield decreases (Morgan, 2005). Osuji et al.(1980) report that this practice reduced annual soil loss from 5.6 t/ha to 0.07 t/ha under corn cropping conditions. k) Minimum Tillage or Reduced Tillage
In this practice chiselling or discing are used to prepare soil and retaining between
15 and 25 % of residue cover. The residues retains soil moisture and the tillage in heavy soils reduces the cracking to promote infiltration and reduce runoff (Morgan, 2005). l) Contour Cultivation
Contour farming reduces soil loss in sloping lands when rows follow the topographic contour compared with cultivation up-and-down the slope. The effectiveness of contour farming varies with the length and steepness of the field. This practice is only effective in places with low rainfall intensities (Hudson, 1995). In the study area, this mechanical practice is the most widely used, although very often fails when runoff exceeds rows’ store capacity. 30 m) Terraces
This mechanical measure consists of earth embankments constructed across the slope to shorten slope length, to intercept surface runoff, and reduce water velocity into a non-erosive flow (Morgan, 2005). Bench terraces have been broadly implemented in the upper areas of the district (Figure 13 on page 93) by the Texcoco Lake Management
Office (Comisión del Lago de Texcoco) and they have shown to be profitable after 12 years of corn harvest (Adame et al., 2000).
D. Soil Erosion and Crop Productivity
Pimentel et al. (1995) states that the effects of soil erosion and pest damage have been covered up or compensated by the extensive use of irrigation, hybrid seeds and growing rates of fertilizers and pesticides. Pimentel and Krummel (1987) point out that erosion reduces soil productivity mainly through the loss of water-holding capacity.
Therefore, when soil erosion occurs, the amount of water runoff increases, less water enters the soil matrix, and less moisture is available for the crops. For example a runoff rate between 20 and 30% of total rainfall can result in important water scarcity for crops
(Pimentel et al., 1995). If the average relationship runoff/rainfall in the Texcoco district is
17.1% (5.5% SD), it implies that there are areas in the district with problems of water scarcity. As soils become impoverished, plant growth becomes increasingly vulnerable to droughts. In this sense, Florescano (1980) in his historical analysis of droughts in Mexico, points out that crop productivity in poor soil has been more susceptible to droughts, frost, flood and pest attacks. 31
Pimentel and Pimentel (1996) consider that if fertilizers, irrigation, and pesticides were withdrawn, corn yields would drop from 130 bushels8 per acre (8.2 t/ha) to about 30 bushels (1.9 t/ha) assuming legume plowing. Without the use of legumes, yields would decline to about 16 bushels per acre (1.0 t/ha), which is about the corn yield in mountainous areas of Central Mexico (Pulido and Bocco, 2003) and into the district.
E. Crop Productivity and Soil Erosion Modeling
A numerical model is an attempt to represent our understanding of the dynamics of a biophysical system and it may help us to improve our conceptual understanding about how the real world works. Models are created for some specific purpose, for answering a specific set of questions, and make predictions of likely scenarios. In using models to make predictions, one should always keep in mind the model’s limitations and the kind of questions asked (Meadows et al., 2004).
In food production it is crucial to assess how and what factors are involved in crop productivity. It has been observed that soil’s productivity is influenced by soil erosion, slope, soil composition, extent of vegetative cover, soil depth, organic matter, available water-holding capacity, soil biota, and nutrient levels. However, these factors form a complex and interdependent system. Subsequently, a change in one factor will affect all or many other factors (Jones et al., 1991; Pimentel et al., 1995).
Models are simplified representations of reality through mathematical equations that describe plant behavior from known process and recognized interactions. It is
8 1 bushel of dry corn = 56 lb = 25.401 kg # 72,800 grains. 32 generally recognized that a good model should satisfy the requirements of conceptual reliability, universal applicability, minimum data, physical based parameters for the processes included, ability to take into account environmental and management changes and easy use (Morgan, 2005). Nevertheless, the description of a plant growth model is complex, since crop yield is affected by many environmental factors (weather, soil properties, crop management, plagues, diseases, and soil erosion) and their interactions.
Dent and Thornton (1988) observed that models are useful tools to design and test technologies and practices, under wide range of conditions that, in other ways, would be expensive and time-demanding. Therefore, the main goal of crop models is to provide an alternative method to quickly evaluate strategies and to assess crop productivity, most of them based on annual cycle under a wide range of soil, climatic, and management conditions. Crop models are also useful to evaluate the best dates and amounts of irrigation, fertilization, and planting (Whisler et al., 1986).
Assessing how and to what extent soil erosion decreases crop productivity, is crucial to evaluate the multiple factors that influence soil erosion rates, as well the soil components that affect crop productivity (Whisler et al., 1986). Thus, to understand the relationship between soil erosion and productivity, many empirical models have been developed incorporating environmental and physiological factors. In this sense,
Rosenberg et al. (1992) indicate that a simulation model must be able to represent a crop’s real behavior in a particular region before it can be applied to predict impacts of plausible environmental changes. 33
F. Models for Crop Productivity and Soil Erosion
Models optimize management information from experiments established in
different regions and periods. It is possible to use models to analyze a region in order to
make better management decisions, develop better planning strategies, predict crop yields,
and identify research needs (Baird, 1999; Moen et al., 1994). For the purpose of modeling
changes on crop productivity related to soil degradation, researchers have developed
models such as the PI model9 (Pierce et al., 1983), the Soil Life model (Elwell and
Stocking, 1984), CREAMS10 (Knisel, 1980), ANSWERS11 (Beasley et al., 1980),
AGNPS12 (Young et al., 1989), GUESS13 (Rose et al., 1983), WEPP14 (Becker, 2003),
EUROSEM15 (Morgan et al., 1998), CERES16 (Jones and Kiniry, 1986), DSSAT17 (Jones
et al., 1998), and EPIC18 (Jones et al., 1991).
1. Empirical Models
Empirical models, like the PI or Soil Life, are based on erosion-productivity
relationships that are statistical in nature. However, these yearly models cannot be
universally applied because they need local calibration19 and validation20 of soil limiting
9 Productivity Index. 10 Chemicals, Runoff and Erosion from Agricultural Management Systems. 11 Areal Nonpoint Source Watershed Environment Response Simulation. 12 Agricultural Nonpoint Source Model. 13 Griffith University Erosion Sedimentation System. 14 Water Erosion Prediction Project. 15 European Soil Erosion Model. 16 Crop Estimation through Resources and Environmental Synthesis. 17 Decision Support System for Agrotechnology Transfer. 18 Erosion/Productivity Impact Calculator. 19 Calibration is the estimation and adjustment of model parameters and constants to improve the agreement between model output and a data set. 20 Vaidation is is defined as the process of determining the degree to which a model within its domain of applicability possesses a satisfactory range of accuracy consistent with the intended representation of the 34
factors. In particular, the Soil Life model only assesses soil’s productive life based on
likely minimum levels of production (Stocking and Lu, 2000).
2. Process-Based Models
Process-based models rely on empirical equations that represent the essential
mechanisms controlling erosion, including spatial and temporal variabilities, and can
identify which parts of the system have the most effect on erosion and runoff processes
(Morgan, 2005). For example, models like CERES, DSSAT, and EPIC that evaluate
runoff, flow rate, and soil erosion, use empirical assumptions like the curve number (CN)
(see page 42), rational equation method (see page 43), and the USLE (see page 52),
respectively. Their relatively simple empirical assumptions, make these models the most
common tools to predict crop productivity. Similar in the crop growth component, they
shared four basic crop characteristics: (1) phenology, expressed through heat units index
(HUI); (2) the solar interception of energy, that depends on leaf area index (LAI) and
day-length reduction factor (FHR); (3) accumulation of biomass, as function of CO2 maximum assimilation and the efficiency in the use of the intercepted photosynthetic active radiation (PAR); and (4) the biomass distribution among different organs, mainly those of economical interest, known as harvest index (HI) (Favis-Mortlock and Savabi,
1996; Moen et al., 1994; Whisler et al., 1986).
The daily-based CERES-Maize model simulates yield and growth from planting to maturity. It includes the effects of cultivar, planting date, planting density, nitrogen (N)
real world. Validation compares simulated system output with real system observations using data not used in model development or calibration. Some times confused with verification, however, verification is often defined as ensuring that the modeling formalism and its implementation is correct (Rykiel, 1996). 35 fertilizer application, and irrigation. CERES does not simulate the phosphorous and potassium cycles in the soil or plant, nor the effects of weeds, pests and wind damage
(Castelan et al., 2000). Although CERES is more specific to evaluate corn productivity; it does not include soil erosion impact on productivity (Jones and Kiniry, 1986).
3. Physically-Based Models
In order to have a more universal application, several models have been developed to with a better physical basis; like the laws of conservation of mass and energy. Although they may still rely on empirical equations to describe the erosion processes. Among the models to describe the erosive process incorporating the laws of conservation of mass (using mass balance continuity equations) are: CREAMS, GUESS,
ASWERS, and AGNPS. Models, like WEPP or EUROSEM, besides the law of conservation of mass, also incorporate laws of conservation of energy to evaluate inputs upslope and outputs of soil particles downslope.
The CREAMS model is a daily basis, field-scale model, developed to assess non- point source pollution and to analyze the environmental impact of different agricultural practices (Knisel, 1980). The model consists of three components: hydrology, erosion and chemistry (plant nutrients and pesticides in the runoff, sediment and percolated water).
The erosion component applies the continuity equation for sediment transport downslope by particle-size. The erosion component is in the form:
wQs D D (1) wx i r 36
where Qs is the sediment load per unit width per unit time, x is the distance downslope,
Di is the delivery rate of particles detached by interrill erosion to rill flow and Dr is the rate of detachment or deposition by rill flow. The interaction between rill detachment and sediment load is expressed by:
Dr Qs 1 (2) Dc Tc where Dc is the detachment capacity, Tc is the transport capacities capacity of the rill flow. This equation implies that if a flow is carrying less material than it has the capacity to transport, it will detach more particles to fill this deficit. On the other hand, if the sediment load is greater than the transport capacity, deposition will occur. The overland flow element operates by estimating detachment by interrill and rill flow and comparing the quantity of detached with the sediment transport capacity of the rill to determine the rates of erosion and deposition. This element depends on rainfall energy, hillslope angle, peak runoff, runoff volume and the USLE’s K (soil erodibility), LS (slope length and steepness), C (crop management) and P (control practice) factors.
The GUESS model simulates the erosive processes and deposition along a hillside.
The model separates the surface soil into two parts: detachment from the topsoil and the
sediment particles without cohesion. The model describes the soil in terms of 50 particle-
size classes (Rose et al., 1983). The continuity equation in GUESS takes the form:
§ wQsi · w(Ci h) ¨ ¸ ei edi ri rdi di (3) © wx ¹ wt where Qsi is the sediment load of sediment class i, Ci is the concentration of sediment in the flow, ei is the rate of detachment of particles of sediment in the original soil by 37
raindrop impact (depends on detachability of the soil, rainfall intensity, particle size ), edi is the rate at which recently detached soil of sediment is re-detached by raindrop impact, ri is the rate of detachment of particles of sediment by flow, rdi is the rate at which recently detached soil of sediment is re-detached by the flow (it is a function of depth of flow and sediment density), and di is the rate of deposition (depends on fall velocity of
particles of sediment). The detachment rate of soil particles by flow is modeled as a
function of stream power; that is defined as the product of flow shear stress and flow
velocity over a critical value.
The ANSWERS model was designed to simulate the hydrological and erosive
process on small agricultural basins during and after a rainfall event. It uses continuity
equations in consecutive downslope segments for runoff generation, flow of runoff over
the land, detachment and transport of sediment. The sediment is routed over the land
surface and the pattern of erosion evaluated through a complete soil profile (Beasley et al.,
1980).
The AGNPS, a storm basis model, uses a mass balance approach for the processes
of runoff generation, flow of runoff over the land and the detachment and transport of
sediment. But it also uses the USLE’s K, LS, C and P factors to account for soil, slope,
land cover and land management (Young et al., 1989).
The WEPP, a daily basis model, was designed to assess soil erosion for soil and
water conservation planning. This model simulates daily climate conditions to drive the
hydrology, erosion, and plant growth components. The model operates on a continuous 38 basis21 but can run for a single storm (event-based) (Becker, 2003). The model considers ponding conditions, by a version of the Green and Ampt equation, to asses infiltration and overland flow hydrology (Hawkins et al., 1991). The erosion model uses the continuity equation for sediment transport downslope as follows:
wQs / wx Di D f (1) where Qs is the sediment load per unit width per unit time, x is the distance downslope,
Di is the delivery rate of detachment of soil particles by interrill erosion to rill flow and
Df is the rate of detachment or deposition by rill flow. The model assumes that all the material detached on the interrill areas is delivered to the rills. The delivery rate between interrill erosion and rill flow depends on soil erodibility, rainfall intensity, plant canopy and rills spacing. The rill detachment is a function of the sediment load in the flow, the sediment load at transport capacity, soil rill erodibility, flow shear stress acting on the soil and the critical flow shear stress for detachment to occur. Deposition in the rills occurs when the sediment load is greater than the transport capacity.
EUROSEM is an event-based model designed to evaluate the sediment transport, erosion and deposition over the land surface throughout a storm. It can be applied to either individual fields or small catchments. This model simulates the transport of water and sediment from interrill areas to rills. This model uses the influence of leaf drainage to simulate the effect of vegetation or crop cover. In EUROSEM soil conservation measures can be chosen taking into account soil properties, microtopography, plant cover parameters and the conditions associated with each practice. EUROSEM does not include
21 A model on a continuous basis use, for each precipitation event, inputs of rainfall under finite intervals of time. 39
sediment particle size by classes (Morgan et al., 1998). The continuity equation in this
model is expressed as:
§ w(AC) · w(QC) ¨ ¸ e(x,t) qs (x,t) (4) © wt ¹ wx where A is the cross-sectional area of the flow, C is the sediment concentration in the flow, t is time, x is the horizontal distance downslope, e is the net rate of pickup of sediment and qs is the rate of input or extraction of sediment per unit length of flow from
land external to the segment. On a flat slope segment, qs = 0. The net pick-up rate or erosion of sediment on the slope segment is defined as:
e Di D f (5)
where Di is the rate of detachment by raindrop impact, Df is the rate of detachment of soil
particles by flow. The raindrop detachment is function of soil detachability resistance,
depth of surface water, rainfall intensity and plant canopy. The rate of detachment of soil
particles by flow (Df) is modeled as a balance between detachment and deposition. The
flow detachment depends on soil cohesion and rate flow. When the sediment
concentration in the flow exceeds transport capacity, e becomes negative, and deposition occurs.
For the present study, the EPIC model was chosen to explore under a wide range of soils, climate, and crop management conditions; the effects of management practices on corn yield and soil degradation. Although physical-based models, like WEPP, include concepts of conservation of mass for infiltration, surface runoff, and erosion process; the results of the models are not necessarily better than the empirical assumptions of EPIC – 40
like the Curve Number (CN), Rational formula (qp) or USLE (Bhuyan et al., 2002). Even though EPIC is well known for its extensive structure and strong data demand, the input parameters are easier to obtain from the available data in the region compared to WEPP or CREAMS. Furthermore, there were not enough data available to calibrate or validate those models on the study area, such as lack of information on rainfall distribution
(spatial and temporal) by event, sediment yield by particle size, microtopography, or leaf drainage. Moreover, the plant growth and water-balance sub-models in WEPP, CREAMS,
CERES, and DSSAT are very similar to those used in EPIC.
G. The EPIC Model
EPIC is a process-based model that predicts, on a daily basis, crop yield and the long-term relationship between the accumulated soil erosion (water and wind) and crop productivity (Jones et al., 1991; Williams et al., 1989). The model, developed by the
USDA/ARS Grassland Soil and Water Research Laboratory, simulates biophysical processes on agricultural system such as plant growth (nutrient cycling, atmospheric CO2, soil temperature, tillage, and crop management), weather simulation, and hydrology
(runoff, water erosion, sedimentation, and wind erosion). The interactions of these processes are calculated at the scale of a single field on a daily basis (Izaurralde et al.,
1999).
The EPIC model was designed as a rural development tool for crop technology transfer and useful to simulate cropping productivity systems and soil degradation, under a wide rank of climatic, edaphic and management conditions (Williams et al., 1989). It is 41 a field size area model and recommended to be used in areas up to 100 hectares. The model assumes that weather, soils, and management systems are homogeneous. It has the option for long-term simulations (100 years) using the weather generator (Singh, 1995).
In EPIC, yields are expressed as a fraction of biomass, which in turn is a function of photosynthetically active radiation and leaf area. Leaf area is simulated as a function of heat unit accumulation (HUI), crop development stage and crop stress (Sharpley and
Williams, 1990a).
The EPIC model requires data on soil physical properties (e.g., bulk density, water-holding capacity, wilting point, etc.) and crop management (e.g., cultivars, fertilization, tillage, planting, harvesting, irrigation) as inputs. In addition, the weather variables necessary for running the EPIC model are daily values of precipitation, minimum/maximum air temperature, solar radiation, wind speed, and relative humidity as well as monthly statistics such as the standard deviation of maximum and minimum temperatures and number of days with precipitation (Easterling et al., 2003; Izaurralde et al., 2003). The sub-models of EPIC are described in the following sections, based on
Sharpley and Williams (1990a). Table 1 shows in more detail the information needed to run the EPIC model. 42
Table 1. Brief list of data needed to run the EPIC model by class. Class Description Daily values of maximum (OBMN) and minimum (OBMX) air Weather temperatures, rainfall (RMO) and monthly solar radiation (OBSL). Topography Latitude (YLT), elevation (ELEV), and slope (S) Soil profile, and for each layer: layer depth (Z), bulk density (BD), Soil physical wilting point (U), field capacity (FC), sand (SAN) and silt (SIL) properties content, pH (PH), sum of bases (SMB), calcium carbonate (CAC), (Up to ten layers) cation exchange capacity (CEC), course fragment content (ROK), and saturated conductivity (SC). Soil fertility and soil water variables for each soil layer: Nitrate Soil fertility concentration (WNO3), Organic N concentration (WN), Organic (Up to ten layers) Carbon (CBN), crop residue (RSD), phosphorus sorption ratio (PSP), and organic P concentration (WP). Phenology and yield characteristics for crops, includes: biomass energy ratio (WA), harvest index (HI), optimal temperature (TB), minimum temperature (TG), maximum leaf area index (DMLA), leaf area decline rate (DLAI), biomass energy ratio decline (RBMD), Crop physiology aluminum tolerance (ALT), maximum stomatal conductance (GSI), seeding rate (SDW), maximum crop height (HMX), maximum root depth (RDMX), CO2 concentration (WAC2), minimum C factor (CVM), and heat units (PHU). Cultivation practices used by local farmers which affect the final output of the model including: tillage depth (TLD), Ridge interval Crop management (RIN), Irrigation volume (IA), Pesticide application rate (PAR), fertilizer application rate (FAP), depth of fertilizer placement (FDP), and amount of crop residue (STD).
1. Hydrological Component
This section describes EPIC’s main hydrological components (runoff volume, peak runoff rate, percolation, lateral subsurface flow, potential evaporation, and evapotranspiration) and the options that were selected according data availability. a) Runoff Volume
EPIC’s hydrological component was designed with the following empirical elements. Surface runoff is predicted for daily rainfall by using the SCS curve number: 43
2 Ri 0.2si Qi if Ri ! 0.2 si (6) Ri 0.8si
Q = 0.0, otherwise where, § 100 · s 254¨ 1¸ (7) i ¨ ¸ © CN II ¹
where: Qi is the daily runoff, in mm; Ri is the daily rainfall, in mm; si is the daily soil’s retention parameter (maximum potential), in mm; and CNII is the curve number for
moisture condition II, or average curve number.
b) Peak Runoff Rate
EPIC contains two methods for estimating peak runoff rate, the modified rational
formula and the SCS TR-55 method. The rational equation method is expressed basically
for the following relations:
U Qi A q p (8) 360 tcc tcs
1.75(L* )(n)0.75 t (9) cc * 0.25 0.125 0.375 (qc ) (A) (V )
0.0216(O • n)0.75 t (10) cs * 0.25 0.375 qo s
3 where: qp is the peak runoff rate, in m /s; Qi is the daily runoff, in mm; ȡ is a runoff
coefficient expressing the watershed infiltration characteristics (Qi /Ri); A is the drainage area, in ha; tcc is the time of concentration for channel flow, in h; tcs is the time of concentration for surface flow, in h; L* is the channel length, in km; n is the Manning's 44
roughness, in m/m; qc* is the average flow rate, in mm/h; V is the average channel slope, in m/m; O is the surface slope length, in m; s is the land surface slope, in m/m; and q*o is
the average flow rate from a 1.0 ha area, in mm/h.
The second alternative is to use a method based on TR-55 which is a simplified
method for estimating runoff and peak runoff in small watersheds (USDA, 1986). This
method is based on the time of concentration and assumes a rectangular-shaped channel
to evaluate the peak flow as follows:
2 § QA · log C0 C1 logTc C2 logTc q p ¨ ¸10 (11) © 640 ¹
3 2 where: qp is the peak runoff rate in ft /s, A is the drainage area in miles , Q is the runoff
volume in inches, and c0, c1, and c2 are coefficients based on 24-hour precipitation and
initial abstraction as made with the curve number. In order to avoid c0, c1, and c2 evaluation, for the present study, the rational equation method was used. Thus, the time of concentration was assessed assuming a plot of one hectare of catchment (except for the experimental plots used during model calibration). c) Percolation
In the EPIC model, percolation from an upper soil layer occurs when soil water content exceeds its field capacity and continuous flowing until the storage returns to field capacity. The routing process is simulated, layer by layer, from the soil surface to the deepest layer. If a layer's porosity is exceeded, then the excess water is transferred to the layer above until the excess appear in the top layer. Local variability in infiltration rates 45
can be high because of differences in soil’s structure, compaction, initial moisture content,
and vegetation density. Percolation is evaluated with the following function:
ª §'t ·º Oi = SWoi FCi «1- exp¨- ¸» (12) ¬ © TT i ¹¼
POi - FCi TT i = (13) SCi
where: Oi is the percolation rate for layer i, in mm/d; SWOi is the soil water contents at
the start of time interval ǻt (24 h) for layer i, in mm; TTi is the travel time through layer i,
in h; FCi is the field capacity (33 kPa for many soils) water content layer i , in mm; POi is the soil porosity layer i, in mm; and SCi is the saturated conductivity layer i, in mm/h.
d) Lateral Subsurface Flow
Lateral subsurface flow is calculated simultaneously with percolation. Equations
(12) and (13) are solved simultaneously to avoid that one process dominates as follows:
ª § 24 ·º = 1- exp - QH i SWoi FCi « ¨ ¸» (14) ¬ © TT Hi ¹¼
TT i TT Hi = S (15)
where: QHi is the lateral flow rate for soil layer i, in mm/d; SWOi is the soil water
contents at the start of time interval ǻt (24 h) for layer i, in mm; TTHi is the lateral flow
travel time through layer i, in d; FCi is the field capacity (33 kPa for many soils) water
content layer i , in mm; TTi is the travel time through layer i, in h; and S is the land
surface slope, in m/m. 46
e) Potential Evaporation
The EPIC’s options for estimating potential evaporation (Eo) are: 1) Penman, 2)
Penman-Monteith, 3) Priestley-Taylor, and 4) Hargreaves and Samani. The Penman and
Penman-Monteith methods require solar radiation, air temperature, wind speed, and
relative humidity as input. The model computes evaporation from soils and plants
separately.
Penman method
The potential evaporation is evaluated as follows:
§ G ·§ h0 G · § J · E0 ¨ ¸¨ ¸ ¨ ¸ 2.7 1.62V ea ed (16) © G J ¹© HV ¹ © G J ¹
where: Eo, in mm; į is the slope of the saturation vapor pressure curve, in kPa/°C; Ȗ is the
2 psychrometer constant, in kPa/°C; ho is the net radiation, in MJ/m ; G is the soil heat flux,
in MJ/m2; HV is the latent heat of vaporization, in MJ/kg; V is the mean daily wind speed
at a 10-m height, in m/s; ea is the saturation vapor pressure at mean air temperature, in kPa; and ed is the vapor pressure at mean air temperature, in kPa.
Latent heat of vaporization (HV) and the saturation vapor pressure (ea) are
estimated base on mean temperature functions. The vapor pressure at mean air
temperature (ed) is simulated as a function of ea value and relative humidity.
Psychrometer constant (Ȗ) is estimated as a function of elevation. The soil heat flux (G) is estimated by using air temperature, on the day of interest and 3 days prior. Net radiation 47
(ho) is obtained from the maximum possible solar radiation for the location latitude on the
day of interest, albedo, and the net outgoing long wave radiation.
Penman-Monteith method
The Penman–Monteith method is the only one to account for the effects of the
vapor pressure deficit (VPD) and CO2 concentration on leaf resistance and ET. In EPIC,
stomatal conductance decreases linearly with VPD once a threshold value from
maximum conductance is reached (Izaurralde et al., 2003; Stockle et al., 1992). In this
method potential (Eo) and plant (Ep) evaporation is evaluated as follows:
86.7AD ea ed G h0 G E AR (17) 0 HV G J
86.7AD e e G h G a d 0 AR (18) Ep § § CR ·· HV¨G J ¨1 ¸¸ © © AR ¹¹ where: Ep is the potential evaporation rate of plant water, in mm/d; į is the slope of the
saturation vapor pressure curve, in kPa/°C; Ȗ is the psychrometer constant, in kPa/°C; ho is the net radiation, in MJ/m2; G is the soil heat flux, in MJ/m2; HV is the latent heat of
vaporization, in MJ/kg; ea is the saturation vapor pressure at mean air temperature, in kPa;
and ed is the vapor pressure at mean air temperature, in kPa, and AD is the air density, in g/m3. The aerodynamic resistance (AR) for heat and vapor transfer depends on crop
height and daily mean wind speed. The canopy resistance (CR) for vapor transfer is a 48
function of the leaf-area-index (LAI), the leaf conductance, the C02 in the atmosphere, and the vapor pressure deficit (VPD).
Priestley-Taylor method
This method provides estimates of potential evaporation without wind and relative humidity inputs as follows:
§ ho · § G · Eo = 1.28 ¨ ¸ ¨ ¸ (19) © HV ¹ © G +J ¹ where: Eo is the potential evaporation, in mm; į is the slope of the saturation vapor pressure curve, in kPa/°C; Ȗ is the psychrometer constant, in kPa/°C; ho is the net radiation, in MJ/m2; HV is the latent heat of vaporization, in MJ/kg.
Hargreaves and Samani method
This method estimates potential evapotranspiration as a function of extraterrestrial radiation and air temperature using the following equation:
§ RAMX · 0.6 Eo = 0.0032 ¨ ¸(T +17.8)(T mx - T mn ) (20) © HV ¹ where: Eo is the potential evaporation, in mm; RAMX is the maximum possible solar radiation at the earth's surface, in MJ/m; T, Tmx, and Tmn are the mean, maximum and minimum air temperatures respectively, in qC; and HV is the latent heat of vaporization, in MJ/kg. 49
In the present study was selected the Penman-Monteith22 method, because for the
district was feasible to find monthly mean data for wind speed, sun radiation, and relative
humidity.
f) Evapotranspiration
The model computes evaporation from soils and plants separately. Potential
evaporation rate of plant water (Ep) is estimated as a function of potential evaporation (Eo) and leaf area index (LAI, area of plant leaves relative to the soil surface area). For all methods except Penman-Monteith (see equation 18), potential plant water evaporation is computed as follows:
( Eo )(LAI) E p = , 0 d LAI d 3.0 3.0 (21)
E p = Eo , LAI >3.0 (22)
Potential soil water evaporation (Es) is simulated by considering soil cover (EA)
according to the following equation:
E s = ( Eo )(EA) (23)
2. Weather Component
The weather variables needed to run EPIC are precipitation, air temperature, and
solar radiation. If the Penman and Penman-Monteith method are used to estimate
potential evaporation, wind speed and relative humidity are too required. Wind speed is
also required when wind-induced erosion is simulated. For calibration and validation,
22 Taking into account that Penman-Monteith equation evaluates leaf resistance base on CO2 concentration, in this study, the CO2 atmospheric concentration was hold at 330 ppm. 50
daily precipitation and air temperature must be given with historical weather data. For
future scenarios, the weather generator simulates precipitation, temperature, solar
radiation, wind speed, wind direction, and relative humidity. If an incomplete historic
data set is available, th e generator can be used to fill in missing data but the statistical
parameters must be provided.
a) Precipitation
This sub-model is based on a first-order Markov chain23 that requires monthly probabilities of receiving precipitation and the probability of a wet day following a wet day P(W/W), and the probability of a wet day following a dry day P(W/D). The precipitation generated by EPIC, through the Weather Generator (WXGEN) 24, follows a
skewed normal daily precipitation distribution (Arnold and Williams, 1989; Richardson
and Wright, 1984) as follows:
§ 3 · ¨ ª§ SCF k ·§ SCF k · º ¸ «¨ SNDi - ¸¨ ¸+1» - 1 ¨ ¬© 6.0 ¹© 6.0 ¹ ¼ ¸ (24) Ri = ¨ ¸ RSDV k + Rk ¨ SCF k ¸ ¨ ¸ © ¹
where: Ri is the amount of rainfall for day i, in mm; SNDi is the standard normal deviate
for day i; SCFk is the skew coefficient in month k; RSDVk is the standard deviation of
daily rainfall in month k, in mm; and Rk is the mean daily rainfall in month k, in mm.
23 Markov chain is a stochastic process where the current state is necessary for predicting a subsequent state, except that the previous state is not needed if the current one is known. 24 EPIC’s Weather Generator (WxGEN) used to produce weather variability under its statistical mean values. 51 b) Temperature and Solar Radiation
In EPIC simulated temperature and solar radiation are mutually correlated with rainfall. Thus, on rainy days the maximum temperature and solar radiation tend to be lower than monthly mean and during dry days temperatures increases beyond its mean values. The residuals of daily maximum and minimum air temperature and solar radiation are generated from a multivariate normal distribution, and the serial correlation of each variable may be described by a first-order model. The temperature model, through
WXGEN, requires monthly means of maximum and minimum temperatures and their standard deviations as inputs (Harmel et al., 2002). c) Wind Speed and Wind Direction
The mean daily wind speed is simulated using the monthly mean wind speed distributed thought an exponential equation of speed probabilities. EPIC wind direction is expressed in radians from north in a clockwise way. The daily distribution of wind direction is generated from an empirical function of cumulative probability. d) Relative Humidity
The relative humidity model simulates daily values from the monthly average by using a triangular distribution. As with temperature and radiation, the mean daily relative humidity is adjusted to account for wet and dry days effects. 52
3. Erosion Component
EPIC’s erosion section is divided in water and wind erosion and they are
evaluated as follow:
a) Water Erosion
The EPIC model simulates erosion caused by rainfall, runoff and by irrigation
(sprinkler and furrow) based on the Universal Soil Loss Equation (USLE). To simulate
rainfall/runoff erosion (Y), EPIC has six options as follows:
Y=ȋ (K) (C) (P) (LS) (ROKF) (25) X=EI for USLE 0.33 X=0.646 EI+0.45(Qqp) for Onstad-Foster modification ȋ = 1.586 (Q * q)0.56 *A0.12 for MUSLE (Modified USLE) 0.5 ȋ = 2.5 *(Q * qp) for MUST 0.65 0.009 ȋ = 0.79 * (Q * qp) * A for MUSS by2 by3 by4 ȋ = by1 * Q * qp * A for MUSI
where: Y is the water erosion, in t/ha; ȋ is the energy component; K is the soil erodibility factor; C is the crop management factor; P is the erosion control practice factor; LS is the slope length and steepness factor; ROKF is the coarse fragment factor; Q is the runoff volume, in mm; qp is the peak runoff rate, in mm/hr; A is the watershed area, in ha; and by(1)(2)(3)(4) are the user coefficients. On MUSLE a runoff variable increase the prediction accuracy and enables to estimate sediment yield for a single storm. The
Onstad-Foster equation contains a combination of USLE and MUSLE factors (Williams et al., 1989). Erosion caused by applying irrigation in furrows is estimated with MUST assuming that furrows are triangular. In this study water erosion was evaluated using
USLE because of data availability to evaluate its energy component through monthly maximum rainfall in 30 min (WI). 53 b) Wind Erosion
The EPIC wind erosion model requires the daily distribution of wind speed. The potential erosion is adjusted using four factors based on soil properties, surface roughness, cover, and distance across the field in the wind direction at 10 m above the ground. The basic wind erosion equation is:
DW YW FI FR FV FD YWRdt (26) ³0 where: YW is the wind erosion, in kg/m; FI is the soil erodibility factor; FR is the surface roughness factor; FV is the vegetative cover factor; FD is the mean unsheltered travel distance of wind across a field, in km; DW is the duration of wind greater than threshold velocity, in s; and YWR is the wind erosion rate in kg/m/s at time t.
The surface roughness factor (FR) calculates the erodible fraction of the soil surface by estimating the protection susceptible to abrasion by saltating particles. This factor depends on angle of the wind relative to ridges, ridge height, wind direction, and ridge roughness. The cover factor FV is based on live biomass, standing dead residue, and flat crop residue. The unsheltered travel distance (FD) is a function of the field length, field width and wind direction. YWR is derived from soil water content and wind speed.
Even though were input wind direction and wind speed, wind erosion result are not reported in this study because lack of observations to compare this output.
4. Crop Growth Component
EPIC uses a single model for simulating all crops, although each crop has unique parameters values. In EPIC annual crops grow from planting date to harvest date or until 54
the accumulated heat units reach their potential heat units. Nevertheless, the phenological
development is determined via heat unit accumulation index (HUI), for crops stages like
date of harvest, leaf area growth and senescence, optimum plant nutrient concentrations,
partition of dry matter among roots and shoots, and economic yield. This index ranges
from 0 at planting to 1 at physiological maturity and it computed as follows:
i ¦ HU k k=1 HUI i = (27) PHU j
T mx,k +T mn,k HU k = - Tb j , HU k t 0 2 (28)
where: HUIi is the the heat unit index for day i; PHUj is the potential heat units required
for the maturation of crop j, in qC; HUk is the heat units on day k, in qC; Tmx,k is the
maximum temperature on day k, in qC; Tmn,k is the minimum temperature on day k, in qC; and Tbj is the crop-specific base temperature (no growth occurs at or below Tb) of crop j,
in qC.
a) Potential Growth
The potential daily increase of biomass is estimated with the following equations:
' B p,i = 0.001( BE j )( PARi ) (29)
PARi = 0.5 RAi [1 - exp(-0.65 LAI i )] (30) 55
where: ǻBp,i is the daily potential increase in biomass, in t/ha; BE is the crop parameter
2 25 for converting energy to biomass for crop j, in kg/haMJ/m ; PARi is the intercepted
photosynthetic active radiation during day i, in MJ/m; RAi is the solar radiation during
2 day i of the year, in MJ/m ; LAIi is the leaf area index, during day i of the year. The
amount of solar radiation converted into photosynthate (biomass) is a function of crop-
specific biomass energy conversion efficiency (BE). In EPIC the radiation use efficiency
is adjusted to account for the influence of vapor pressure deficit (VPD).
From emergence to the start of leaf decline, LAI is estimated as a function of heat
units, crop stress, and crop development stages. It is computed in EPIC as follows:
LAI i LAI i1 'HFU LAI mx 1 exp>@5 LAI i1 LAI mx REGi (31)
where: LAImx is the maximum leaf area index value possible for the crop; ǻHUF is the
heat unit daily change factor, and REG is the value of the minimum crop stress factor
In most crops, leaf area index (LAI) is initially zero or very small and increases
exponentially during early vegetative growth. From the start of leaf decline to the end of
the growing season, LAI is estimated as follows:
ad § 1 HUI · j LAI LAI ¨ i ¸ (32) i 0 ¨ ¸ ©1 HUI 0 ¹ where: ad is a parameter that governs LAI decline rate for crop j and subscript 0 is the
day of the year when LAI starts declining.
25 1 MJ = 239 kcal = 0.372 Hp-h = 0.278 kW-h. 56 b) Economic Yield
Yields are expressed as a fraction of above-ground biomass at maturity (by a harvest index), which in turn is a function of photosynthetically active radiation (PAR), leaf area index (LAI) at maturity, water stress, and the efficiency of the harvest operation
(Mearns et al., 2001). The economic yield, the grain for corn, is estimated by using the harvest index concept:
YLD j =( HI j )( BAGj ) (33) where: YLDj is the amount of the crop removed from the field, in t/ha; HIj is the harvest index for the crop j; and BAGj is the above-ground biomass for crop j, in t/ha. For non- stressed conditions harvest index increases non-linearly from 0 at planting to HI at maturity. Thus grain crops are simulated to produce the greatest economic yield in the second half of the growing season. c) Water Use
Potential water use from the soil surface to root depth is a function of potential evaporation rate of soil water (Ep), soil depth (Z), root zone depth from the top 10% of the root zone. The potential water use in each layer is reduced when the soil water storage is less than 25% of plant-available soil water. d) Nitrogen Uptake
The daily crop nitrogen demand for the crop is estimated using a supply-demand approach. The crop N demand is defined by difference between the crop N content and its 57
ideal content (CNB). The demand depends on the accumulated biomass and the actual
uptake rate. CNB declines with increasing the growth stage as a function of the heat unit
index (HUI).
e) Phosphorus Uptake
The daily demand of phosphorus (UPD) for the crop is estimated using a supply-
demand approach. It is a function of the optima concentration for the plant (CPB), accumulated plant biomass -above ground and roots, and the actual P uptake (UP). The optimal concentration (CPB) depends in the crop phenological stage and is a function of
HUI.
f) Growth Constraints
The potential biomass growth and root growth is adjusted daily in proportion to
the most severe constraint for that day, from the following factors: nitrogen stress,
phosphorus stress, and water shortage (through the stomatal conductance based on the
Penman-Monteith model), root aeration, as well as sub-optimal temperatures (heat or
cold), shortages of solar radiation, soil compaction (aeration), excessive soil acidity and
aluminum toxicity. In EPIC, climate change can affect the number of nitrogen-stress days
by altering both the availability and demand for nitrogen during the growing season. The
biomass constraint, used by EPIC, corresponds to the minimum value of the water,
nutrient, temperature, and aeration stresses. Ventilation stress appears when soil’s water
content reaches the saturation point and the porous space is reduced drastically (Brown et
al., 2000; Brown and Rosenberg, 1999; Izaurralde et al., 2003; Stockle et al., 1992). 58
For the root growth also are considered soil depth, temperature, and aluminum toxicity that affects shoot growth. Soil erosion reduces biomass growth by loss of nutrients and reducing root growth because roots need to grow in more compact soil layers (deeper soils horizons) (Barbier and Bergeron, 1999; Williams et al., 1989). g) Crop Yield
Crop yield may be reduced by water-stress shortages through the harvest index
(HI). Optimum conditions for growth may reduce harvest index slightly if biomass accumulation is large and economic yield is being limited by root growth.
5. Crop Tillage Component
This component mixes nutrients and crop residues within the plow depth and simulates the change in bulk density that is associated with each tillage as follows:
§ 2 · BDPi BDPoi ¨ BDPoi BDoi ¸ EF (34) © 3 ¹
3 where: BDP is the bulk density after tillage, in t/m ; BDPoi, is the bulk density in soil
3 layer i before tillage, in t/m ; BDo is the bulk density of the soil when it has completely settled after tillage, in t/m3; and EF is the mixing efficiency of the tillage operation (0-1).
Other functions of the tillage component include converting standing residue to flat residue, simulating ridge height and interval, and surface roughness. 59
6. Sensitivity Analysis
Sensitivity analysis indicates by how much the output of a model alters in relation to a unit change in the value of one or more of the inputs (Morgan, 2005). Sharpley and
Williams (1990a) mention that model calibration is an adjustment process for the most sensible reported parameters in order to improve yield simulation. Most of the simulation models, with a great number of parameters, are significantly sensitive to few variables and a higher level of accuracy is required for those inputs. Under certain physical limits these parameters can be changed to improve simulation and it is recommended to recalibrate the model for different physical and crop conditions.
The sensitivity analysis also may be used to simplify the model, reducing processes, without significant loss of accuracy. Usually, the model’s accuracy is tested by comparing predicted with measured values and applying a measure of goodness-of-fit.
Ideally, a regression equation for the relationship should have a slope of 1.0 and pass through zero. The models local validation allows to know which are feasible or no sensible judgments base on the severity of the problems and likely costs (Morgan, 2005).
EPIC-simulated yields have compared well against historic yield averages under a wide range of cropping and environmental conditions. However, the simulated corn yields are overestimated when there are anomalous climatic events such as late or early frosts, hail storms, windstorms, floods or outbreaks of weeds, diseases, and insects. Thus, one or all of these factors may come together to overrate EPIC’s mean historic yields
(Brown et al., 2000; Brown and Rosenberg, 1999; Izaurralde et al., 1999). 60
H. GIS and Agricultural Environmental Modeling
The process of crop productivity and soil erosion simulation through models represents an important advance in the evaluation of strategies. The previously described models are location specific (point based) in nature, but in decision making, spatial variability within a region needs to be evaluated to understand the impacts of weather, soil, and agricultural practices on the agricultural systems. However, agroecosystem modeling on a spatial basis is an overwhelming task of data manipulation that can be facilitated by geographical information systems (GIS).
In this sense, GIS technology provides powerful tools to know the impact of differences between inputs and output spatially, in particular, to assess simultaneously the effect of farm practices to crop production in addition to soil and water resources.
Consequently, model simulation can be substantially improved when the input data is integrated with GIS to manage, maintain, manipulate, and analyze spatial data efficiently.
Besides to offer a mechanism to integrate many scales of data, GIS improves data consistency between the information layers and the simulation model. Additionally, GIS provides modelers with a visual presentation of spatial data and modeling results (Baird,
1999; Moen et al., 1994; Singh, 1995).
I. Conclusions from Literature Review
Soil degradation have been covered up or compensated by increasing amounts of fertilizers, irrigation, and pesticides. It has been observed that without these crop inputs, crop productivity in poor soils becomes more susceptible to droughts, frost, floods and 61 pest attacks. However soil, water and fertility management practices are an option that must be analyzed to reduce costs associated with fertilization and irrigation.
Mexico is water-scarce and highly eroded country with increasing corn’s importations. Therefore, soil and water conservation practices seem to be the best investment, in the long term, to reduce vulnerability to soil degradation, drought menace, and sustain the regional food security base.
EPIC has simulated well historic averages crop yields and soil erosion under a wide range of cropping and environmental conditions and the impact of soil erosion on crop productivity in long-term simulations. For these reasons, this process-based model was selected in this study to evaluate the cumulative effect of water erosion on corn productivity. The model also was chosen, because it is feasible to calibrate EPIC’s empirical process for the region from experiments established in different areas, periods, and management practices.
Models are useful tools to assess crop productivity under a broad range of conditions and their application can substantially improve when they are working together with GIS to evaluate regional strategies and technologies. In this sense, it is feasible to use a GIS to validate EPIC on a regional basis, if it is assumed that the average yields into a region are produced by average temporal and spatial climate conditions, soil properties, crop management, and seed genetic characteristic (Moen et al., 1994).
Besides, by means of GIS the model can be useful to identify the most vulnerable areas for soil degradation and categorize the BMPs in the region. 62
III. METHODOLOGY
A. Site Description - Texcoco District
The study area is located in the district of Texcoco, at the eastern region of the
Basin of Mexico and the Estado de Mexico (Figure 3). This area has a long history of
human occupation and has been an important focus for human settlement over the last six
to seven thousand years (Sanders et al., 1979). The Texcoco district covers an area of
260,348 hectares -12% of the state’s surface area- and is located between 18°51’02’’ and
19°39’49’’north latitude and between 98°37´50” and 99°07´47” west longitude.
The Texcoco district is part of the subprovince of the lakes and volcanoes of
Anahuac which is located in the middle of the Trans-Mexican Neovolcanic Belt -a Late
Tertiary formation of 20-70 km wide that crosses the country from coast to coast (Ezcurra
et al., 1999). Its topography is characterized by plateaus, mountain ranges, plains and
valleys. The elevation in the district varies from 1,760-5,452 m above sea level, with an
average elevation of 2240 m.
According to García (1973) this region has the most humid of the temperate
climates with 660.7 mm of mean annual rainfall and mean annual temperature between
15 and 16°C in the lacustrine plains with little annual variability, less than 5°C. The
temperature range for the coldest month is between -2 and 18 °C, and for the warmest
month, it oscillates between 7.5 and 27°C. Frosts are present from October to March and
as late as April (Lozano and Xelhuantzi, 1997). 63
101°0'0"W 100°0'0"W 99°0'0"W 98°0'0"W Legend Basin of Mexico at Central Mexico Guanajuato [_ [_ 110°0'0"W 100°0'0"W 90°0'0"W 21°0'0"N 21°0'0"N Observatories Cities country
Queretaro Basin of Mexico [_ Central Mexico
30°0'0"N 30°0'0"N State of Mexico Pachuca Tulancingo [_ [_ 20°0'0"N 20°0'0"N
Morelia [_ 1:3,500,000 Tacubaya[_ To l u c a[_ [_ Montecillo Tlaxcala D.F. [_ Estado de México Puebla 20°0'0"N 20°0'0"N Cuernavaca [_ 19°0'0"N [_ 19°0'0"N
110°0'0"W 100°0'0"W 90°0'0"W
101°0'0"W 100°0'0"W 99°0'0"W 98°0'0"W 101°0'0"W 100°0'0"W 99°0'0"W 98°0'0"W Basin of Mexico and Sub-basins into The Texcoco District 450000 460000 470000 480000 490000 500000 510000 520000 530000 540000
TAXHID O JILOTZINGO TEZONTEPEC HUEHUETOCA TIZAYUCA XALPA TEMASCALAPA SAN JUAN ZITLALTEPEC ZUMPANGO COYOTEPEC REYES ACOZAC, LOS 2190000 2190000 TEOLOYUCANJALTENCO SANTA ANA NEXTLALPAN BARRIO DE LAS ANIMAS JALTEPEC TEPOTZOTLÁN TECÁMAC MELCHOR OCAMPO SAN MARTÍN DE LAS PIRÁMIDESOTUMBA TULTEPEC 2180000 CUAUTITLÁN OZUMBILLA San Juan Teotihuacán 2180000 TULTITLÁN CHICONAUTLA TEXCOCO NICOLÁS ROMERO VENTA DE CARPIO TEPEXPAN QUEBRADA, LA ECATEPEC 2170000 2170000 TEPETLAOXTOC ACUEXCOMAC CIUDAD LÓPEZ MATEOS ATENCO TLALNEPANTLA CHILUCA CHICONCUAC Papalotla TEXCOCOXalapango
2160000 Lower Area (< 2260 msmm) 2160000 HUEXOTLACoxcacoaco ChapingoTe xc o co San Bernardino MÉXICO CITY CHIMALHUACÁN Santa Mónica 2150000 2150000 CIUDAD NEZAHUALCÓYOTLCoatepec HUIXQUILOCANCUAJIMALPA REYES ACAQUILAPAN, LOS SANTA LUCÍA FRACCIONAMIENTO ACOZACSan Francisco TLALPIZAHUAC ACOZAC 2140000 AYOTLA 2140000 IXTAPALUCA
XICO CHALCO SAN JUAN IXTAYOPAN La Compañía COCOTITLÁN 2130000 MIXQUIC 2130000 SAN ANTONIO TECOMITL SAN PEDRO ATOCPAN TEMAMATLA PUEBLO NUEVO MILPA ALTA TLALMANALCO XALATLACO SAN PABLO OZTOTEPEC SANTIAGO TILAPA TENANGO DEL AIRE
2120000 COATEPEC Amecameca 2120000 AMECAMECA JUCHITEPEC 2110000 2110000 2100000 2100000
450000 460000 470000 480000 490000 500000 510000 520000 530000 540000 2090000
Legend Meters 010,000 20,000 40,000 To w ns 4 Basin of Mexico 1:600,000 District boundary Eastern Sub-basins Projected Coordinate System: UTM, Zone 14N Datum: North American 1927 Urban Areas Figure 3. Texcoco’s district localization map, according to the national, state and the basin of Mexico boundaries (Source: INEGI digital databases scale 1:250,000). 64
The rainy season in the area is from June to October, with 70 and 90% of the precipitation occurring during this period. The remaining seven or eight months are quite dry although some events occur during the winter (Lozano and Xelhuantzi, 1997). This district has a SE-NW climatic gradient, due in part to the marked topographic relief; moisture is higher in the south (average annual precipitation between 800-1000 mm) than in the semiarid north and northwestern plains with 400-600 mm of rainfall.
Agricultural activities occupy 97,212 ha, with 83,089 ha (85%) are rainfed and the rest 14,123 ha (15%) are cultivated under irrigation. The main crop in the district is corn, planted at the beginning of the rain season, where it is cultivated between 40,000 and 70,000 hectares annually with an average yield between 2.0 and 3.6 t/ha. According to the FAO26 soil classification in the region the main soil types are Vertisol Pellic and
Feozem Haplic, while silty clay loam and clay loam are the most common soil textures
(Ezcurra et al., 1999).
B. Design of the Study
1. Database Sources
For the present work, digital elevation model (DEM), contours, roads, human settlements, administrative boundaries, hydrological features and soil boundaries were obtained from INEGI27 with a scale 1:50,000. The database was analyzed and stored in
26 United of Nations Organization for Food and Agriculture (FAO). http://www.fao.org/ 27 Mexican institute for integrating statistical and geographic information (INEGI). http://www.inegi.gob.mx 65
ArcMap 9.228 layers with explicit representation of their geographic features and attributes. All geographic information collected were standardized according to INEGI cartography, where the projection system adopted was the UTM29 (zone 14 north), based
on Clarke’s 1866 international spheroid and NAD2730 the reference datum. This
homogenization allowed for the use of raster procedures using GRID commands of
ArcMap and extract EPIC’s parameters (ver. 5300) to run model validation and
simulation scenarios.
2. Study Development
The EPIC model was assembled to estimate biomass, runoff and sediment yield
on a daily, monthly, and annual (crop cycle for seasonal plants) time step (Williams,
1990). For this study it was chosen to use an annual output in order to calibrate and
validate EPIC. The annual time step (focus on the crop cycle) was considered the best
option in terms of data availability and also because it better matches the main goal of
this research: analyze the long-term relationship (e.g. hundred of years) between soil
erosion and crop productivity (E/P) in order to recommend the BMPs for the region.
Although were used daily weather records for model calibration and model validation,
the comparison between simulated and observed was always performed on annual basis.
This study does not attempt to predict accurately the relationship between soil
erosion and crop productivity but rather to understand broad tendencies on corn
28 ArcMap is a Geographic Information System (GIS) program produced by the Environmental Systems Research Institute (ESRI). http://www.esri.com 29 UTM: Universal Transverse Mercator. 30 NAD27: North American Datum 1927. 66 production in the Texcoco District; through the BMPs that could maintain corn yield for the most vulnerable lands of the region. The Figure 4 shows the general procedure that was followed in the present study to calibrate, validate, and evaluate the BMPs.
3. Calibration Process
The EPIC model was calibrated using 352 experimental corn plots; 308 assessing grain productivity (Appendix A1) and 44 evaluating the total soil erosion and runoff during the crop cycle (Appendix A2). These parcels were set up between 1972 and 1992 by the authors that are shown in the Table 2. The crop management and the environmental data of each experimental plot were taken from 17 thesis reports covering fifty locations (Table 2) in 36 rural communities (Figure 5) of the district. The plot’s temporal distribution and geographic dispersion covers an extensive sort of soils properties, topographic conditions, and weather circumstances. These plots also represent a wide range of cultural practices, seed varieties, seedtime, plant population, irrigation amounts, and fertilization rates in order to raise corn into the district. The varies environmental conditions and crop management allows strengthening the model calibration for regional analysis; which is the main purpose of this study. From these experimental parcels 19 management strategies were identified; most of them for fertility, soil and water conservation (Table 4). Agriculture Area Irrigated Polygons Soil Map
HRU
Average Soil Topographic data Erosion Crop yield & from DEM Crop CM soil erosion 1 Average Corn distribution E Yield P Climate and/or I weather information C Identify how Soil erosion over CM impacts, 100 years water balance, 2 M E/P relationship for CM corn yield and Corn yield over 100 erosion O years D Spatial Corn RM Validation on a E municipio basis L Soil erosion over Comparison E/P relationship for RM 100 years between RM 3 Corn yield over 100 and CM years Model calibration from experimental plots Average Comparison Soil Erosion between BMPs 4 and CM Catalog BMPs Evaluate feasible BMPs in the most representative soils Average Corn Yield
Figure 4. Flow diagram for model calibration, validation, and analysis of crop management scenarios. 67 Table 2. Location and physical characteristics of the experimental plots. Slope Channel Weather Num Place Latitude Longitude Elevation Soil Profile Authors Steepness Slope Station YLT ELEV S CHS Table 10 and deg. deg. m m/m m/m Appendix B1 Appendix A3 1 Amecameca 19.1083 -98.7667 2479 0.125 0.0625 15007 Amecameca Estrella (1973) and Peña (1973) 2 Amecameca 19.0944 -98.7444 2479 0.125 0.0625 15103 Amecameca II Estrella (1973) and Peña (1973) 3 Amecameca 19.1375 -98.7708 2479 0.01 0.005 15007 Amecameca III Estrella (1973) and Peña (1973) 4 Amecameca 19.1555 -98.7764 2479 0.01 0.005 15007 Amecameca IV Estrella (1973) and Peña (1973) 5 Chalco 19.2292 -98.8917 2243 0.01 0.005 15020 Chalco Estrella (1973) and Peña (1973) 6 Chalco 19.2250 -98.9167 2243 0.01 0.005 15020 Chalco II Estrella (1973) and Peña (1973) 7 Chicoloapan 19.4000 -98.9500 2260 0.008 0.004 15167 Chicoloapan Alvarado (1975) and Ortíz (1974) 8 Chiconcuac 19.5510 -98.9093 2240 0.016 0.008 15138 Chiconcuac Alvarado (1975) and Ortíz (1974) 9 Chiconcuac 19.5500 -98.9083 2240 0.019 0.0095 15138 Chiconcuac II Alvarado (1975) and Ortíz (1974) 10 Chimalhuacán 19.4000 -98.9333 2240 0.032 0.016 15167 Chimalhuacán Alvarado (1975) and Ortíz (1974) 11 Coatlinchán 19.4666 -98.8583 2300 0.014 0.007 15167 Coatlinchán Alvarado (1975) and Ortíz (1974) 12 Coatlinchán 19.4676 -98.8593 2300 0.024 0.012 15167 Coatlinchán II Alvarado (1975) and Ortíz (1974) 13 Cocotitlán 19.2250 -98.8750 2250 0.0075 0.00375 15020 Cocotitlán Estrella (1973) and Peña (1973) 14 Cocotitlán 19.2292 -98.8292 2250 0.025 0.0125 15020 Cocotitlán II Estrella (1973) and Peña (1973) 15 Cocotitlán 19.2333 -98.8542 2250 0.025 0.0125 15020 Cocotitlán III Estrella (1973) and Peña (1973) 16 Cocotitlán 19.2542 -98.8333 2250 0.025 0.0125 15020 Cocotitlán IV Estrella (1973) and Peña (1973) 17 Colonia 19.4916 -98.8333 2380 0.029 0.0145 15170 Colonia Alvarado (1975) and Ortíz (1974) 18 INIFAP, Chapingo 19.2833 -98.8833 2248 0.005 0.0025 15000 INIFAP González and Zúñiga (1992) 19 Juchitepec 19.1305 -98.8375 2306 0.03 0.015 15094 Juchitepec Estrella (1973) and Peña (1973) 20 Juchitepec 19.1014 -98.8694 2306 0.12 0.06 15039 Juchitepec I Estrella (1973) and Peña (1973) 21 Juchitepec 19.1250 -98.8708 2306 0.12 0.06 15039 Juchitepec II Estrella (1973) and Peña (1973) 22 Juchitepec 19.1208 -98.8417 2306 0.03 0.015 15094 Juchitepec III Estrella (1973) and Peña (1973) 23 Loma de Guadalupe 19.4000 -98.9583 2280 0.022 0.011 15167 Loma de Guadalupe Alvarado (1975) and Ortíz (1974) 24 Loma de Guadalupe 19.4010 -98.9593 2280 0.012 0.006 15167 Loma de Guadalupe II Alvarado (1975) and Ortíz (1974) 25 Lomas S.J. Chapingo 19.5333 -98.8583 2300 0.033 0.003 15170 Lomas de San Juan Trueba (1978) 26 Lomas S.J. Chapingo 19.5343 -98.8573 2300 0.033 0.003 15170 Lomas de San Juan II Zazueta (1984) 27 Lomas S.J. Chapingo 19.5353 -98.8563 2300 0.035 0.0072 15170 Lomas de San Juan III Campos de Jesús (1982) 28 Lomas S.J. Chapingo 19.5363 -98.8553 2300 0.0259 0.003 15170 Lomas de San Juan IV Ríos (1987) 29 Lomas S.J. Chapingo 19.5363 -98.8553 2300 0.0259 0.003 15170 Lomas de San Juan IV Ventura (1988) 30 Lomas S.J. Chapingo 19.5373 -98.8543 2300 0.018 0.003 15170 Lomas de San Juan V Macias (1992) 31 Lomas S.J. Chapingo 19.5383 -98.8533 2300 0.035 0.003 15170 Lomas de San Juan VI Ruíz (1979) 32 Lomas S.J. Chapingo 19.5393 -98.8523 2300 0.018 0.0072 15170 Lomas de San Juan VII Antezana (1978) 33 Montecillo, CP 19.4833 -98.9000 2240 0.005 0.0025 15000 Montecillo, CP Veliz (1993) 34 Nativitas 19.5000 -98.8416 2360 0.024 0.012 15170 Nativitas Alvarado (1975) and Ortíz (1974) 68 Table 2. Location and physical characteristics of the experimental plots — Continued. Num Place Latitude Longitude Elevation Slope Steepness Channel Slope Weather Station Soil Profile Authors YLT ELEV S CHS Table 10 and deg. deg. m m/m m/m Appendix B1 Appendix A3 35 Nativitas 19.5330 -98.8500 2300 0.03 0.0072 15170 Nativitas II Solano de la Sala (1982) 36 Papalotla 19.5583 -98.8416 2270 0.013 0.0065 15138 Papalotla Alvarado (1975) and Ortíz (1974) 37 Papalotla 19.5593 -98.8426 2270 0.021 0.0105 15138 Papalotla II Alvarado (1975) and Ortíz (1974) 38 San Dieguito 19.5330 -98.8500 2400 0.01 0.0072 15170 San Dieguito Solano de la Sala (1982) 39 Tecamac, CP 19.7170 -98.9500 2460 0.0075 0.00375 15090 Tecamac Mora (1990) 40 Tecamac, CP 19.7180 -98.9510 2460 0.0075 0.00375 15090 Tecamac II Osorio (1989) 41 Tenango del Aire 19.1389 -98.8542 2400 0.025 0.0125 15094 Tenango del Aire Estrella. (1973) and Peña (1973) 42 Tenango del Aire 19.0944 -98.8055 2400 0.1 0.05 15007 Tenango del Aire II Estrella (1973) and Peña (1973) 43 Tenango del Aire 19.1222 -98.8555 2400 0.03 0.015 15094 Tenango del Aire III Estrella (1973) and Peña (1973) 44 Tezoyuca 19.6000 -98.9250 2250 0.023 0.0115 15124 Tezoyuca Alvarado (1975) and Ortíz (1974) 45 Tezoyuca 19.6010 -98.9260 2250 0.018 0.009 15124 Tezoyuca II Alvarado (1975) and Ortíz (1974) 46 Tlalmanalco 19.2028 -98.7667 2389 0.02 0.01 15106 Tlalmanalco Estrella (1973) and Peña (1973) 47 Tlalmanalco 19.1889 -98.7778 2389 0.02 0.01 15106 Tlalmanalco II Estrella (1973) and Peña (1973) 48 Tlalmanalco 19.1917 -98.7653 2389 0.02 0.01 15106 Tlalmanalco III Estrella (1973) and Peña (1973) 49 Totolzingo 19.6250 -98.9583 2260 0.028 0.014 15124 Totolzingo Alvarado (1975) and Ortíz (1974) 50 Xaltepa 19.4916 -98.8583 2300 0.017 0.0085 15170 Xaltepa Alvarado (1975) and Ortíz (1974) 69 70
490000 500000 510000 520000 530000 540000
TotolzingoI# Tezoyuca 2170000 TotolzingoI# 2170000 Tezoyuca I#Papalotla I# Papalotla ChiconcuacI#I#
2160000 Nativitas 2160000 Colonia INIFAPI# I#I#I# I# #I Montecillo, CP Coatlinchán 2150000 2150000 Loma de Guadalupe I#I#Chicoloapan Loma de Guadalupe I# Chicoloapan Chimalhuacán 2140000 2140000
#Cocotitlán 2130000 I 2130000 Chalco ChalcoI# I# I#I# I# Te xt ChalcoCocotitlán CocotitlánI# I#I#Tlalmanalco Tlalmanalco Amecameca
2120000 I# 2120000 Juchitepec JuchitepecI#I#I# I# I#I#I# Amecameca Juchitepec I# I# I# I# Tenango del Aire Amecameca 2110000 2110000 2100000 2100000
490000 500000 510000 520000 530000 540000 Legend Meters I# Plots EPIC calibration 07,500 15,000 30,000 Sub_basins_UTM 1:400,000
District Boundary Projected Coordinate System: UTM, Zone 14N 4 Datum: North American 1927 Corn Figure 5. Location of the experimental plots used for Epic’s calibration. 71 a) The Curve Number
In EPIC, the curve number (CN) is one of the most sensitive parameters to calculate runoff. It implies that CN value must be selected carefully, because its selection also affects the amounts of soil erosion and crop productivity (by affecting the runoff rate). The EPIC model requires, as input, the CN value for the average rainfall event during the crop cycle (Sharpley and Williams, 1990a). In order to observe how CN behaves under corn conditions and for the amounts of precipitation in the region, this parameter was calculated using 20 plots with different conservation strategies for conventional till (CT), minimum till (MT), and no till (NT). The results are shown in
Figure 6 where CN was evaluated in terms of storm volume and its corresponding runoff event as follows (Hawkins, 1993):
R 0.2S 2 R 2 0.4PS 0.04S 2 Q from equation 6 R 0.8S R 0.8S
Resolving via quadratic equation:
§ 2 · S 5¨ R 2Q 4Q 5RQ ¸ © ¹
25,400 Since CN from equation 7 254 S
25,400 CN (35) § 2 · 254 5¨ R 2Q 4Q 5RQ ¸ © ¹
The minimum CN value where runoff starts (Q=0 in eq. 35)
25,400 CNo (36) 254 5R 72
where: Q is the runoff event, in mm; R is the rainfall event, in mm; S is the soil’s
retention parameter (maximum potential), in mm; and CN is the curve number.
Conventional Till (CT) Minimum Till (MT) 100 100
90 90
80 80 CN CN 70 70
60 60
50 50 0 102030405060 0 102030405060 P (mm) P (mm) Straight Row (2) 50% Mulching (3) Straight Row (3) Straight Row (3) Bare Soil (3) CNo 1yr rest (3) Straight Row (1) Contouring (1) CNo
No Till (NT) Terraces (3 Types) 100 100
90 90
80 80 CN 70 CN 70
60 60
50 50 0 102030405060 0 102030405060
0% Mulching (3) P (mm) NT, Bank (1) 33% Mulching (3) NT, SARH (1) P (mm) 66% Mulching (3) NT, CP (1) 100% Mulching (3) CT, Bank (1) Straight Row (1) CT, SARH (1) Contouring (1) CT, CP (1) CNo CNo Figure 6. Evaluated CN values for corn plots under different management strategies into the region. Data source: Zazueta (1984), Solano (1982), and Tapia (1999).
Figure 6 shows that the observed data follows a trend referred to as standard behavior with an asymptotic tendency, or lower CN value, around 65. Over the line of 73 minimum CN values (CNo), the figure, shows the rainfall events without runoff. The observed data dispersion could be associated with the antecedent soil’s moisture condition (Hawkins, 1993).
From Figure 6, one can observe that there is no clear tendency to discriminate crop management, mechanical practices, soil physical properties (infiltration capacity), cover density (hydrological condition) or soil moisture. However for the hydrology groups31 where the plots were established (C and D) and for 11.8 mm of average rain storm (from 681 events), the CN values are in the range proposed by USDA-SCS (1985)
32 (between 82 and 94) for fallow and row crops (Table 3).
Table 3. Runoff curve numbers for hydrologic soil-cover complex for tillage, residue, and mechanical management (USDA-SCS, 1985). Land Use Treatment or practice Hydrologic Hydrologic soil group condition A B C D Fallow Straight row (SR) -- 77 86 91 94 Poor 72 81 88 91 SR Good 67 78 85 89 Poor 71 80 87 90 SR and Conservation tillage (CsT) Good 64 75 82 85 Poor 71 80 87 85 SR and mulching Good 64 75 82 88 Poor 70 79 84 88 Contoured Good 65 75 82 86 Row crops Poor 69 78 83 87 Contoured and CsT Good 64 74 81 85 Poor 69 78 83 87 Contoured and mulching Good 64 74 81 85 Poor 66 74 80 82 Contoured and terraced Good 62 71 78 81 Poor 65 73 79 81 Contoured, terraced, and CsT Good 61 70 77 80
31 Hydrologic Soil Groups: group A (low runoff potential), group B (moderate infiltration), group C (slow infiltration rates), and group D (high runoff potential). 32 National Engineering Handbook, Section 4, Hydrology (NEH4). The current version is NEH-630 chapter 9 by the Natural Resources Conservation Service (NRCS). ftp://ftp.wcc.nrcs.usda.gov/downloads/hydrology_hydraulics/neh630 74
Based on the similarity between the CN figures reported by the Engineering
Handbook and the CN values observed for the average rain storm, it was decided to use the CN values proposed by the USDA-SCS (1985). As a result, Table 4 shows the CN values used for the 19 management conservation strategies (fertility, soil, and water) identified during this study. The hydrologic groups showed in Table 4 are based on soil texture values (Wanielista, 1990), reported in the Appendix A3, for upper soil layer. b) Climate Data
For EPIC’s calibration, daily data of temperature (maximum and minimum) and rainfall from the closest meteorological stations to each plot were used between the fallow and the harvest period. Even though the calibration was performed using daily data, EPIC generated the daily values of solar radiation based on its mean monthly values
(OBSL) and daily rainfall. In the same way, the rainfall erosivity is evaluated in EPIC by using the maximum rainfall in 30 minutes (TP5), maximum rainfall in six hours for a return period of 10 years (TP6), and monthly maximum rainfall in 30 min (WI) for the period of record (TP24). Thus, from López (1996) and Cortes (1992) were obtained TP5,
TP6, WI, and TP24 from 10 meteorological observatories around the study area (
Table 5, Table 6, and Figure 7). From Ortíz (1987), Table 7 shows the monthly average daily solar radiation (OBSL). All these climatic parameter were interpolated and extracted in GIS for the validation and simulation of management scenarios that are described afterward. 75
Table 4. Crop management strategies for fertility, soil, and water conservation. Surface Erosion Runoff Roughness Hydro- Channel Control Condi- Mean Curve Num Crop Management Tillage logic Roughness Practice tion CN Number and Group33 Factor Code Residues SN CHN PEC CN2 1 Conventional till (CT), contouring 0.09 0.015 0.70 C Good 81 1 C Fair 83 2 C Poor 85 4 2 Conventional till (CT), contouring, beef 0.09 0.015 0.60 C Poor 88 5 manure 3 Conventional till (CT), contouring, 0.09 0.015 0.45 D Poor 82 6 terraces 4 Conventional till (CT), furrow dike 0.09 0.033 0.60 C Poor 88 7 5 Conventional till (CT), irrigation 0.09 0.025 1.00 C Good 85 8 6 Conventional till (CT), irrigation, beef 0.09 0.025 0.90 C Good 85 9 manure 7 Conventional till (CT), irrigation, 0.19 0.025 0.70 C Good 85 10 mulching, beef manure 8 Conventional till (CT), poultry manure 0.09 0.033 0.90 B Poor 86 11 C Good 85 12 9 Conventional till (CT), straight row 0.09 0.033 1.00 B Poor 82 13 B Fair 83 14 C Good 85 15 C Poor 88 16 D Poor 92 17 10 Conventional till (CT), straight row, 0.09 0.015 0.75 C Poor 80 18 terraces D Fair 82 19 D Poor 84 20 11 Minimum till (MT), beef manure 0.08 0.025 0.75 D Good 87 21 12 Minimum till (MT), contouring 0.08 0.015 0.55 D Poor 88 22 13 Minimum till (MT), contouring, mulching 0.19 0.025 0..35 C Poor 82 23 14 Minimum till (MT), mulching 0.19 0.025 0.65 C Poor 86 24 15 Minimum till (MT), mulching, beef 0.19 0.025 0.55 C Poor 84 25 manure 16 Minimum till (MT), straight row 0.08 0.025 0.85 C Poor 85 26 17 No till (NT), contouring 0.07 0.015 0.40 D Good 85 27 D Poor 87 28 18 No till (NT), contouring, terraces 0.07 0.015 0.15 B Poor 80 29 C Good 81 30 C Fair 82 31 19 No till (NT), contouring, mulching 0.19 0.015 0.20 C Fair 79 32
33 Hydrologic Soil Groups based on texture (Wanielista, 1990): group A (Sand, loamy sand, or sandy loam), group B (Silt loam or loam), group C (Sandy clay loam), and group D (Clay loam, silty clay loam, sandy clay, silty clay, or clay). 76
Table 5. Meteorological observatories around Texcoco’s District with rainfall intensity data. Place Latitude Longitude Rainfall intensity TP24 Rainfall Elevation RP=10 yr (mm) annual deg. deg. TP5 TP6 Yr mm m Tacubaya -99.20041 19.39707 37.65 65.36 11 720.8 2308 Cuernavaca -99.22270 18.92240 30.14 95.59 5 1061 2148 Puebla -98.18068 19.04299 42.6 56.07 11 822.9 2162 Tlaxcala -98.22467 19.31713 27.98 46.1 9 802.3 2252 Tulancingo -98.36153 20.07396 31.83 38.2 11 760.7 2222 Pachuca -98.73511 20.11038 26.98 45.84 11 800.2 2426 Chapingo -98.88795 19.49333 20.27 45.56 11 552.9 2250 Montecillo -98.89996 19.46677 21.05 49.68 11 586.8 2250 Toluca -99.65656 19.27860 30.65 62.03 7 683.7 2680 Querétaro -100.39260 20.58610 37.93 62.59 11 591.7 1842 Table 6. Monthly maximum rainfall in 30 min (WI) for period of record TP24 at Central Mexico. Place Long. Lat. Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec deg. deg. mm mm Mm mm Mm mm mm Mm mm mm mm mm Tacubaya -99.20041 19.39707 1.60 5.06 3.11 5.49 12.34 20.86 18.26 17.59 18.93 14.79 2.10 3.98 Cuernavaca -99.22270 18.92240 2.73 2.82 2.20 7.83 14.23 18.63 20.68 20.96 25.40 20.15 1.56 2.64 Puebla -98.18068 19.04299 4.09 3.82 3.28 9.13 16.19 22.87 22.36 15.62 19.31 10.56 5.49 1.50 Tlaxcala -98.22467 19.31713 2.88 5.37 2.22 10.91 14.08 18.36 16.72 19.69 15.94 13.48 8.73 2.61 Morelia -101.2008 19.69847 4.18 2.50 3.10 7.67 11.71 14.81 13.37 18.15 12.47 9.41 5.28 2.85 Toluca -99.65656 19.27860 1.54 2.90 5.20 7.30 11.44 14.56 11.46 15.64 12.80 7.80 3.89 3.65 Tulancingo -98.36153 20.07396 3.51 2.58 4.34 6.50 8.09 9.97 7.77 7.53 8.83 5.26 4.19 1.72 Pachuca -98.73511 20.11038 1.92 2.15 2.63 5.57 8.61 9.94 8.61 8.76 5.14 3.65 2.90 1.25 Guanajuato -101.2489 21.01140 3.90 2.14 1.41 6.44 8.79 13.62 21.46 16.65 16.36 7.80 6.92 2.62 Queretaro -100.3926 20.58610 3.70 7.13 7.08 4.08 12.98 18.66 20.36 15.19 16.88 8.40 3.19 4.97
Table 7. Monthly average daily solar radiation (OBSL) at Central Mexico (Ortíz, 1987). City Longitude Latitude Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Degrees degrees ly/d ly/d ly/d ly/d ly/d ly/d ly/d ly/d ly/d ly/d ly/d ly/d Toluca -99.65656 19.27860 388 446 470 460 478 451 449 448 403 411 388 351 Pachuca -98.73511 20.11038 416 480 510 561 557 511 509 524 446 455 425 391 Tacubaya -99.20041 19.39707 354 446 495 470 488 442 440 448 394 395 353 312 Tlaxcala -98.22467 19.31713 381 431 453 433 478 451 459 448 412 427 409 371 Morelia -101.20080 19.69847 330 397 442 451 451 415 413 430 385 408 349 302 Guanajuato -101.24890 21.01143 413 494 565 578 567 523 520 542 487 481 436 376 Chapingo -98.88795 19.49333 413 491 537 561 574 500 478 495 447 458 442 401 Puebla -98.18068 19.04299 430 501 548 562 554 518 515 532 457 476 446 405 Tulancingo -98.36153 20.07396 383 450 485 506 528 492 480 486 411 408 390 359 Montecillo -98.89996 19.46677 441 505 554 540 549 493 486 490 454 477 454 423 77 78
101°0'0"W 100°0'0"W 99°0'0"W 98°0'0"W Legend Guanajuato 21°0'0"N [_ 21°0'0"N [_ Observatories Cities country
Queretaro Basin of Mexico [_ Central Mexico State of Mexico Pachuca Tulancingo [_ [_ 20°0'0"N 20°0'0"N
Morelia [_ 1:3,500,000 Ta cu b[_ ay a To lu ca[_ [_ Montecillo Tlaxcala D.F. [_ Estado de México Puebla Cuernavaca [_ 19°0'0"N [_ 19°0'0"N
101°0'0"W 100°0'0"W 99°0'0"W 98°0'0"W Figure 7. Meteorological observatories around the study area. c) Physiological Data
In this study it was opted to calibrate thought modifications in the corn’s physiological parameters recommended by EPIC. Most of the these factors, which are recommended in the model documentation, were maintained unchanged34 (Sharpley and
34 Corn parameters recommended by EPIC that were maintained unchanged (file clascrop.dat): Optimal temperature for plant growth (TB): 25 °C, Minimum temperature for plant growth (TG): 8°C, Maximum stomatal conductance (GSI): 0.007, Critical aeration factor (CAF): 0.85, Minimum value of C factor for water erosion (CVM): 0.2, Fraction of nitrogen in yield (CNY): 0.0175 kg/kg, Fraction of phosphorus in yield (CPY): 0.0025 kg/kg, Lower limit of HI (WSYF): 0.01, Pest Factor or fraction of yield remaining after insect, weeds, and disease damage (PST): 0.6, Nitrogen fraction at emergence (BN1): 0.044, Phosphorus fraction at emergence (BP1): 0.0062, Phosphorus fraction at 0.5 maturity (BP2): 0.0023, Phosphorus fraction at maturity (BP3): 0.0018, Wind erosion factor for standing live biomass (BW1): 0.433, Wind erosion factor for standing dead crop residue (BW2): 0.433, Wind erosion factor for flat residue (BW3): 0.213, Heat Units required for germination (GMHU): 100 °C, and Biomass energy ratio (WA): 40 kg/ha/MJ On clastill.dat were used: Furrow dike height (DKH): 200mm, Irrigation application that is lost to runoff (EFI): 0.15, Depth of fertilizer placement (FDP): 50 mm, Fraction of furrow dike volume available for water storage (FDSF): 0.8, Initial soil water content--fraction of field capacity (FFC): 0.55, Pesticide 79
Williams, 1990b). However, the following parameter’s values were adopted according to local evaluations: harvest index, HI=0.33 (Ortiz et al., 2005); the fraction of water in yield, in this case, commercial moisture WCY=0.14 (Alvarado, 1975; Estrella, 1973); maximum crop height, HMX=2.7m (Ortiz, 1990); and maximum root depth, RDMX=
1.0m (Campos de Jesús, 1982). Additionally, the model was calibrated for nitrogen at half maturity (BN2=0.018) and at entire maturity (BN3=0.0165) based on Grageda (1988) findings. The maximum potential leaf area index (DMLA) was evaluated using
DMLA=0.6169*FPP-0.1207 (Ortiz et al., 2005), around 3.5m2/m2 for a plant population
(FPP) of 6.0 plt/m2.
application efficiency (HE): 0.95, Harvest efficiency (HE): 0.95, Pest control factor (PCF): 0.8, Row cultivator ridge height (RHT): 200 mm, Row planter ridge height (RHT): 170 mm, Furrow dikes ridge height (RHT): 200 mm, Number of years of cultivation before simulation starts (RTN): 100 years, Row cultivator tillage depth (TLD): 25 mm, Mold board plow tillage depth (TLD): 300 mm, Tandem disk tillage depth (TLD): 75 mm, Subsoil tillage depth (TLD): 350 mm, Tillage depth (TLD): 40 mm. 80
4. Validation Process
The goal of model validation is to evaluate model accuracy from the system being modeled in order to simulate scenarios (Rykiel, 1996). The purpose of the validation process in this study was to: 1) analyze the behavior of the plant growth sub-model by means of grain yield, and 2) evaluate the reliability of regional soil, weather, and crop management information that will be used for management strategies. For a spatial evaluation, Moen et al. (1994) assess that the mean crop yield can be considered a result from the average values of climate distribution (temporal and spatial), soil properties, crop management, and seed genetic characteristics. It indicates that the differences on grain yields among areas reproduce the differences on mean natural conditions. Trying to extend Moen’s et al. (1994) criteria, EPIC was validated on a municipio (see footnote 4 on page 17) basis, comparing the simulated and observed average corn yields.
Taking into account that this study evaluates the district’s most susceptible areas for soil erosion; the model was validated on a spatial basis by using regional soil, weather
(synthetic or observed), and crop management strategies. For synthetic weather data were used the climate statistical parameters and the observed records of rainfall and temperature (maximum and minimum) for 2002 and 2003. By using synthetic weather data, this study pretends to validate the EPIC climatic generator in order to be used for long-term (a hundred of years) evaluation of BMPs.
In order to validate EPIC, using means of grain yields, it was necessary to identify the agriculture areas where corn usually is cultivated. It was also necessary to identify the soil types, in the agriculture area, and their physical and chemical properties. Moreover, it 81 was necessary to identify the meteorological stations around the district and those stations with data availability for 2002 and 2003. Finally all this information and the crop management data were handled in Hydrological Response Units (HRU) as follows: a) Areas of Corn Production
The NASA’s satellite Landsat-ETM images (30 m resolution), scenes 2646 and
2647 for May 30th of 2002, were used to identify the areas where corn grows into the district. The agricultural pixels were analyzed with IDRISI Andes35 based on non- supervised and supervised classification methods. The unsupervised classification was achieved, with a hard classifier (ISOCLUST), by clustering the following compositions: false color (bands 2, 3, and 4), Tasscap36, NDVI37, SAVI38 and PCA39. This clustering identified the ten most representative (94%) land-use/covers in the district. These ten classes were used to project the training sites that were captured from a GPS survey and the INEGI’s 2.0 m resolution digital orthophotos (e14b11, e14b22, e14b32, and e14b41).
The training sites information was used, in the supervised classification, to create
(MAKESIG), edit (EDITSIG), and check the spectral signature distinctness (SIGCOMP)
35 GIS software developed by the Graduate School of Geography at Clark University. http://www.clarklabs.org 36 TASSCAP (tasselled cap) is a vegetation analysis that generates three images; known as greenness, brightness, and moistness images. The brightness image refers to soil brightness, the greenness (or Green Vegetation Index – GVI) refers to green vegetation cover, and the moistness image refers to soil moisture. 37 NDVI is the Normalized Difference Vegetation Index. It assumes that live green plants absorb solar radiation in the photosynthetically active radiation (PAR) spectral region. This index is evaluated using the light’s red (band 3) and near-infrared (band 4) spectral regions (Jensen, 1996). 38 SAVI: Soil-adjusted Vegetation Index. This index also is evaluated using the red and near-infrared bands but it reduces soil-brightness variation (Jensen, 1996). 39 PCA: Principal Components Analysis is a statistical technique that transforms the original grid layers into a new set of layers such that the greatest variance of any layer of the data comes to lie on the “first principal component”, the second greatest variance on the “second principal component”, and so on. PCA will be used to reduce the original layers data by keeping lower-order principal components that often contain the "most important" aspects of the data (Jensen, 1996). 82 of each land-use/cover. For the supervised classification the maximum-likelihood
(MAXLIKE), the minimum-distance to means (MINDIST), and the k-nearest neighbor
(KNN) classifiers (Figure 8) were used. From 206 GPS points, independent from the training sites, an accuracy of 87% between the observed and supervise-classified pixels
(Figure 9) was evaluated. For the purposes of this study (identify the potential areas of corn production) this accuracy was considered adequate, if the mean size of corn plots
(1.85 ha)40, the image resolution (30 m), and the high variation of plant cover between the neighboring pixels (mostly by green fences) are taken in consideration. The polygons of irrigated areas, shown in Figure 13, were digitized from SAGARPA’s district records.
For the purposes of this study, it was concluded that corn potentially is cultivated in
69,720 ha, i.e. 27% of the districts area (Figure 13) because the farmers sometimes rotate corn by sowing wheat, oats, barley or alfalfa.
40 Census of the Mexican rural support program (PROCAMPO) http://www.aserca.gob.mx/artman/publish/article_1424.asp NON SUPERVISED CLASIFICATION Bands in GeoTIFF format COMPOSIT (False Color) (radiometrically and TASSCAP 1 geometrically 2 corrected) 3 VEGINDEX (NDVI, SAVI) 4 5
ISOCLUST Principal Component CP1 Analysis (PCA) CP2 Visual Analysis: CP3 Ten classes identification
SUPERVISED CLASIFICATION Spectral Signatures •Six Training sites per class. MAKESIG •Three areas per p EDITSIG 490000 500000 510000 520000 530000 540000
Coacalco 21700002170000 21700002170000 site Chiautla Ecatepec Tepetlaoxtoc AtencoChiconcuac
SIGCOMP 2160000 2160000 2160000 2160000 EXTRACT Texc oco
2150000 2150000 Chimalhuacan 2150000 2150000 Netzahuacoyotl Chicoloapan
La paz Ixtapaluca Name ID Annual 2140000 2140000 2140000 2140000 Chalco 2130000 2130000 2130000 2130000
Cocotitlan Tlalmanalco Crop Areas Temamatla Crops 1 2120000 2120000 Tenango del Air 2120000 2120000 MINDIST: 63% (Hard) Ayapango Amecameca Juchitepec 21100002110000 21100002110000 Ozumba Atlautla Tepetlixpa Forest 2 2100000 2100000 2100000 2100000 MAXLIKE: 74% (Hard) Ecatzingo 490000 500000 510000 520000 530000 540000 water 3 KNN: 83% (Soft) . . 490000 500000 510000 520000 530000 540000 . . Cover Area 21700002170000 21700002170000 21600002160000 21600002160000
. . Final accuracy Agriculture ---- 21500002150000 21500002150000
RECLASS 21400002140000 21400002140000 Urban 10 Forest --- COUNT assessment 21300002130000 21300002130000 HISTO 87 % Water ---- 21200002120000 21200002120000 etc ---- 2110000 2110000 2110000 2110000 21000002100000 21000002100000
490000 500000 510000 520000 530000 540000
Figure 8. Classification procedure, for corn detection pixels, using Landsat-ETM image (adapted from Tapia, 1999). 83 84
490000 500000 510000 520000 530000 540000 2170000 2170000 2160000 2160000 2150000 2150000 2140000 2140000 2130000 2130000 2120000 2120000 2110000 2110000 2100000 2100000
490000 500000 510000 520000 530000 540000
Legend Meters 07,500 15,000 30,000 Agricultural Grassland Alfalfa fields Reforestation 1:400,000 Erosion Reservoir Forest Urban Projected Coordinate System: UTM, Zone 14N Datum: North American 1927 Forest scarce Wet soil 4 Source: Landsat-7 ETM+ 2646-47, May/30/2002 Figure 9. Map of land-use/cover for the Texcoco district. 85
b) Soil Data The soil types polygons were digitized from the INEGI’s soil maps41 at scale
1:50,000 according to the soil units (Figure 10) and phases42 (Figure 11) of the FAO’s soil classification system (FAO-UNESCO, 1988). From the overlay between soil’s units and phases the soil map for the district was derived (Figure 12). The soil physical and chemical properties43 for this soil map were obtained from 1109 sites surveyed by
SAGARPA in 1994 by means of a lattice of 1 km that covers the district’s agriculture area (Figure 11). In this lattice the mean values of SAN, SIL, WN, PH, CAC, OM44 and
AP (Bray-Olsen) for the top 0.3 m of the soil profile were recorded; where dry matter accounts between 64-69% of corn roots (Rhoads and Bennett, 1990). The soil’s physical and chemical properties (Z, SAN, SIL, WN, PH, SMB, CBN, CAC, CEC, ROK, WNO3, and O) deeper than 0.3m were assessed from 271 soil profiles (Figure 10) reported in the
INEGI’s soil maps for the district. The soil profiles shown in the Appendix B1 represent the mean extracted values into the boundaries of each soil type. Parameters like BD, U,
FC, SC and BDD were determined based on texture (Saxton et al., 1986). The soil albedo
41 INEGI’s edaphic mapas: E14B11, Tizayuca; E14B21, Texcoco; E14B31, Chalco; E14B41, Amecameca; E14B51, Cuautla; E14A19, Zumpango de Ocampo; E14A29, Cuautitlán; E14A39, Ciudad de México; E14A49, Milpa Alta; E14B12, Ciudad Sahagún; E14B22, Apan; E14B32, Mariano Arista; and E14B42, Huejotzingo. 42 Soil’s suitability (such as saline, lithic, stony, duripan, etc) referred to three soil depth (0-0.5m, 0.5-1.0m, and >1.0 m). 43 Soil albedo (SALB), depth from the surface to the bottom of the soil layer (Z), bulk density of the soil layer (BD), wilting point (1500 kPa) (U), field capacity (33 kPa) (FC), sand content (SAN), silt content (SIL), organic N concentration (WN), soil pH (PH), sum of bases (SMB), organic carbon (CBN), calcium carbonate (CAC), cation exchange capacity (CEC), coarse fragment content (ROK), initial Nitrate concentration (WNO3), labile P concentration (AP), crop residue (RSD), bulk density (oven dry) (BDD), and saturated conductivity (SC). 44 Soil organic matter (OM) is around 58% carbon by weight. This carbon that is part of soil OM is called organic carbon (CBN). Actually, there is no easy way to measure OM so soil laboratories measure the CBN and divide by 58% to asses the amount soil OM. 86
(SALB45) values were assessed based on OM and soil’s texture (Baumer, 1990; Jones and Kiniry, 1986). Table 8 shows the soil types in the district’s agriculture areas that were covered to validate EPIC, analyze the current management (CM), and the soil conservation scenarios.
45 SALB = 0.6 /EXP (0.4 %OM) (Baumer, 1990) 87
490000 500000 510000 520000 530000 540000
2938 2131 2129 2134 2132 2941 2136 2135 2944 2943 2139 2945 2142 2948 2144 2957
2170000 2959 2170000 2155 2154 2955 2960 2157 2159 2160 2165 2164 2968 2166 2163 2168 2973 2972 2169 2977 2173 21722174 2177 2176 2180 2179 2182 2984 2185 2160000 2190 2189 2160000 311 2191 315 314 317 313 318 3113 3112 3116 3117 3118
2150000 3119 2150000 3122
3125 3127 3130 3128 3131 3133 3134 3135 3140 2140000 2140000 3152 3154 3160 3161 3162 3164 31633165 3168 3166 3173 3175 421 413 2130000 414 2130000 417 418 4111 419 4114 4116 4118 4121 4126 4123 4131 4135 2120000 4137 4140 41384139 2120000 4143 4146 4152 4244
41574158 4165 41634164 2110000 2110000
41714173 4172
4176 4177 4180 4181 514 513 519 2100000 2100000
5111 5114
Legend 490000 500000 510000 520000 530000 540000
INEGI's Soil Survey Dystric Regosol Lithosol Meters Soil Units Eutric Cambisol Mollic Andosol 05,000 10,000 20,000 Calcaric Feozem Eutric Fluvisol Mollic Gleysol Calcaric Regosol Eutric Regosol Mollic Solonchak 1:400,000 4 Calcic Cambisol Gleyic Solonchak Ochric Andosol Projected Coordinate System: UTM, Zone 14N Chromic Vertisol Haplic Feozem Orthic Solonchak Datum: North American 1927 Dystric Cambisol Humic Andosol Pellic Vertisol Source: INEGI Dystric Fluvisol Humic Cambisol Vertic Cambisol Figure 10. Map of FAO’s Soil Units for the Texcoco district. 88
490000 500000 510000 520000 530000 540000 E EE EE EEEE E EE E EEE EEEE E EE E E EE
2170000 EEE EE EEE 2170000 EEE E EEE EE EEE EEEEEEEEE EEEE EE E EE E EEEEEEEEE EEEEE EE E EE EEEEE E E EE E EEE EEE E EEEEEEE E EE EEEEEEEEEE E E E EE EEEE EEE E EEEEEEEEEEEEE EEEEEE EEE
2160000 EEEEE EEEEEEEEEEEEEE EEE 2160000 EEEE EEEEEEEEEEEE EE EEE EEEEEEEEEEEEEEEEEEEEEEEEE EEEEE EEEEEEEEEEEE EEEEE EEEEEEEEEEEE EEEEEE EEEEEEEE EEEEEEEEEEE EEE EEEEEEEEEE E E EEEEEEEE E EEE EEEEEEEEEEE
2150000 EEEEEEEEEE 2150000 EE EEEEEEEEE EEE EEEEEEEEE EE EEEEEEEEE E EEEEEEEEEE E E EEEEEEEE EEEEEE EEEEEEEEEE EEEEEE E E EEEEEE E EE EEEEEEE EE
2140000 EE E EEEEE EEEE EEE 2140000 EEE EEEEEEEEEEE E EEEEEE EE E E EEE EEEEE E EEEEEEEEEEE E EEEEEEEEEEEEEEE EEEEEEEEEEEEEEE EEEEE EEEEEEE EEEEEEEEEEEEEEE EEEE EE EEE E
2130000 EEEEEEEEEEEEEEEEEEE 2130000 EEEEEEEEEE EEEEEEE EEE EEEEE EEEEEEEEE E EEEEEEEEEEEEEEE E EEEEE EEEEEEEEEEEEEEEEE EE EEEE EEEEE EEEE EE EEEEEEEEEE EEEEEEEEEEEE EEEEEE EE EEEE E EEEE E
2120000 EEE EEEE E 2120000 EE EEEEE EEEEEEE E EEEEEEEEE EEEEEEEEEE EEEE EEEEEEEEEEEEEEEE EE EEEEEEE EEEEEEEE EEEE EEEE EEEEEEEEEEEEEEEEEEEE E E EEEEEEEEEEEEEE EEEEE EE EEEEEEEEEEEEEEE E EE EE EEE EEEEEEEEEEEEEEE E EEEEE EEEEEEEEEEEEEEEEEEE
2110000 E EEEEE EEEEEEEEEE EEEEEEE 2110000 E EEEE EEEEEEEEEE EEEE EEEEEEEEEEEEEEEEE EEEEEEEEEEEEEE EEEEE EEEEEE EEE EEEEE EEEEEEEEEEEEEE EEEEEEEEEEEEEE EEEEEEEEEEEE EEEEEEEE EE EE EEEE EEEE
2100000 EEEEEEEEEEEEE EEE 2100000 EEEEE EEEEEEEEEEEE EEEEEEEEEEE EEEE EEE EEEEEE E
490000 500000 510000 520000 530000 540000
Duripan Legend Meters Lithic 05,000 10,000 20,000 E SAGARPA's Soil Survey No phase 4 1:400,000 Soil Phases Petric Projected Coordinate System: UTM, Zone 14N Deep duripan Rock debris Datum: North American 1927 Deep lithic Stony Source: INEGI Figure 11. Map of FAO’s Soil Phases for the Texcoco district. 89
Legend 490000 500000 510000 520000 530000 540000
Soil RD-LP VP-SF HC-SF BD-SF RD-P ZO-SF HC-SFBE-D VP-SF ZM-SF ZM-SF HC-D VP-L BE-D RD-SF HC-SF HH-L HH-L HC-SF I-L I-L HH-D VP-L HH-LVP-D 2170000 2170000 BE-D HH-LP RC-L RE-L I-LRE-L BE-L RE-D BK-D VP-D HH-SF I-LVP-L I-L VP-LP I-L ZO-SF RE-GVP-LP I-L I-L BE-L BE-LP RE-DP HC-L BE-SF I-L HH-L BE-SFI-L I-L I-L VP-D HH-SF VP-LP HH-LVP-LP BE-P RE-G HC-L RE-LHH-D HH-G VP-D RE-LI-L RE-L BE-SF RE-L HC-L RE-L RE-L VC-L HH-SF HH-D I-L VP-D HH-L VC-SF RC-L I-L TH-SF I-LHH-D ZO-SFI-L BH-G RE-P HH-D RE-LHH-L HH-LVC-SF HH-SF ZO-SF ZO-SF BH-L HH-D VP-SF RE-L RE-L BH-L HH-D ZM-SF I-L I-L I-L 2160000 RE-SF 2160000 ZG-D VP-L I-L RE-L BH-L BH-SF HH-L TH-SF TH-G HH-D RE-LVP-L BH-SF BE-D BK-D TH-L HH-DBE-D TH-SF HC-SF HH-DP BH-SF HH-DPBE-SF HH-L TH-SF BV-SF TH-P I-LHH-D I-LI-LBE-D GM-SF TH-SF HH-D I-L ZG-SF HH-DPBE-SF BD-SFTM-SF HC-D TM-L HH-SF HH-LHH-D 2150000 2150000 HH-D RE-SF HC-L TM-SF HH-SFHH-L BE-LP I-L HC-L HC-SF HC-D BE-LP BH-SF HC-SF TO-G ZM-SF RE-SF HH-L I-L I-L RE-SF TH-SF ZG-L HH-DP BH-SF HH-D TO-L I-L HH-L I-L I-L HH-LHH-D BH-SF HH-D BH-SF HH-D I-L HH-DP TO-LP HH-D I-LHH-L I-L HH-DRE-LHH-L HH-SF HH-DPHH-DPHH-D TO-LP HH-G TO-P HH-SF BV-SFI-L HH-DP RE-L RE-D TH-SF TO-SF RE-L HH-D HH-DHH-L HH-D TH-SF 2140000 2140000 RE-G HH-L TO-SF RE-DPHH-D BD-SFHH-L RE-L HH-D TO-P HH-D HH-D TH-SF BD-SF TH-SF HH-LP VC-D HH-SF VP-L RE-D BD-SF HH-P VC-L HH-D TH-L HH-L HH-DHH-SF I-L BD-SF ZG-SF VC-D TH-SF HH-SF VC-SF ZM-SF RE-DHH-SF BD-SF HH-SF HH-D I-L BE-SF I-L VP-D RE-SF HH-DPI-L I-L HH-D I-L I-L 2130000 JD-G VP-L 2130000 GM-SF JE-G TO-SF RE-G JD-LP VP-LP TO-SF TO-SF TH-SF HH-P I-L TH-G I-L JD-SF VP-SF HH-SF HH-G TH-GRE-G TH-P JE-P I-L JE-G ZG-D HH-P HH-P I-L TH-G TH-G HH-P RE-G TH-P RE-G JE-P ZG-L I-L TH-PRE-G TH-P HH-P TH-SF JE-SF ZG-SF I-L I-L 2120000 2120000 TH-P TH-PTH-P RE-G RC-L ZM-SF TH-P I-L HH-G HH-GTH-P TH-P JD-G RD-G ZO-SF RE-SF I-LI-L I-L TH-L RE-SF I-L HH-P TH-P TH-SF TH-P JD-LP TH-P TH-P RE-G RE-SF RE-P I-L TH-GRE-P RE-P RE-D I-L TH-G TH-P TH-SFRE-P TH-G RE-P JD-LP RD-G I-L BH-G RD-P I-L I-L RD-G TH-P RE-P 2110000 2110000 TH-P I-L HH-SF TH-P HH-SF RE-SF RE-P HH-L TH-G RD-GRD-G TH-PJE-SF I-LTO-SF HH-SF TH-PTH-P TH-P RE-GRD-SF RD-G RE-P I-L TM-L JD-SFTO-L RD-GJD-G TH-P TO-LTO-G TH-L BE-P TO-P TH-L 2100000 2100000 TO-P TH-P
BE-P TO-GTO-P TO-G TO-P TO-L
490000 500000 510000 520000 530000 540000
Meters 07,500 15,000 30,000
1:400,000 4 Projected Coordinate System: UTM, Zone 14N Datum: North American 1927 Source: INEGI's Soil Charts 1:50,000 Figure 12. Map of FAO’s soil types for the Texcoco district (see Table 8 for legend description). Table 8. Soil types for the agriculture area into the Texcoco district. Num Soil FAO's FAO's USLE's SALB CN46 Texture Depth Slope Area Class Soil Unit Soil Phase K47 Group m % % 1 BE-D Eutric Cambisol Duripan 0.34 0.129 B Loam 0.35 11.1 1.8 2 BE-L Eutric Cambisol Lithic 0.25 0.129 C Sandy clay loam 0.35 19.6 0.1 3 BE-P Eutric Cambisol Stony 0.24 0.130 B Sandy loam 0.35 9.7 0.7 4 BE-SF Eutric Cambisol No phase 0.25 0.128 D Clay loam 1.00 0.5 1.5 5 BH-G Humic Cambisol Rock debris 0.24 0.130 B Sandy loam 0.65 7.4 0.3 6 BH-L Humic Cambisol Lithic 0.34 0.129 B Loam 0.35 16.2 0.8 7 BK-D Calcic Cambisol Duripan 0.24 0.131 B Sandy loam 0.65 3.5 0.1 8 GM-SF Mollic Gleysol No phase 0.29 0.108 B Loam 1.00 1.0 1.2 9 HC-DP Calcaric Feozem Deep duripan 0.25 0.132 C Sandy clay loam 0.65 7.2 1.5 10 HC-L Calcaric Feozem Lithic 0.24 0.147 B Sandy loam 0.35 15.9 0.3 11 HC-SF Calcaric Feozem No phase 0.25 0.129 D Clay loam 1.00 0.8 1.8 12 HH-D Haplic Feozem Duripan 0.34 0.131 B Loam 0.35 11.1 3.3 13 HH-DP Haplic Feozem Deep duripan 0.25 0.130 C Sandy clay loam 0.65 6.4 9.6 14 HH-G Haplic Feozem Rock debris 0.24 0.130 B Sandy loam 0.65 3.8 1.4 15 HH-L Haplic Feozem Lithic 0.25 0.129 C Sandy clay loam 0.35 15.0 6.9 16 HH-LP Haplic Feozem Deep lithic 0.25 0.130 C Sandy clay loam 0.65 6.9 2.5 17 HH-SF Haplic Feozem No phase 0.25 0.129 D Clay loam 1.00 2.1 6.8 18 JD-G Dystric Fluvisol Rock debris 0.24 0.133 B Sandy loam 0.65 5.3 4.3 19 JD-LP Dystric Fluvisol Deep lithic 0.24 0.140 B Sandy loam 0.65 3.7 0.5 20 JE-G Eutric Fluvisol Rock debris 0.24 0.131 B Sandy loam 0.65 3.8 0.9 21 JE-P Eutric Fluvisol Stony 0.34 0.130 B Loam 0.35 9.3 0.0 22 JE-SF Eutric Fluvisol No phase 0.34 0.132 B Loam 1.00 2.2 6.3 23 RC-L Calcaric Regosol Lithic 0.25 0.130 C Sandy clay loam 0.35 16.9 0.0 24 RD-G Dystric Regosol Rock debris 0.24 0.131 B Sandy loam 0.65 5.7 7.4 25 RD-P Dystric Regosol Stony 0.24 0.130 B Sandy loam 0.65 7.7 0.1 26 RE-G Eutric Regosol Rock debris 0.34 0.128 B Loam 0.35 9.8 4.8 27 RE-L Eutric Regosol Lithic 0.34 0.127 B Loam 0.35 13.4 1.4 28 RE-LP Eutric Regosol Deep lithic 0.34 0.130 B Loam 0.65 7.3 5.5 29 RE-SF Eutric Regosol No phase 0.24 0.131 B Sandy loam 1.00 2.7 1.7 30 TH-L Humic Andosol Lithic 0.34 0.129 B Loam 0.35 14.7 0.2
46
Hydrologic Soil Groups based on texture (Wanielista, 1990) reported on Appendix B3. 90 47 K: USLE’s Soil erodibility factor (Estrada-Berg Wolf, 1988) Table 8. Soil types for the agriculture area into the Texcoco district — Continued. Num Soil FAO's FAO's USLE's SALB CN Texture Depth Slope Area Class Soil Unit Soil Phase K Group m % % 31 TH-LP Humic Andosol Deep lithic 0.24 0.130 B Sandy loam 0.65 5.0 0.4 32 TH-P Humic Andosol Stony 0.24 0.130 B Sandy loam 0.35 11.3 4.0 33 TO-L Ochric Andosol Lithic 0.34 0.130 B Loam 0.35 11.8 1.2 34 TO-LP Ochric Andosol Deep lithic 0.34 0.130 B Loam 0.65 5.5 0.0 35 VC-SF Chromic Vertisol No phase 0.25 0.127 D Clay loam 1.00 0.9 6.6 36 VP-D Pellic Vertisol Duripan 0.25 0.130 C Sandy clay loam 0.35 8.0 1.8 37 VP-L Pellic Vertisol Lithic 0.25 0.131 D Clay loam 0.35 13.4 0.7 38 VP-LP Pellic Vertisol Deep lithic 0.25 0.128 C Sandy clay loam 0.65 6.3 0.1 39 VP-SF Pellic Vertisol No phase 0.25 0.129 D Clay loam 1.00 3.0 4.2 40 ZG-SF Gleyic Solonchak No phase 0.25 0.127 D Clay loam 1.00 0.6 4.5 41 ZM-SF Mollic Solonchak No phase 0.25 0.131 D Clay loam 1.00 0.6 1.1 42 ZO-SF Orthic Solonchak No phase 0.25 0.132 D Clay loam 1.00 0.9 1.7 91 92
c) Hydrological Response Units (HRU)
In order to validate the model on a regional basis, the district’s agriculture area was divided in 255 HRU (Figure 18 and Appendix B4). These units were obtained from an overlay between the soil map (Figure 12) and the water supply conditions (irrigation and rainfed plots) where corn usually grows (Figure 13). Base on the fact that the same soil type potentially can be spread along the district, for HRU identification, the soil map was grouped into continuous polygons in order to have compact units. For each HRU,
EPIC was run with the extracted mean values of elevation (Figure 14), slope (S), and latitude (YLT), the previously calibrated corn parameters (under conventional till) and the crop’s current management (Table 11). 93
490000 500000 510000 520000 530000 540000
Coacalco 2170000 2170000
Chiautla Ecatepec Tepetlaoxtoc Chiconcuac Atenco Papalotla 2160000 2160000
Tex c oc o
2150000 Chimalhuacan 2150000 Netzahuacoyotl Chicoloapan
La paz Ixtapaluca 2140000 2140000
Chalco 2130000 2130000
Cocotitlan Tlalmanalco Tem am at la
2120000 Tenango del Air 2120000
Ayapango Amecameca
Juchitepec 2110000 2110000 Ozumba Atlautla
Tep etl ix pa
2100000 Ecatzingo 2100000
490000 500000 510000 520000 530000 540000 Legend Meters 07,500 15,000 30,000 Te r ra c es
Municipios Projected Coordinate System: UTM, Zone 14N 4 Datum: North American 1927 Irrigation Irrigation Plots: DDR Texcoco, SAGARPA Rain fed plots Terrace Area: Comisión del Lago de Texcoco, CNA. Figure 13. Irrigated, rainfed, and terraced plots into Texcoco’s district. 94
490000 500000 510000 520000 530000
PATLACHICO VALLE DE MÉXICO San Juan Teotihuacán MEXQUIPAYAC
2170000 METECÁTL 2170000 TRES PADRES TEZONTLALE SIERRA DE GUADALUPE CARACOL, EL GRAN CANAL DEL DESAGÜE PAPALOTES TEXCOCO SALES, LAS JALAPANGO Papalotla COXCAOAL Xalapango SAN BARTOLO C. I. DEL MEJORMTO. DEL MAÍZ Y EL TRIGO
2160000 SALES, LAS 2160000 TEXCOCO NORTE HUEYAPA RÍO DE LOS REMEDIOS CHAPINGO HORARIA SAN BERNARDINO Coxcacoaco NABOR CARRILLO BOSQUE DE ARAGÓN ChapingoTe xc o co CHIMALHUACÁN DOSSan Bernardino ZOQUIAPAN BENITO JUÁREZLower Area (< 2260 msmm)
2150000 XOCHIACA 2150000 Santa Mónica MIRADOR, EL QUETZALTEPEC CHURUBUSCO ARROYO GRANDE Coatepec TELAPON
San Francisco 2140000 2140000 XALTEPEC SAN FRANCISCO SANTA CRUZ SANTO DOMINGO SAN FRANCISCO CANAL GENERAL SAN RAFAEL TEJA, LA GUAJOLOTE, EL La Compañía 2130000 2130000 AMECAMECA TEUHTLI CUESTA, LA CABEZA DEL NEGRO
IZTACCÍHUATL AYAQUEME
2120000 AMILPULCO 2120000 Amecameca
GABRIEL RAMOS MILLÁN TLALOC CILCUAYO
C.I.C.I.T.E.C SISTEMA ALFREDO DEL MAZO
2110000 OCLAYUCA TLAMACAS 2110000 HUIPILO 2100000 2100000
490000 500000 510000 520000 530000 Legend Topographic Landmarks District Boundary Meters 05,000 10,000 20,000 Streams Sub_basins_UTM CANAL Topography 1:400,000 Stream Intermittent High : 5465 4 Stream Perenne Artificial Saline Projected Coordinate System: UTM, Zone 14N Datum: North American 1927 Reservoirs Low : 1297 Figure 14. Topographic features of the Texcoco District and the hydrological network for the basin of Mexico. Source: INEGI’s (3” arc) digital elevation model. 95
d) Climate Data
The validation was run on a municipio basis by using 1) EPIC’s climatic
generator (based on monthly statistical parameter of precipitation and temperatura) to
evaluate historical mean of corn yields and 2) daily weather (temperature and rainfal)
records for the corresponding 2002 and 2003 corn yields.
Average Climatic Values
The validation of the historical mean of corn yields and the soil conservation
management scenarios (analyzed further on) were run with the mean monthly climatic
parameters or seeds48 (Appendix B1) interpolated (IDW49) and extracted over each HRU.
These climatic parameters were calculated through WXPARM based on the historical
daily records of precipitation and temperature (Appendix B1). For the 72 meteorological
stations in the region (Figure 15 and Table 10) such records were taken from the ERIC50 data base. The values of OBMX and OBMN were evaluated, for each HRU, from the following linear function (spline interpolation) that relates location (latitude and longitude in degrees) and elevation in meters (Table 9).
Tm C C1 lat C2 long C3 elev (35)
48 The average monthly maximum air temperature (OBMX), the average monthly minimum air temperature (OBMN), the monthly standard deviation maximum air temperature (SDTMX), the monthly standard deviation minimum air temperature(SDTMN), the average monthly precipitation (SMY), the monthly standard deviation of daily precipitation (RST2), the monthly skew coefficient for daily precipitation (RST3), the monthly probability of wet day after dry day (PRW1), the monthly probability of wet day after wet day (PRW2), and the average number of days of rain per month (DAYP). 49 Inverse distance weighting (IDW) method with a squared power factor (the most common one). This method combines the effect of proximity with a gradual change of trend surfaces (Burrough and McDonnell, 1998) 50 Spanish acronym for an electronic database of meteorological records (ERIC) compiled by the Mexican Institute of Water Technology (IMTA). http://www.imta.mx/productos/software/meteorologia.html 96
where Tm is the maximum or minimum temperature (°C); C is a constant; C1 is the
latitude coefficient; C2 is the longitude coefficient; and C3 is the elevation coefficient
(Table 9). All the functions show a coefficient of determination (r2) between moderately
strong and very strong (Table 16).
In order to evaluate evapotranspiration, for 60 stations (out of 72), were captured from the SMN’s hard copies, the wind data required to asses the average monthly speed
(WVL) (see Appendix B2). Table 9. Regression coefficients to evaluate the average monthly maximum (OBMX) and minimum (OBMN) air temperature in Texcoco District. Maximum air temperature (OBMX) Minimum air temperature (OBMN) Month 2 2 C C1 C2 C3 r C C1 C2 C3 r January -1.000 1.6560 -0.0762 -0.00742 0.83 -23.569 -6.3624 -1.5702 -0.00222 0.49 February -85.302 2.4117 -0.8139 -0.00824 0.85 -129.225 -6.0218 -2.5801 -0.00217 0.53 March -135.208 3.4119 -1.1511 -0.00845 0.87 -276.511 -4.6918 -3.8365 -0.00244 0.52 April -86.542 2.6332 -0.8349 -0.00892 0.88 -267.811 -4.0660 -3.6634 -0.00319 0.59 May -84.228 3.1004 -0.7302 -0.00939 0.89 -227.565 -3.0764 -3.0992 -0.00410 0.70 June -167.860 3.1027 -1.5437 -0.00897 0.85 -316.958 -1.9704 -3.7987 -0.00429 0.77 July -181.857 3.2576 -1.6217 -0.00815 0.83 -361.144 -1.5906 -4.1633 -0.00420 0.82 August -124.593 3.0511 -1.0936 -0.00852 0.83 -394.604 -1.8102 -4.5417 -0.00411 0.81 September -71.577 2.9028 -0.5746 -0.00820 0.82 -345.050 -2.2322 -4.1238 -0.00416 0.80 October -85.070 2.6386 -0.7610 -0.00808 0.81 -310.304 -3.5534 -3.9999 -0.00364 0.71 November 19.954 2.1194 0.1984 -0.00826 0.85 -170.747 -5.6815 -2.9557 -0.00260 0.57 December 84.100 1.4073 0.7245 -0.00789 0.81 -76.619 -6.3244 -2.1101 -0.00229 0.55 97 98
480000 490000 500000 510000 520000 530000 540000
CUAUTITLÁNPTULTEPEC OZUMBILLA TEOTIHUACÁN SANTIAGO TEOYAHUALCO TULTITLÁN VILLA DE LAS FLORES 15091 CHICONAUTLA P TEXCOCOP15041 SAN MATEO CUAUTEPEC VENTAP DE CARPIO TEPEXPAN P15135
2170000 ECATEPEC P15124 2170000 QUEBRADA, LA P29006 29019 P15044 TEPETLAOXTOC P 15040 ACUEXCOMAC 29013 P ATENCO P TLALNEPANTLA PP15138 P9017 29028 SAN MIGUEL PTOCUILA 15163 P15210 P
2160000 P15092 TEXCOCO 2160000 P P15101 P15125 P9029 P HUEXOTLA P9062 LOMAS DE CRISTO PP9043 15145 P COATLINCHAN 15167 9013 P
2150000 MÉXICO CITYP CHIMALHUACÁN 2150000 P9068 P9012 P9009 CIUDAD NEZAHUALCÓYOTL 15017 P9011 15050 P PREYES ACAQUILAPAN, LOS 9026 9070 P P FRACCIONAMIENTO ACOZACACOZAC P15082 SAN FRANCISCO ACUAUTLA 2140000 2140000 P TLALPIZAHUAC 15018 P 15141 AYOTLA P P9014 IXTAPALUCA P9034 XICO P9051 9042 CHALCO SANTA CRUZP ACALPIXCA P15020 2130000 SAN JUAN IXTAYOPAN 2130000 COCOTITLÁN MIXQUIC SAN ANTONIO TECOMITL SAN PABLO ATLAZALPAN 15106 SAN MIGUEL TOPILEJOSAN PEDRO ATOCPAN TEMAMATLA TLALMANALCO PUEBLOP NUEVO MILPA ALTA P SAN PABLO OZTOTEPEC PP9058 15280 P9045 P15094 TENANGO DEL AIRE 2120000 2120000 15007 PAMECAMECA P15080 JUCHITEPEC P15039 P15103 2110000 2110000 P17021 P15252 17039 P P17066
P17051 2100000 P15060 2100000 P15288 P17045
480000 490000 500000 510000 520000 530000 540000 Roads Legend Arterial Street Meters P Weather Stations 05,000 10,000 20,000 Road Unpaved Road District Boundary
Major Road Urban Areas Tol l Ro a d 4 Projected Coordinate System: UTM, Zone 14N Datum: North American 1927 Railroad Figure 15. Meteorological stations around Texcoco’s district. Source: SMN. Table 10. Meteorological stations used on model validation and soil conservation scenarios. Elevati Latitude Longitude First Last Rainfall CODE Name State on (degree) (degree) Record Record (mm) (m) 9009 Col. Agrícola Oriental D.F. 19.3976 -99.0733 01/1961 08/1988 2212 548.1 9011 Col. Del Valle (SMN) D.F. 19.3818 -99.1647 01/1949 12/1973 2234 660.7 9012 Col. Escandón D.F. 19.4039 -99.1781 01/1951 09/1988 2243 755.7 9013 Col. Moctezuma (SMN) D.F. 19.4333 -99.1000 11/1966 10/1988 2211 677.2 9043 Col. San Juan De Aragón D.F. 19.4667 -99.0667 06/1953 12/2006 2206 585.8 9014 Col. Santa Ursula Coapa, Coyoacán D.F. 19.3063 -99.1395 01/1971 10/2005 2239 7906 9070 Coyoacán I.N.I.F. D.F. 19.3528 -99.1714 01/1976 09/2006 2250 807.5 9017 Cuautepec Barrio Bajo D.F. 19.5373 -99.1529 11/1970 04/1988 2246 623.2 9029 Km. 6+250 Gran Canal D.F. 19.4898 -99.0862 01/1952 01/2006 2224 589.3 9032 Milpa Alta, Milpa Alta D.F. 19.1925 -99.0257 10/1929 09/2006 2438 680.9 9026 Mor. 77, Sn. Pablo Barrio, Ixtap. D.F. 19.3588 -99.0871 07/1955 12/1996 2218 601.1 9034 Moyoguarda, Xochimilco D.F. 19.2833 -99.1000 01/1921 10/1988 2223 800.6 9068 Puente de La Llave, Pantitlan D.F. 19.4202 -99.0600 05/1976 10/2006 2210 518.8 9042 Sn Gregorio Atlapulco, Xochimilco D.F. 19.2530 -99.0571 05/1961 12/1983 2248 754.7 9045 Santa Ana Tlacotenco, Milpa Alta D.F. 19.1758 -98.-9962 01/1969 12/2005 2600 709.6 9051 Tlahuac (Xochimilco) D.F. 19.2616 -99.1037 01/1961 09/2006 2233 603.2 9062 Vencedora 144, Industrial D.F. 19.4776 -99.1258 01/1961 04/1975 2227 641.5 9058 Vertedor, Milpa Alta D.F. 19.1925 -99.0171 01/1969 12/1985 2405 702.0 15007 Amecameca De Juárez, Amecameca México 19.1277 -98.7672 03/1969 09/2006 2479 949.9 15138 Atenco (CFE), Atenco México 19.5500 -98.9167 03/1969 02/1984 2234 548.8 15008 Atenco (DGE), Atenco México 19.5440 -98.9119 01/1961 12/2006 2236 592.6 15252 Atlautla E9, Atlautla México 19.0269 -98.7809 07/1978 09/2006 2343 808.0 15080 Repetidora de T. V., Amecameca México 19.1201 -98.6539 01/1961 08/1987 4000 883.5 15145 Plan Lago de Tex. (Campamento) México 19.4627 -99.0205 01/1969 12/2006 2204 530.5 15020 Chalco, Chalco México 19.2589 -98.8971 01/1961 12/2005 2243 627.9 15170 Chapingo, Texcoco México 19.4933 -98.8880 01/1952 05/2006 2247 597.7 15000 Montecillo, Texcoco (CP) México 19.4668 -98.9000 07/1982 12/2006 2219 554.7 15022 Chiconautla, Ecatepec México 19.6333 -99.0000 03/1963 12/2005 2248 589.0 15017 Coatepec de Los Olivos, Ixtapaluca México 19.3837 -98.8468 01/1961 12/2005 2403 649.4 15018 Col. Avlla Camacho, Ixtapaluca México 19.3238 -98.7612 01/1961 12/2005 2900 766.8 15141 E.T.A. 32, Tlalpitzahuac, Ixtapaluca México 19.3311 -98.9595 05/1971 09/1998 2279 604.8 15288 Ecatzingo E8, Ecatzingo México 18.9560 -98.7527 01/1970 12/1987 2387 1014.7 99 Table 10. Meteorological stations used on model validation and soil conservation scenarios — Continued. Elevati Latitude Longitude First Last Rainfall CODE Name State on (degree) (degree) Record Record (mm) (m) 15167 El Tejocote, Texcoco México 19.4405 -98.9095 01/1961 11/1996 2236 554.6 15039 Juchitepec, Juchitepec México 19.1005 -98.8787 01/1969 12/2005 2306 752.2 15040 Km. 2+120 (Bombas), Ecatepec México 19.5635 -99.0126 05/1966 12/2005 2215 568.4 15041 Km 27+250 Gran Canal, Ecatepec México 19.6403 -99.0534 01/1961 10/2005 2224 626.5 15044 La Grande, Atenco México 19.5779 -98.9175 01/1961 12/2006 2241 6061 15050 Los Reyes, La Paz México 19.3667 -98.-9833 01/1961 12/2005 2239 556.2 15060 Nepantla, Tepetixtla (SMN) México 18.9787 -98.8400 01/1961 08/1988 1980 895.7 15065 Otumba, Otumba México 19.6989 -98.7569 01/1961 12/2005 2355 523.0 15082 Rio Frio, Ixtapaluca México 19.3508 -98.6687 01/1961 01/1988 2977 975.2 15083 San Andres Riva Palacio, Texcoco México 19.5311 -98.9095 01/1961 10/2005 2237 563.0 15092 San Juan Ixhuatepec, Tlalnepantla México 19.5207 -99.1061 01/1961 11/1990 2247 617.5 15210 San Juan Totolapan, Tepetlaoxtoc México 19.5293 -98.7264 01/1976 12/2005 2750 547.3 15094 San Luis Ameca, Tenango Del Aire México 19.1806 -98.8576 01/1961 12/2006 2400 674.8 15097 San M. De Las Pirámides, S. M. Pira. México 19.7000 -98.8333 01/1937 05/1988 2298 610.3 15101 San Miguel Tlaixpan, Texcoco México 19.5121 -98.8113 06/1969 12/2006 2400 613.6 15103 San Pedro Nexapa, Amecameca México 19.0826 -98.7352 02/1961 08/2006 2638 896.5 15106 San Rafael, Tlalmanalco México 19.2092 -98.7546 01/1961 12/2005 2572 1067.7 15090 Sn. Jerónimo Xonacahuacan, Tecam México 19.7436 -98.9519 01/1961 12/2005 2255 612.4 15091 San José De Las Presas, Otumba México 19.6534 -98.7073 02/1969 04/1977 2657 674.7 15124 Tepexpan, Acolman México 19.6133 -98.9364 01/1961 12/2002 2250 604.8 15125 Texcoco, Texcoco (DGE) México 19.5000 -98.8833 01/1961 11/2006 2246 584.5 15163 Texcoco, Texcoco (SMN) México 19.5175 -98.8818 01/1961 06/1976 2251 684.5 15280 Tlalmananco, Tlalmananco México 19.2029 -98.8027 05/1981 12/2005 2389 711.3 15129 Tultepec, Tultepec México 19.6833 -99.1333 01/1961 10/2006 2238 641.6 15135 Xochihuacan, Otumba México 19.6233 -98.6746 01/1969 08/2006 2759 628.6 17066 El Vigía, Tlalnepantla Morelos 19.0106 -98.9594 05/1981 12/2005 2143 1110.0 17045 Huecauaxco E7, Ocuituco Morelos 18.9360 -98.7831 01/1976 08/2003 2091 1018.1 17039 San Juan Tlacotenco Morelos 19.0167 -99.0926 01/1975 05/2006 2352 1541.1 17021 Tlacualera, Tlacualera Morelos 19.0500 -98.9500 02/1962 08/2003 2616 888.0 17051 Totoloapan E10, Totoloapan Morelos 18.9841 -98.9215 01/1976 12/2005 1871 854.2 17043 E.T.A. 118, Yecapixtla Morelos 18.8833 -98.8667 01/1976 10/2006 1571 984.2 21193 San Pedro B. Juárez E1 Puebla 18.9461 -98.5532 01/1982 12/1990 2310 866.8 100 Table 10. Meteorological stations used on model validation and soil conservation scenarios — Continued. Elevati Latitude Longitude First Last Rainfall CODE Name State on (degree) (degree) Record Record (mm) (m) 21096 Santa Rita Tlahuapan Puebla 19.3288 -98.5809 01/1963 04/1987 2630 855.1 21196 Tochimilco D1 Puebla 18.8930 -98.5726 01/1982 12/1990 2070 910.5 29006 Cuaula, Calpulalpan Tlaxcala 19.6002 -98.6505 07/1966 06/1992 2660 650.2 29039 Esc. Agrop. Nanacamilpa Tlaxcala 19.4923 -98.5355 05/1974 12/1999 2720 769. 29013 La Venta, Calpulalpan Tlaxcala 19.5622 -98.6862 01/1967 05/2005 2807 705.0 29014 Límites, Calpulalpan Tlaxcala 19.5333 -98.5500 06/1967 03/1977 2680 714.0 29019 San Antonio Calpulalpan Tlaxcala 19.5833 -98.6500 01/1969 12/2005 2585 682.8 29028 Sombrerito, Calpulalpan Tlaxcala 19.5333 -98.6500 11/1966 04/1977 2860 804.5 101 102
Daily Weather Events
The model was also validated for the years 2002 and 2003. For this purpose the daily record of temperature (maximum and minimum) and precipitation were used from
21 meteorological51 stations (Figure 16 and Figure 17). These meteorological stations were assigned to each HRU according to their closeness (Appendix B4) that was evaluated by the Thiessen’s polygons (Figure 18)
5. Crop Management Analysis
The crop management investigation was based on 100-years of synthetic weather parameters generated by EPIC’s climatic generator (WXGEN) for each HRU. WXGEN used the statistical parameters shown in the Appendices B1 and B2 for the water stations reported in. This analysis was performed for the current (CM) and recommended management (RM) as follows: a) Current Management (CM)
The crop’s regional or current management (CM) shown in Table 11 was gathered and combined from: Rodríguez (1995), Loza (1985), Tapia, (1985), and Parra (1981).
This Table represents the corn’s management under conventional till (CT), i.e., as farmers prefer.
.
51 Only 21 meteorological stations, out of 72, were recording during 2002 and 2003. 80
70 15007 15008 15017 60 15018 15020 15022 50 15039 15041 15050 40 15094 15101 15106
Rainfall (mm) 30 15124 15135 15170 20 15210 15252 15280 10 17021 17045 17051 0 1/1/02 4/9/02 5/7/02 6/4/02 7/2/02 12/3/02 11/5/02 1/15/02 1/29/02 2/12/02 2/26/02 3/12/02 3/26/02 4/23/02 5/21/02 6/18/02 7/16/02 7/30/02 8/13/02 8/27/02 9/10/02 9/24/02 10/8/02 12/17/02 12/31/02 11/19/02 10/22/02 Date
Figure 16. Daily Rainfall during 2002 for the meteorological stations used for model validation (Table 10). 103 80
70 15007 15008 15017 60 15018 15020 15022 50 15039 15041 15050 40 15094 15101 15106
Rainfall (mm) 30 15124 15135 15170 20 15210 15252 15280 10 17021 17045 17051 0 1/1/03 4/9/03 5/7/03 6/4/03 7/2/03 1/15/03 1/29/03 2/12/03 2/26/03 3/12/03 3/26/03 4/23/03 5/21/03 6/18/03 7/16/03 7/30/03 8/13/03 8/27/03 9/10/03 9/24/03 10/8/03 11/5/03 12/3/03 10/22/03 11/19/03 12/17/03 12/31/03 Date Figure 17. Daily Rainfall during 2003 for the meteorological stations used for model validation (Table 10). 104 105
480000 490000 500000 510000 520000 530000 540000 P! 15129
14 16 15091 E 15E P 17 E ! E 23P15041 22 E E EE ! 15022 30 20 P19 E 2729 28 E 25 18E 21 EE E ! 15135 34 E 36 P E E 32 37 ! 15124 33 E 41 2170000 E E 24 46P 40 E E31 2170000 E E E53 E 44 4556 E4850 E 54 E E E E P29006 52 39 E 55E 47 E E 51 E 3 E 58 E 5 43 E 60 P29019 59 E P15044 E 62 38E61 E E 65 E 57 E 64 E 6770 63 P15040 71 4 E E E P29013 49E 35 69 E E 75 E E E 76 78 P!E15138 E 79E 74E E 1 66 E E 9017 80 83 P15008 E 81 E73 82 P E E 2 E E 72 E !86 29028 42 P 77 E E15210E P E E 15083 E 87 P 2160000 P15092 E 89 2160000 P15163 !85 91 E 90 PE 15101 95 E 92 E E E P! 15125 93 97 ! 9029 7 P15170 99 E E 88 P E E 101 E 9062 E96 6 104 E P EE 108 107E ! 9043 15000 E E EE PPP15145 E94 103 E 98E8 106109 E 110 E 15167 E 9013 P 2150000 P 114 2150000 ! E P9068 115 113 111E 112 116 E E E E 120 9 119 P9012 E E E E P9009 10 124 E 127 122 E E 126130 E E 123 E E ! 15017EE 100 P9011 E PE E 139 E 137 ! 142141E P15050 143 135 EE 145 129147 9026 E EE140E 146 E ! 9070 P E133 136 E E E P 134 P15082 148 E E 132 2140000 E 150 151 E 2140000 157 154 152 E PE 15141 E 138 E E E E 11 144 ! 15018 P E 160E 155P E ! 9014 E P 159 12 165 164 E E E E 168 163 171EE 172 162E 170 E 9034 156 E E E E P E 167E 153 175176 174 E E ! E 179 E 173 P9051 P! 15020E EE 181 P9042 E 2130000 177 169E182 178183 2130000 E E E E180 186 188E199 189 E 187 EE EE E200 E 191192 193 195 E190 E ! 15106 9032 EE E E ! 15280 P 194 202 206PE E P!P9058 207 E E 208 E 205E 209 P! 15094E E P9045 198 204 E
2120000 212 E 2120000 E 213 E 216 E 215 EE 201 E 220 E E E! 217 219E 221 P15007 E E E 15080 224 226 222 P 229EE E E E 231! 15039 223 228EP 211 E 227 E 230E234 E236 E 225232 E E 15103 E 233 P
2110000 235 E 2110000 E 237 E 238 P! 17021 E 244 242 E E 240 245 ! E E E246E P15252 ! 17039 241 243 E 249 P ! E251 E 248 P17066 E E 247 E ! P17051 250 2100000 P25215060 2100000 E E 254 253 E E 255 E P15288 P! 17045
480000 490000 500000 510000 520000 530000 540000
Meters Legend 05,500 11,000 22,000 E HRU's Centroids District Boundary ! Wth Stat 2002-03 Urban Areas 2002-03 Thiessen 4 Projected Coordinate System: UTM, Zone 14N P Weather Stations Datum: North American 1927 Figure 18. Hydrological Response Units (HRU) and meteorological stations (2002 and 2003) used to validate EPIC at district level. Table 11. Corn’s current management (CM) for the Texcoco district. Irrigation Rainfed: ELEV<2350 m Rainfed: 235010% S>10% S< 2% 210% S>10% S< 2% 210% S>10% Leaf area index (DMLA) m2/m2 3.15 2.5 2.0 1.5 2.5 2.0 1.5 2.5 2.0 1.5 Seeding Rate (SDW) kg/ha 25 18 14 11 18 14 11 18 14 11 Plant population (FPP) plt/m2 5.6 4.0 3.2 2.4 4.0 3.2 2.4 4.0 3.2 2.4 Heat Units to harvest (PHU) °C 1300 1300 1300 1300 1300 1300 1300 1300 1300 1300 Plowing (moldboard plow) m/d 2/22 3/13 3/13 3/13 3/13 3/13 3/13 3/13 3/13 3/13 Harrowing52 (tandem disk) m/d 3/30 5/11 5/11 5/11 4/12 4/12 4/12 3/13 3/13 3/13 Furrowing/Row Planting m/d 4/7 5/26 5/26 5/26 4/27 4/27 4/27 3/28 3/28 3/28 Weeding 1st row cult m/d 5/10 7/10 7/10 7/10 6/11 6/11 6/11 5/12 5/12 5/12 2nd row cult m/d 5/25 7/30 7/30 7/30 7/1 7/1 7/1 6/1 6/1 6/1 Total fertilization (N-P-K) kg/ha 80-34-00 40-17-00 31.5-8.5-0 23-0-0 40-17-00 31.5-8.5-0 23-0-0 40-17-00 31.5-8.5-0 23-0-0 Date (2nd fert.) m/d 5/10 7/30 7/30 7/30 7/1 7/1 7/1 6/1 6/1 6/1 Weeding (Sprayer) m/d 6/25 8/19 8/19 8/19 7/21 7/21 7/21 6/21 6/21 6/21 Aatrex lt/ha 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2,4-D lt/ha 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 Total irrigation volume (IA) mm 390 1st m/d 4/4 2nd m/d 4/30 3rd m/d 5/25 Harvesting m/d 10/10 11/3 11/3 11/3 11/3 11/3 11/3 11/3 11/3 11/3 m/d: month and day 106 52 Harrowing is often carried out on fields to reduce the rough finish left by plowing operations. 107 b) Recommended Management (RM)
The recommended corn management, (Table 12) under conventional till, was suggested from INIA (1977), INIFAP53 (1981), SEDAGRO54, Valverde (2002), and
ICAMEX55 (1994). This management represents the institutional research efforts to increase corn productivity to its optimum level, i.e., as researchers recommend. c) Sustainable or Best Management Practices (BMPs)
This section identifies, for Texcoco district, the BMPs in terms of soil and water conservation. The present analysis was based on the corn’s current management (CM) and the practices that have been revised in this study. In other words, it compares CM
(specifically conventional till using straight rows) with tillage systems –conventional
(CT), minimum (MT), and no till (NT)– combining manures (10 t/ha of beef or poultry manure), mulching (5 t/ha), furrow dike56, contouring57, and/or bench terraces58 as shown on Table 13. The calibrated PEC and CN parameter of Table 13 were taken from Table 4
(page 75).
53 Mexican Institute for Research in Forestry, Agriculture and Cattle Raising (INIFAP) formerly called INIA. http://www.inifap.gob.mx/ 54 State Secretariat of the Agriculture Development (SEDAGRO). http://www.edomex.gob.mx/SEDAGRO 55 State Institute for Research and Extension in Agriculture, Cattle Raising, Fishery and Forestry (ICAMEX). http://www1.edomexico.gob.mx/icamex/inicio.htm 56 Furrow dike dimensions: height: 0.2 m, ridge interval: 0.85 m, furrow dike interval 1.25 m. 57 Ridge dimensions: height 0.2 m, interval 0.85 m. 58 For bank terraces in the region is recommended (Trueba-C., 1978) according to the USDA-SCS the following vertical (IV) and horizontal interval (L): VI=0.228*Slope+0.45, L=IV/slope*100. Today SCS (Soil Conservation Service) has changed to NRCS (Natural Resources Conservation Service). Table 12. Corn recommended management (RM) for Texcoco district. Irrigation Rainfed: ELEV<2350 m Rainfed: 235010% S>10% S< 2% 210% S>10% S< 2% 210% S>10% Seeding Rate (SDW) kg/ha 25 20 20 20 20 20 20 20 20 20 Plant population (FPP) plt/m2 6 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 Heat Units to harvest (PHU) °C 1300 1300 1300 1300 1300 1300 1300 1300 1300 1300 Plowing (moldboard plow) m/d 2/22 3/13 3/13 3/13 3/13 3/13 3/13 3/13 3/13 3/13 Harrowing (tandem disk) m/d 3/30 5/11 5/11 5/11 4/12 4/12 4/12 3/13 3/13 3/13 Furrowing/Row Planting m/d 4/7 5/26 5/26 5/26 4/27 4/27 4/27 3/28 3/28 3/28 1st row cult m/d 5/10 7/10 7/10 7/10 6/11 6/11 6/11 5/12 5/12 5/12 2nd row cult m/d 5/25 7/30 7/30 7/30 7/1 7/1 7/1 6/1 6/1 6/1 Total fertilization (N-P-K) kg/ha 150-50-00 80-45-00 80-45-00 80-45-00 80-45-00 80-45-00 80-45-00 80-35-00 80-35-00 80-35-00 1st Fertilization: Date m/d 4/7 5/26 5/26 5/26 4/27 4/27 4/27 3/28 3/28 3/28 N-P-K 75-50-00 40-45-00 40-45-00 40-45-00 40-45-00 40-45-00 40-45-00 40-35-00 40-35-00 40-35-00 2nd Fertilization: Date m/d 5/25 7/30 7/30 7/30 7/1 7/1 7/1 6/1 6/1 6/1 N-P-K 75-00-00 40-00-00 40-00-00 40-00-00 40-00-00 40-00-00 40-00-00 40-00-00 40-00-00 40-00-00 Sprayer (Pest) m/d 4/7 5/26 5/26 5/26 4/27 4/27 4/27 3/28 3/28 3/28 Furadan kg/ha 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 Sprayer (Pest) m/d 5/10 7/10 7/10 7/10 6/11 6/11 6/11 5/12 5/12 5/12 Methyl Parathion lt/ha 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 Sprayer (Weeds) m/d 6/25 8/19 8/19 8/19 7/21 7/21 7/21 6/21 6/21 6/21 Gesaprim lt/ha 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2,4-D lt/ha 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 Total irrigation volume (IA) mm 520 1st m/d 4/4 2nd m/d 4/30 3rd m/d 5/25 4th m/d 6/25 Harvesting m/d 10/10 11/3 11/3 11/3 11/3 11/3 11/3 11/3 11/3 11/3 m/d: month and day 108 109
Table 13. Studied agronomic and mechanical practices for soil and water conservation. Crop Mana- Surface Channel Slope Erosion CN Runoff Management gement Roughness Rough- Length Practice group Curve number ness (m) Factor Number (Table 3) Fair SN CHN SL PEC CN2 CT, Contouring, 2 0.09 0.015 100 0.60 B 84 beef manure C 86 CT, Contouring, 3 0.09 0.015 28 0.45 B 80 Terrace C 82 CT, Furrow dike: 4 0.09 0.033 100 0.60 B 83 1.25m C 85 CT, Manure 8 0.09 0.033 100 0.90 B 84 poultry C 86 MT, beef manure 11 0.08 0.025 100 0.75 B 84 C 86 MT, Contouring 12 0.08 0.015 100 0.55 B 82 C 84 MT, Contouring, 13 0.19 0.025 100 0.35 B 79 Mulching C 81 MT, Mulching 14 0.19 0.025 100 0.65 B 82 C 84 MT, mulching, 15 0.19 0.025 100 0.55 B 81 beef manure C 83 NT, contouring 17 0.07 0.015 100 0.40 B 81 C 83 NT, contouring, 18 0.07 0.015 28 0.15 B 78 Terraces C 82 NT, contouring, 19 0.19 0.015 100 0.20 B 77 mulching C 79
For the present section thirteen soils types (Table 14) were selected according to the following criteria: rainfed conditions, erosion exceeding 20 t/ha, slope over 5% (base on validation results), soil areas greater than 500 ha, and covering more than 50% of district’s agriculture area. Thus, the chosen soils shown in Table 14 cover 51.6% of the district’s surface, and 94.1% of the area over 5% of slope. Because that the same soil type covered by several HRU, in the present analysis, the most representative HRU were selected according to their area dominance, slope, corn yield, and soil erosion levels. 110
Table 14. Soils selected for BMPs analysis. No Soil Depth HRU Texture CN Area m Group ha 1 BE-D 0.35 155 Loam C 1131.7 2 HC-DP 0.65 111 Sandy clay loam C 922.3 3 HH-D 0.35 135 Loam B 2061.5 4 HH-DP 0.65 161 Sandy clay loam C 5983 5 HH-L 0.35 77 Sandy clay loam C 4333.1 6 HH-LP 0.65 47 Sandy clay loam B 1555.3 7 RD-G 0.65 211 Sandy loam B 4653 8 RE-G 0.35 214 Loam B 3018.1 9 RE-L 0.35 134 Loam B 854.8 10 RE-LP 0.65 197 Loam C 3437.9 11 TH-P 0.35 250 Sandy loam B 2499.7 12 TO-L 0.35 196 Loam C 721.7 13 VP-D 0.35 31 Sandy clay loam C 1098.8
The BMPs analysis was based on the crop’s CM (Table 11) applied to the selected
HRU (Table 14) and extended to the agronomic and mechanical practices shown on
Table 13. As result, Table 15 shows the crop management combining, soil types (the most representative HRU), topographical constrains, agronomical and mechanical practices, and tillage systems that were considered. Table 15. Corn’s current management (CM) applied to the selected HRU under conventional (CT), minimum (MT), and no (NT) till. Rainfed: Rainfed: 235010% 210% S>10% 210% S>10% HRU: HRU: HRU: HRU: 31, 214 HRU: 77, 155 47, 111, 250 161, 197, 211 134, 135, 196 Till CT CT MT NT CT CT MT NT CT CT MT NT CT CT MT NT CT CT MT NT Leaf area index (DMLA) m2/m2 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 1.50 1.50 1.50 1.50 2.00 2.00 2.00 2.00 1.50 1.50 1.50 1.50 Seeding Rate (SDW) kg/ha 14 14 14 14 14 14 14 14 11 11 11 11 14 14 14 14 11 11 11 11 Plant population (FPP) plt/m2 3.2 3.2 3.2 3.2 3.2 3.2 3.2 3.2 2.4 2.4 2.4 2.4 3.2 3.2 3.2 3.2 2.4 2.4 2.4 2.4 Plowing (moldboard plow) m/d 3/13 3/13 3/13 3/13 3/13 3/13 3/13 3/13 3/13 3/13 3/13 3/13 3/13 3/13 3/13 Harrowing m/d 5/11 5/11 5/11 4/12 4/12 4/12 4/12 4/12 4/12 3/13 3/13 3/13 3/13 3/13 3/13 Manure (10 t/ha) m/d 5/26 5/26 4/27 4/27 4/27 4/27 3/28 3/28 3/28 3/28 1st Fertilization -N- m/d 5/26 5/26 5/26 5/26 4/27 4/27 4/27 4/27 4/27 4/27 4/27 4/27 3/28 3/28 3/28 3/28 3/28 3/28 3/28 3/28 1st Fertilization -N-* kg/ha 16 16 16 16 16 16 16 16 12 12 12 12 16 16 16 16 12 12 12 12 Fertilization -P- m/d 5/26 5/26 5/26 5/26 4/27 4/27 4/27 4/27 3/283/28 3/28 3/28 Fertilization -P- * kg/ha 8.5 8.5 8.5 8.5 8.5 8.5 8.5 8.5 8.5 8.5 8.5 8.5 Furrowing/Row Planting m/d 5/26 5/26 5/26 5/26 4/27 4/27 4/27 4/27 4/27 4/27 4/27 4/27 3/28 3/28 3/28 3/28 3/28 3/28 3/28 3/28 Weeding 1st row cult m/d 7/10 7/10 6/11 6/11 6/11 6/11 5/12 5/12 5/12 5/12 2nd Fertilization -N- m/d 7/30 7/30 7/30 7/30 7/1 7/1 7/1 7/1 7/1 7/1 7/1 7/1 6/1 6/1 6/1 6/1 6/1 6/1 6/1 6/1 2nd Fertilization -N- * kg/ha 15.5 15.5 15.5 15.5 15.5 15.5 15.5 15.5 11 11 11 11 15.515.5 15.5 15.5 11 11 11 11 Weeding 2nd row cult m/d 7/30 7/30 7/1 7/1 7/1 7/1 6/1 6/1 6/1 6/1 Furrow dike m/d 7/30 7/1 7/1 6/1 6/1 Sprayer Aatrex (1 l/ha) m/d 8/19 8/19 8/19 8/19 7/21 7/21 7/21 7/21 7/21 7/21 7/21 7/21 6/21 6/21 6/21 6/21 6/21 6/21 6/21 6/21 weeding Sprayer 2, 4-D (1 l/ha) m/d 8/19 8/19 8/19 8/19 7/21 7/21 7/21 7/21 7/21 7/21 7/21 7/21 6/21 6/21 6/21 6/21 6/21 6/21 6/21 6/21 weeding Harvesting m/d 11/3 11/3 11/3 11/3 11/3 11/3 11/3 11/3 11/3 11/3 11/3 11/3 11/3 11/3 11/3 11/3 11/3 11/3 11/3 11/3 m/d: month and day; *: 0.1 m depth 111 112
IV. ANALYSIS OF RESULTS
The analysis of results in this study was accomplished according coefficient of determination (r2) computed by regression analysis. This coefficient was used (besides the slope and the intercept) to compare simulated and observed values and also to analyze the relationship between independent and dependent variables. The following general categories of r2 were used to interpret model results (Davis, 2000; Neter et al., 1985).
Table 16. General categories for analysis regression interpretation. Correlation Coefficient of Relationship Categories Coefficient59 Determination60 (±r) (r2) No relationship 0.0 0.00 Very weak < 0.2 < 0.04 Weak (diffuse cloud) 0.2 to 0.4 0.04 – 0.16 Moderate 0.4 to 0.6 0.16 – 0.36 Moderately strong 0.6 to 0.8 0.36 – 0.64 Strong (tight cloud) 0.8 to 0.9 0.64 – 0.81 Very strong >0.9 >0.81 Perfect relationship 1.0 1.00
A. Calibration
Biomass assessment is crucial in EPIC to evaluate soil erosion because biomass development is used to evaluate the USLE’s C factor. During the model calibration of this study, first, corn yield was evaluated with the purpose to test the biomass sub-model.
59 The Pearson’s correlation coefficient (r) is a measure of the direction and strength of a relationship (weak, moderate, strong) between the independent and dependent variables. The coefficient varies between -1 and 1. The closer r is to 1, more tightly the points on the scatter plot are clustered around a line. This coefficient is the square root of r2, 60 The coefficient of determination (r2) also sometimes called goodness of fit, is a measure of how well the regression line calculates the data. This coefficient represents the percentage of variation (as measured by the sum of the squared residuals) in the dependent variable that results from the independent variable, i.e., how much of the variation in the data is explained by the regression line. The larger and closer r2 is to 1, more confident the regression line. 113
Once the plant growth sub-model was evaluated, it was run under several management conditions (Figure 19 and Figure 20). Second, EPIC was calibrated to the annual runoff data (Figure 20) by using the curves numbers (CN) shown on Table 4. Finally, the annual sediment yield data (Figure 22) was standardized by adopting a rainfall energy factor of 3
(APM=3); a factor that is used by EPIC to adjust the maximum 0.5 h rainfall records.
Most of the revised experiments (87.5%) where cultivated under conventional till and the main goal of their researchers were to investigate the impact of fertilization, plant population and planting dates on corn productivity. In Figure 19 one can observe that
EPIC satisfactorily simulated the observed corn’s yield with a very strong relationship
(r2=0.88, i.e, 88% of the total variation in the simulated corn’s yield can be explained by
EPIC) between observed and simulated values and there were not under or overestimation (regression slope =0.99). The observed dispersion around the tendency line (between observed and simulated) can be attributed to: 1) the plots were run with daily meteorological records from the closest station, and 2) due to a non-reported crop management, because most of these plots were cultivated by the owner, following the researcher’s instructions.
Taking into consideration that these plots were disperse along the district, it is remarkable to observe corn yields between 3.0 to 8.0 t/ha, because the mean crop productivity in the district, under conventional tillage, is around 2.2 t/ha. This notable difference in corn yields shows the impact of crop’s management on grain productivity. 114
9 y = 0.9898x R2 = 0.8804
7
5
Simulated Crop Yield (t/ha) Yield Crop Simulated 3
1 13579 Observed Crop Yield (t/ha)
Figure 19. Observed and simulated corn yield in dry lands under Conventional Till (CT).
In Figure 20 it is observed that conventional till (CT) reports bigger corn yields than minimum till (MT). In this case, it must taken into account that, in such plots, corn under MT was introduced for the first time after a long history of CT. Figure 20 shows that poultry’s manure increases corn yield as irrigation does, which emphasizes the positive effect of OM and the manure’s nutrients on corn productivity. The furrow dikes implemented in the northern region (the driest one) of the district report the lowest grain yields, because the low rainfalls during the crop cycle (271 mm). Nevertheless it is observed that furrow dikes with a spacing of 1.25m slightly increased the grain yield compared with the spatial arrangements of 2.5m. 115
From Figure 21 one can observe that plots under no till (NT) report lower amounts of annual runoff than CT or MT. It must be noticed that the annual runoff produced by NT plots is comparable to the water yield reported with bench terraces. In these results, it must be taken into consideration that the values are not slope standardized; in other words, they are not conclusive.
9 y = 0.9766x R2 = 0.9012 8 CT Poultry 10 t/ha 7 CT Irrigation-Manure CT Irr-Mulch-Manure 6 CT Irrigation CT Furrow Dike 1.25m 5 CT Furrow Dike 2.50m MT Contouring-Mulching 4 MT Mulching Simulated Crop YieldSimulated Crop (t/ha) MT Manure 3 MT Mulching-Manure MT Straight Row 2 23456789 Observed Crop Yield (t/ha) Figure 20. Observed and simulated corn yield for Conventional (CT) and Minimum Till (MT). 116
180 y = 0.9719x 160 R2 = 0.9826
140
120 CT Contouring CT Straight Row 100 CT Straight Row-Terrace
80 MT Contouring NT Contouring 60 NT Straight Row-Terrace
Simulated Runoff (mm) 40
20
0 0 20 40 60 80 100 120 140 160 180 Observed Runoff (mm) Figure 21. Observed and simulated annual runoff for corn plots under Conventional (CT), Minimum (MT), and No Till (NT).
In Figure 22 one can note that NT, if compared with CT or MT, accounts the lowest sediment yields. In the same way, practices involving bench terraces also report the lowest amounts of annual soil loss. From the same figure, consistently one can see that CT associated with straight rows reports the highest amounts of annual soil loss, which is theoretically coherent. Based on Figures 18, 19 and 20, where soil conservation practices are included, there is a very strong correlation (r2 between 0.90 - 0.98) between the simulated and observed values. Those plots with higher correlations (when compared to Figure 19) can be based on more detailed crop management reports and better weather records; because most of these experiments had their own meteorological station. 117
y = 1.0728x R2 = 0.9606 8
6 CT Contouring CT Contouring-Terrace CT Straight Row CT Straight Row-Terrace 4 MT Contouring NT Contouring NT Straight Row-Terrace
Simulated Sediment (t/ha) Sediment Simulated 2
0 02468 Observed Sediment (t/ha) Figure 22. Observed and simulated annual sediment yield for corn plots under Conventional (CT), Minimum (MT), and No Till (NT).
The results from the calibration process show that the EPIC model simulates corn yield, annual sediment production, and annual runoff satisfactorily under a wide range of climatological conditions, crop management, soil conservation strategies, and soil properties. Based on the result’s consistency and model’s versatility, this study proceeded with model validation on a spatial basis. The idea for a spatial validation is to take advantage of model capabilities to simulate corn yield under a broad variety of environmental crop management conditions; circumstance that is observed in the district. 118
B. Model Validation
Considering that EPIC estimates soil erosion with USLE and one of the goals of this study is to evaluate the district’s most susceptible areas for soil erosion; model validation, on a spatial basis, focused on ULSE most uncertain parameter, the cover factor (C). Due to the lack of runoff or sediment yield information from the corn producer area, like in the calibration process, the grain yield (via the Harvest Index) was considered as an indicator of the aerial biomass that grows during the crop’s cycle.
The validation was performed by using the mean corn yields (between 1999 and
2005), which SAGARPA reports online at municipio scale61. For this purpose also the mean records (1978-1986) reported by Solis (1988) were used. Both historical grain mean yields were run, in EPIC, using the mean climatic parameters into the weather generator. Figure 23 shows the observed and simulated corn yields, for both historical series, on a municipio basis.
In order to observe how the model works with daily meteorological records, the validation was also executed for the 2002 and 2003 grain harvest reported online by
SAGARPA and the 2003 corn harvest reported by SEDAGRO (2003) at municipio level.
The results are shown in the Figure 24.
61 http://www.siap.sagarpa.gob.mx/ar_comdownload.html 6.0
5.0 SAGARPA Solis (1988) SIMULATED 4.0
3.0
2.0 Grain Yield (t/ha)) Yield Grain 1.0
0.0 Paz La Paz Chalco Atenco Atlautla Texcoco Ozumba Chiautla Ecatepec Coacalco Papalotla Ecatzingo Ayapango Cocotitlán Ixtapaluca Juchitepec Tepetlixpa Temamat la Tlamanalco Chiconcuac Amecameca Chicoloapan Tepetlaoxtoc Chimalhuacán Tenango del aire del Tenango Municipio Figure 23. Observed and simulated (using the weather generator) historical corn yield by municipio.
6.0
5.0 SAGARPA 2003 Simulated 2003 SEDAGRO 2003 SAGARPA 2002 Simulated 2002 4.0
3.0
2.0 Grain Yield (t/ha)) Yield Grain 1.0
0.0 Paz La Chalco Atenco Atlautla Chiautla Ozumba Texcoco Ecatepec Papalotla Coacalco Ecatzingo Ayapango Ixtapaluca Cocotitlán Juchitepec Tepetlixpa Temamatla Chiconcuac Amecameca Tlalmanalco Chicoloapan Tepetlaoxtoc Chimalhuacán Tenango del aire del Tenango Municipio Figure 24. Observed and simulated corn yield for 2002 and 2003 by municipio. 119 120
From the Figure 24, some observations of corn yield look too low (0.09 t/ha) or too high (5.87 t/ha). Also, one can see that four municipios in 2002 have the same yield
(2.5 t/ha) and such value is repeated again in 2003 in three municipios (the same ones out of the four reported in 2002). From these data, it was inferred that there are some problems associated with the methodology applied by the federal and state institutions to evaluate corn yields. In both graphics one can observe that in many cases (40%) the simulated value is located between the observed amounts of different source, so 24% of the observed data was removed to create Figure 23. This figure shows the correlation between observed and simulated corn yield on a municipio basis. From Figure 23, it was assumed that the correlation (r2=0.576) is moderately strong and consequently acceptable for the purpose of this study, given all the sources of uncertainty; like the crop yield sampling method, soil map accuracy62, crop management, closeness between HRU and meteorological station, and seed’s genetic characteristics.
62 Ortiz and Gutiérrez (2001) report that the INEGI’s soil maps (1:50,000) , in a section of the Atenco municipio (1447 ha), show low degrees of precision (8%) and accuracy (14%) compared with the 50% of average purity in the US soil maps (1:24,000). 121
6.0
5.0
y = 1.0566x R2 = 0.576 4.0
3.0 Observed (t/ha) Observed
2.0
1.0
0.0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Simulated (t/ha) Figure 25. Municipio observed and simulated corn yield (the correlation line corresponds to 76 % of data observations, the red points).
C. Crop Management Analysis
The current analysis was based on the plant, soil and topographical parameters determined during the calibration and the validation process. This analysis was also performed according to the daily simulated values of solar radiation, rainfall, temperature, relative humidity, and wind velocity. Daily values were estimated by the EPIC’s weather generator according to the average climatic conditions observed into the district. 122
1. Current Management (CM)
Figure 26 shows corn yield distribution, under CM, in the district. In Figure 26 it
is observed that the highest grain yield is related to irrigated plots; followed, in descending order, by HRU with soils over 1.0 m deep and ground slopes under 2% of
Texcoco63 and Amecameca64 sub-regions65. Figure 26 also indicates that ares with lower
corn yield are found in soils with higher slope and shallower profile, i.e., in eroded soils.
Figure 27 shows that under the CM, 60.5% of the district’s agriculture area hold
soil erosion magnitudes over the theoretical 11.2 t/ha for the soil’s natural formation
(Hudson, 1995; Renard et al., 1997). The Mexican ministry of agricultural has adopted a
soil erosion tolerance of 20 t/ha (Figueroa et al., 1991), that it implies that the district
needs to implement soil conservation practices on least in 50.0% of its agricultural land.
Appendix B6 shows the land owners with problems of soil erosion exceeding 20.0 t/ha.
63 Texcoco region: Coacalco, Ecatepec, Atenco, Chiautla and Texcoco municipios. 64 Amecameca region: Chalco, Tlalmanalco, Amaecameca, Ayapango, Tenango del Aire, Juchitepec, Ozumba, and Atlautla municipios. 65 Texcoco and Amecameca sub-regions have been classified according to their valley mean elevation; 2250 m and 2500 m respectively (INIA, 1977). 123
490000 500000 510000 520000 530000 540000
Coacalco 2170000 2170000
Chiautla Ecatepec Te p etl a ox toc ChiconcuacPapalotla Atenco 2160000 2160000
Tex coco
2150000 Chimalhuacan 2150000 Netzahuacoyotl Chicoloapan
La paz Ixtapaluca 2140000 2140000
Chalco 2130000 2130000
Cocotitlan Tlalmanalco Te m am at l a
2120000 Tenango del Air 2120000
Ayapango Amecameca
Juchitepec 2110000 2110000 Ozumba Atlautla
Tep etl i x pa
2100000 Ecatzingo 2100000
490000 500000 510000 520000 530000 540000 Corn Yield (t/ha) Legend Meters 0.989 - 1.163 07,500 15,000 30,000 1.164 - 1.458 Municipios 1.459 - 1.712 Projected Coordinate System: UTM, Zone 14N 1.713 - 1.926 Datum: North American 1927 Source: INEGI's Soil Charts 1:50,000 1.927 - 2.527 4 Data classification: natural breaks (Jenks) 2.528 - 3.330 Figure 26. Mean corn yield under the Current Management by HRU. 124
490000 500000 510000 520000 530000 540000
485
585 586 566 583 580 575 567 569 581 540 565565 568 570 582584587 579578 539 2170000 577 537538 573 2170000 552564576 543 545 547 571 484 553 563 536 549 562561 535 536 544 546 488 489 542 550 551 561 532 572 549 557 535534 543 574 490 560 558 511 541 556 533 508 548559 512 510 487 555 526 524 554 530 521 507 486 531 525 483523 509 529 522 519520518 513 505 506 528 481482483 517 527526 480479 516 504 461 466 478 515514 499 498 501 464 465470469 474475 477 502 503 473 497 462 467 468 471472 476 493501 500 451452 494 463 460 492 2160000 460 450449 495 2160000 458 491 496 459444 448 441 453 454 457 436 442 456 444445447 440 431 455 445 437438 439 429 428 430 443 413 426 432 436 400 434 421 405 435 427 433 398 399 423 425 397394 404 431 422 420 393 401 424 395396 403 402 419 382 381 373 383384 409 392 385380 415 417 374 416 2150000 413414 413 2150000 386387379 378 391 418 389 388 412 386 389 370 411 406 376 371 407 390 370 410 344 375 408 360 370 345 349 351 372 343 347 369 346 331 337 370 348 350 337 304 342 330 341340339324 328 337 321 327 336 326 2140000 338305 313 323 329 367 2140000 305305308309 273 320 325 366 310 368 322 298 332 307 312314311 318320319 293 317316 291 304 294299 292 334 306 315 293 303 296 295 288 301300315 333 364365 302 296289 290 335 272 286 362 269 271 273 363 285 284 287361 270 274 280 282 283 269 267 279 280 268 281 223 220 219 263264 275 278 280 2130000 262261 225 2130000 256 265266 277 174 255 259260 227 222 218 217 216 214215 257 253 242241 276 254 224226224 252 240239 221 212 249250 251 238 211213 209210 183 243245 248 235 228 208 179 174 232 202204205 175 247 229 207 201 206 178176 244237 200 195203 184 173 245 236 230 196 188185 179180 230231 198 191 182 197 192 181 246 199 196194193 174 172 154 190189 187 147 234 138 186 155 137 143 233 170 119 136 144145 170 2120000 117 118 133134135 142 146 2120000 109 110 111 120 125 112113114115115 129 132 141 150149 124123 131 116 122 128 140 148 104 139 103102121126 128 130 100 127 101 98 169 99 108 97 96 88 95 94 93 63 89 92 76 8687 77 84 91 75 78 62 85 90 74 73 83 79 64 151 168 69 72 2110000 68 70 8180 64 2110000 107 71152 67 8281 106 65 81 153 105 64 66
168 156 167 158 157 166 163 168 165 160 2100000 159 162 2100000
161 164
490000 500000 510000 520000 530000 540000
Soil Erosion (t/ha) Legend Meters 07,500 15,000 30,000 < 10 District Boundary 10 - 20 483 Land Ownership Projected Coordinate System: UTM, Zone 14N 20 - 30 Datum: North American 1927 30 - 50 Source: Land ownership according to SRA 50 - 110 4 > 110 ` Figure 27. Mean soil erosion under the Current Management (CM) by HRU. 125
Figure 28 shows the CM’s hydrological balance, by HRU, for the average
climatological conditions during the first year of simulation. Even though the rainfall in
the district, during the crop cycle, varies between 651.9 and 906.7 mm ( x =806.4mm)
there is not a clear relationship with crop productivity within the range (Appendix B5).
However, in Figure 33 it is observed that bigger plant evaporation is related with higher
grain yield and the highest rates correspond to the irrigated HRU (Figure 28).
Figure 30 shows that soils with larger amounts of soil erosion have lower crop
productivity and more accelerated rates of soil erosion. In general, it was found (Figure
29) that under CM the rate of soil erosion at district level grows 12.0% (14.66% SD) for
the first 50 years of simulation and in a hundred years it grows to 19.9% (27% SD). Thus,
the district’s soils show the tendency of becoming less fertile as they are more eroded.
This increment of erosion’s rates can be explained by the reduction in the aerial biomass
associated with soil fertility. In Figure 31 is observed a very strong correlation (r2=0.87) between soil erosion and slope; where the larger rates of soil erosion are observed in the soils with higher slope. Thus, from this figure it is concluded that soil conservation practices are mainly required on slopes over 5%. Rainfall ET Irrigation Plant Evap. Runoff Percolation Subsurface Flow 900
800
700
600
500
400
Water (mm) 300
200
100
0 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 105 110 115 120 125 130 135 140 145 150 155 160 165 170 175 180 185 190 195 200 205 210 215 220 225 230 235 240 245 250 255 HRU Figure 28. HRU’s water balance for the current corn management (CM) (1st simulated year).
50 )
40 CM: 50th-yr CM: 100th-yr 30
20
10 Soil Erosion Increment (% 0 0 100 200 300 400 500 Soil Erosion CM 1st-yr (t/ha)
Figure 29. Change of soil erosion rate for CM in a hundred years. 126 4 10000 y = 2.3131x1.5081 -0.114 y = 2.435x R2 = 0.8724 ) 3 2 1000 R = 0.781
2 100
Corn Yield (t/ha 1 10
0 1 0.1 1 10 100 1000 10000 0 1020304050 Soil Erosion (t/ha) Log. Scale Log. (t/ha) Erosion Soil Soil Erosion (t/ha) Log. Scale 0 Surface Slope Figure 30. Relationship between soil erosion and corn Figure 31. Relationship soil erosion/slope 1st year of productivity (under CM). simulation (under CM). 5 Current Recomended ) 8
y = 0.009x + 2.966 4 7 y = 0.016x - 2.018 R2 6 = 0.050 R 2 = 0.747 ) 5 3 4 3 2 2 Corn Yield (t/ha
Corn decrement (% yield 1 1 0 y = 0.007x + 0.112 0 50 100 150 200 R2 = 0.761 Erosion increment(%) 0 100 150 200 250 300 350 400 Plant Evaporation (mm) Figure 32. Relation between the differences of soil erosion and Figure 33. Effect of crop management on plant evaporation corn yield simulated in hundred years (under CM). st and corn yield (1 simulated year). 127 128
Figure 32 shows that under CM the corn productivity, in a hundred of years, decreases 3% even when soil erosion does not increases. This value could be associated with the loss of soil fertility due to acidification, loss of nutrients or OM. After this threshold of 3%, soils susceptible to increasing rates of soil erosion, apparently (very weak relationship) they undergo an additional decrease of 1% on corn yield for each increment of 100% in the rate of soil erosion. This figure implies that for the district soil conservation practices focused on fertility conservation should be recommended.
Otherwise, the chemical fertilization, in the district, would grow between 3 to 5% for every hundred years in order to maintain corn productivity associated with soil degradation.
2. Recommended Management (RM)
The water balance shows that crop yield increases with higher rates of plant evaporation (Figure 33), lower quantities of runoff (Figure 34), and lower amounts of subsurface flow (Figure 35). When comparing the CM to the RM, for rainfed HRU, the
RM reduces runoff by 13.1% and percolation by 17.8%, which increases the amount of water available for plant evaporation by 36.6%. Thus, one can infer that the higher fertilization, associated with RM, favors larger biomass development and consequently higher rates of plant transpiration. As a result, this increment on plant transpiration modifies the water balance by reducing water percolation and surface runoff. Therefore, these relationships show the impact of soil moisture on crop productivity and the importance of BMPs oriented to keep soil’s fertility and reduce runoff and percolation. 5 5 Current Recomended Current Recomended
4 4 y = -0.011x + 3.870
R 2 ) ) = 0.615 3 3 y = -0.032x + 3.175 R2 = 0.447
2 2 Corn Yield (t/ha Yield Corn Corn Yield (t/ha
y = -0.005x + 2.291 2 1 R = 0.665 1 y = -0.018x + 1.922 R2 = 0.495 0 0 0 50 100 150 200 250 0 1020304050607080 Subsurface Flow (mm) Ruroff (mm) Figure 34. Crop’s management impact on runoff and Figure 35. Crop’s management impact on subsurface flow st corn yield (1 simulated year on the rainfed HRU). and corn yield (1st simulated year on the rainfed HRU).
5.0 Current rain fed Recommended rain fed Current Irrigated Recommended Irrigated 0 ) 4.0 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 -1
3.0 -2
2.0 -3 y = 1.1019x - 4.834 Corn Yield (t/ha) Yield Corn -4 R2 1.0 = 0.2337 -5 Decrement Corn (% yield 0.0 Current Recommended -6 0 50 100 150 200 250 300 350 400 450 500 550 Corn Yield 1 st Pe rcolation (mm) yr (t/ha)
Figure 36. Relationship between percolation and corn Figure 37. Decrement of corn yield between the 1st and the 129 yield (1st simulated year of the current management). 100th year of simulation. 130
In Figure 36 one can observe on average, that RM increases grain yield by 1.09
t/ha on rainfed conditions and RM reduces crop productivity by 0.16 t/ha in irrigated
HRU. This implies that corn yield under irrigation, for RM, does not react to increased
amounts of fertilization (Table 12). At the same time, in Figure 36 one can observe that
the highest percolation rates occur in the irrigated areas. Thus, it is inferred that the
simulated irrigation volume, mainly for RM, creates problems of aeration stress and
fertilization leaching, which reduces grain yield. On field conditions, this over-watering
problem (intrinsic to long term simulation on average climatic conditions) is faced, by the
corn producer supplying irrigation according to the climatic conditions and the water crop
needs instead of the fixed dates that were simulated.
Taking into account that 1) the CM was simulated using 390 mm of irrigation and
2) the percolation differences between irrigated and rainfed HRU is 232.3mm; it is
feasible to save 157.5 mm of water or at least one irrigation application of 130 mm (33%).
Following the same analysis, for RM, it is viable to reduce the irrigation rate by 50%
without affecting crop yield.
Figure 37 shows how corn yield decreases, in each HRU, between the 1st and the
100th year of simulation. Figure 37 shows that lower crop yields are associated with
increased decrements on crop productivity. RM, compared to CM, produces higher yields
and lower decrements on productivity. This means that higher fertilization associated to
RM attenuate effects of soil degradationif. Beside if the RM were followed in the district
the average crop productivity could be increased 54.3%. 0.5 0.6
) Current Recommended Current Recommended C calculated 0.5
C=0.176 m 0.4 0.4 C=0.188
0.3 0.3 C=0.210 y = 0.2606e-0.8519x 0.2 R2 = 0.6105 C=0.232
USLE' C Factor (Adi C Factor USLE' 0.2
Increment Soil Erosion (% Erosion Soil Increment 0.1
0.0 C=0.258 0.1 1.0 10.0 100.0 1000.0 10000.0 0.1 0.0 0.2 0.4 0.6 0.8 1.0 Soil Erosion 1st yr (t/ha) log. scale. Increment Soil Erosion (%)
Figure 38. Soil Erosion Increment between the 1st and the Figure 39. Relationship between soil erosion increment and the 100th year of simulation according to USLE’s C factor. USLE’s C factor. 131 132
From Figure 38 one can see that the rate of soil erosion between the 1st and the
100th year of simulation is almost the same (lower than 0.5%) and the rate of change it is
not affected RM. However, the rate of erosion change apparently is related to the USLE’s
C factor (Figure 39). A lower C, related with crop healthier canopies, is found in more
erodable, deeper, steeper, and more fertile soils; like the Humic Andosols that are
associated with forest soils. Comparing the mean grain yield percentage differences
between CM and RM (54.3%) and the CM’s change between the 1st and the 100th year (-
3.4%) one observes the impact of fertilization on crop productivity.
The next section identifies the BMPs to counterbalance the loss of productivity associated with soil erosion, basically, considering soil’s moisture conservation practices instead of extra fertilization.
3. Sustainable Best Management Practices (BMPs)
This section analyzes the BMPs practices that were calibrated and potentially could sustain regional food security and eventually reduce farmer’s production costs associated with fertilization and water supply. These practices were run for thirteen soils types (Table 14 and Table 15) with slopes over 5% that represent 94.1% of the area with soil erosion problems exceeding 20 t/ha. These soils correspond to shallow profiles, nine of them are below 0.35 m depth and four under 0.65 m (Table 8 on page 90). Appendix
C1 shows EPIC’s results for these management scenarios graphed on Figure 40.
Figure 40 shows that shallower soils (below 0.35 m depth) tend to produce larger runoff and higher soil erosion rates. These higher soil erosion rates are observed mainly on loamy textures like in the HH-L, RE-L, HH-D, BE-D, TO-L, RE-G, and TH-P soil 133
types (see Table 8 on page 90). Besides the higher soil erosion, these soil types also
reveal lower values of evapotranspiration and consequently poorer corn yields.
In order to have a base scenario, for comparison purposes, the CM (CT with
straight rows) was selected; because it is the most common and critical management.
Figure 40 (a and b) shows that the base scenario, in average, reports the highest annual
soil erosion (90.7 t/ha) and the lowest corn yield (1.5 t/ha). In terms of soil erosion, it can
be observed that the BMPs with lower average rates of soil erosion are CT-contouring-
terrace (29.7 t/ha) followed by NT-contouring-terrace (31.8 t/ha), and NT-contouring-
mulching (37.0 t/ha). In terms of corn productivity the BMPs with higher grain yield are
CT-contouring-terrace (4.1 t/ha) and CT-poultry manure (4.1 t/ha) followed by MT-beef
manure (2.07 t/ha), and MT-mulching-beef manure (2.08 t/ha). In this case, one can
observe the importance of manures in improving corn productivity.
In the rainfed areas with slopes over 5%, the BMPs that reduce the average runoff and increase the average percolation are show in the Table 17. The differences between
CM and NT-contouring-mulching are 19.1 mm for runoff and -13.9 mm for percolation.
It implies that such practice could increase crop’s water availability around 16%.
Table 17. The BMPs in terms of runoff and percolation BMPs in terms of: RunoffPercolation mm mm NT-contouring-mulching 96.6 86.8 CT-contouring-terrace 98.0 81.2 NT-contouring-terrace 103.5 81.1 CT-furrow dike 107.2 77.3 CM (CT-straight row) 115.7 72.9 134
CT, straight row CT, contouring, beef manure CT, contouring, terrace CT, furrow dike: 1.25m CT, manure poultry MT, beef manure MT, contouring MT, contouring, mulching MT, mulching MT, mulching, beef manure NT, contouring NT, contouring, terraces NT, contouring, mulching
200.0 5.0
180.0 4.5 160.0 ).
a 4.0 /h
t 140.0 ( 3.5 on
i 120.0 ros 100.0 3.0 il E o 80.0
lS 2.5
60.0 Corn Yield (t/ha).
nnua 2.0 A 40.0 1.5 20.0
0.0 1.0 RE-L TH-P BE-D TO-L RE-G VP-D HH-L RE-L RD-G HH-D TH-P TO-L RE-G BE-D VP-D HH-L RD-G HH-D RE-LP HH-LP RE-LP HC-DP HH-DP HH-LP HC-DP HH-DP Soils Soils a) b)
250.0 160.0
140.0
200.0 120.0
100.0
150.0 80.0
60.0 Runoff (mm). Percolation (mm).
100.0 40.0
20.0
0.0 50.0 RE-L TH-P BE-D TO-L RE-G VP-D HH-L RD-G HH-D RE-LP RE-L TH-P HH-LP RE-G BE-D TO-L VP-D HH-L HH-DP HC-DP RD-G HH-D RE-LP HH-LP HC-DP HH-DP Soils Soils c) d) 280.0 570.0
560.0 260.0 . . 550.0 240.0 540.0
220.0 530.0
200.0 520.0 Evapotranspiration (mm) Evapotranspiration Plant Evaporation (mm) Evaporation Plant 180.0 510.0
500.0 160.0 RE-L TH-P BE-D TO-L RE-G VP-D HH-L RD-G HH-D RE-LP HH-LP HH-DP HC-DP RE-L TH-P BE-D TO-L RE-G VP-D HH-L RD-G HH-D RE-LP HH-LP HC-DP HH-DP Soils Soils f) e) Figure 40. Hydrological and productive evaluation of the BMPs on main soils types (over 5% slope) from the Texcoco’s district. 135
From these results it is concluded that in slopes over 5%, if growing corn under conventional till (CT), bench terraces should be constructed in order to control soil erosion and increase water percolation. A cheaper alternative is to grow corn without till
(NT). NT would be more effective in terms of water conservation (more plant evaporation) and corn productivity if the rows follow the surface contour, the soil is covered by mulching (5 t/ha) and beef (even better poultry) manure (10 t/ha) is added.
D. Institutional Implications
The adoption or implementation of new management practices depend heavily on cultural and institutional factors. This section analyzes the effectiveness of the main
Mexican institutions dealing with environmental issues in agricultural lands and the institutional changes that could facilitate the adoption BMPs in Mexico. In the early
1990s Mexico went through a process of restructuration and modernization of its environmental laws, following the combined pressures of globalization and liberalization.
In this process, the Mexican environmental agencies were reorganized into a single, cabinet-level secretariat: The Secretariat of the Environment & Natural Resources
(SEMARNAT). Thus, this agency acquired the mandate for sustainable development, however, does not make explicit references about the sustainability of the agricultural sector. The lack of explicit inclusion of the agricultural environment and lack of organizational structure have undermined its capacity to effectively coordinate and pursue sustainability objectives jointly with other national and state ministries. Although
SEMARNAT has been involved in rural development plans, managed by the Agriculture 136
Ministry, this contribution has been marginal. The reason of such unclear environmental contribution in the agriculture sector appears to be related with limited funding
(approximately 0.05% of the total Federal budget66). In order to have a better understanding of the role of SEMARNAT, three of SEMARNAT’s organs relevant in the context of environmental sustainability are analyzed in the following: The National
Institute of Ecology (INE), The National Water Commission (CNA), and The Office of the Attorney General for Environmental Protection (PROFEPA).
The INE is in charge of environmental research and has the mandate to promote the sustainable use of natural resources through ecological land use planning, biodiversity conservation, and integrated watershed management. However, this institute does not explicitly include the study of agricultural impacts on the natural environment as part of its mandate. Although INE includes few projects related to agriculture in its agenda, lack of funding, make their efforts limited compared to the sustainability needs of the agricultural sector.
The CNA is the most developed environmental institutions in Mexico. Indeed, it is the only environmental institution in Mexico that explicitly includes the agricultural sector within its mandate and organizational structure. This institution is also in charge of enforcing regulation in areas related to inland watersheds. CNA addresses its mandate by promoting the efficient use of water in agricultural production and the sustainable management of watersheds and aquifers.
66 http://www.apartados.hacienda.gob.mx/presupuesto/index.html 137
Unfortunately, inadequate enforcement capacity at local and regional levels restricts CNA’s ability to promote more sustainable usage of water resources, collect water usage fees, control water property rights, and manage excessive water depletion rates.
PROFEPA is the primary monitoring, protection and enforcement agency and runs Mexico's environmental audit program. Since its conception, PROFEPA has had difficulties complying with its monitoring and enforcement role that come mainly from human and monetary limitations. Its structure does not include any area in charge for supervision, enforcement, and regulation related to the agriculture sector. Consequently it does not concentrate significant efforts on monitoring environmental impacts and enforcing regulation in this sector.
The Mexican government historically supported rural production through national institutions, such as the Bank of Agricultural Development (Banrural), as well as price support mechanisms such as CONASUPO. However, most of these mechanisms were ceased or significantly under funded after the 1994 crisis. The fact that the peasantry does not have access to credit from commercial banks, makes them rely on government financial redistribution schemes to become productive and sustainable. Given this, the
Ministry of Agriculture, Ranching, Rural Development, Fisheries and Feeding
(SAGARPA) set two basic policy instruments aimed to assist producers, especially low income producers, during the transition period to an open economy, the PROCAMPO and the Alliance for the Countryside. 138
PROCAMPO was established in 1994 as an income support mechanism to
compensate for loss of income expected as a consequence of lower corn prices after
international trade liberalization. The Alliance for the Countryside was conceived in 1995
to promote farming productivity and crop substitution according the NAFTA’s objectives.
The coverage of each programs is still limited (60% and 18% respectively67), and
concentrated in few and large private producers. These rural developing programs are not
environmental oriented. In many cases, in order to receive their incentives, the producers are cultivating, without any criteria of soil conservation, sensible areas to soil erosion.
The overwhelming conclusion is that the Mexican institutions are not addressing explicitly the agriculture impact and are avoiding environmental enforcement in order to avoid political costs and investment in soil conservation. Based on the above considerations, this study makes the following recommendations for institutional changes that may facilitate the adoption of soil and water conservation practices discussed in this study: x Reestablish rural development banks with restricted credits to soil conservation
practices that meet the needs of the subsistence producers. x Strengthen rural development plans, from an environmental point of view, giving
incentives to producers that enroll in sustainable agricultural practices. x Improve administrative coordination of Federal and State environmental agencies. x Increase and strengthen social participation in decision making for environmental
protection and sustainable exploitation of natural resources.
67 http://www.sagarpa.gob.mx/infohome/programas.htm 139 x Enhance social communication to inform producers about environmental and
conservation policies and programs. x Promote educational processes, training and extension services to inform producers of
the importance to maintain ecological balance. x Guarantee strict compliance with, and enforceability of, the regulations on
environmental matters. x Ensure in the environmental institutions an agricultural focus and an adequate
funding to deal with ecological stresses in agriculture areas. 140
V. CONCLUSIONS AND RECOMMENDATIONS
From the calibration process, is concluded that under a wide range of climatological conditions, crop management, soil conservation strategies, and soil properties the EPIC model simulated satisfactorily (very strong relationship) corn yield
(r2 between 0.88 and 0.90), runoff (r2=0.98), and sediment production (r2=0.96).
Based on the observed and the simulated corn yield, on a municipio basis, several sources of spatial uncertainty were found; such as the crop yield sampling method, soil map accuracy, crop management, closeness between HRU and meteorological station, and seed’s genetic characteristics (see assumptions on page 17). Even with the spatial uncertainties a moderately strong relationship (r2=0.576) was found between observed and simulated corn yield. Validation parameters that were used to identify areas with problems of soil erosion and the suggest BMPs for the district.
Comparing the CM and the RM indicated that if the crop technical management recommendations were followed, in the district, the average crop productivity could be increased by 32.6%.
Under the current management, the loss of soil fertility in the district reduces corn productivity 3% (over a hundred years). After this threshold of 3%, soils with increasing rates of soil erosion, apparently they undergo an additional decrease of 1% on corn yield for each increment of 100% in the rate of soil erosion. As a result, the technical recommendations for soil conservation must be oriented to fertility conservation issues.
Otherwise chemical fertilization in the district would grow between 3 to 5% in a hundred 141 years in order to maintain corn productivity associated with soil degradation. However, the low percentages of loss of productivity associated with soil erosion and loss of fertility can be counterbalanced easily with BMPs designed to mantain soil moisture, i.e., reducing runoff and percolation. Thus, extra fertilization can be avoided.
Furthermore, it is concluded that the district needs to implement soil conservation practices on least in 50.0% of its agricultural land and soil conservation practices must be focused on areas with slopes over 5%. The 94.1% of the area with problems of soil erosion is basically is covered with 13 soil types, all them below 0.65 m in depth (eight under 0.35 m) primarily on loamy textures. Thus, if it is decided to grow corn under conventional till in these soils, it is necessary to construct bench terraces in order to control soil erosion and increase water percolation. A good alternative to terraces is to manage corn under no till conditions. Such an alternative also can increase mean grain productivity by at least 40% (0.6 t/ha) if combined with contouring, mulching, and manures.
Study Limitations x The model calibration reported in this study can be useful for the highlands of central
Mexico; for corn producer areas below 1,700 m asl is recommended to recalibrate
EPIC’s environmental, management, and plant growth parameters. x The current calibration is useful for annual (crop cycle) comparison between observed
and simulated corn yield, surface runoff, and sediment yield. For daily or monthly
comparisons is recommended to recalibrate the model. 142
Recommendations
In order to improve applications of the EPIC model as a tool for decision making in rural development actions and natural resources conservation (soil and water) in
Mexico, the following steps are recommended: x Increase the accuracy of INEGI’s soil maps and make them available in GIS format.
Develop a national database for soil’s physical and chemical properties. x Make available online the national weather database (on a daily basis) from
metrological stations and climatological observatories. x In the most common soils of the district, implement experimental plots to evaluate CN
under several conditions of crop management. x Set up demonstration and experimental plots to evaluate crop productivity, runoff, and
soil erosion under minimum till and no till conditions. x Evaluate in more detail practices like mulching, manures, dikes spacing, contour
plowing, plant density. Also include other practices not included in this study such as:
subsoiling, green manures, composting, crop residues incorporation, leguminous
associations, alley cropping, and agroforestry. 143
APPENDIX A: PLOT CALIBRATION A1. Calibrated parcels evaluating grain productivity.
Water- Distance to Slope Plant Seeding Ridge Fertili- Management & Corn Corn Num Corn Planting Harvesting Place CN2 shed Area Outlet Length Popula. rate interval zation observations Grain Grain
WSA CHL SL Variety Date SDW(2) RIN(2) N-P-K Date Code Code Code Obs. Sim. ha km m plt/ha kg/ha m kg/ha Table 2 Table 4 Table 4 t/ha t/ha 1 1.0 0.1 100 H-30 6/9/72 60000 19.4 0.83 80-50-00 11/4/72 23 9 13 3.56 4.06 2 1.0 0.1 100 H-30 5/3/72 60000 19.4 0.74 40-50-00 9/21/72 11 9 13 2.20 2.48 3 1.0 0.1 100 H-30 5/3/72 60000 19.4 0.74 40-25-00 9/21/72 11 9 13 2.38 2.48 4 1.0 0.1 100 H-30 5/11/72 60000 19.4 0.76 80-50-00 10/5/72 50 9 15 4.81 3.88 5 1.0 0.1 100 H-30 5/11/72 60000 19.4 0.76 120-50-00 10/5/72 50 9 15 4.05 3.96 6 1.0 0.1 100 H-30 5/11/72 45000 14.8 0.76 0-25-00 10/5/72 50 9 15 3.50 3.96 7 1.0 0.1 100 H-30 5/11/72 60000 19.4 0.76 80-50-00 10/5/72 50 9 15 4.36 3.96 8 1.0 0.1 100 H-30 5/11/72 75000 23.9 0.76 80-50-00 10/5/72 50 9 15 4.31 3.87 9 1.0 0.1 100 H-30 5/10/72 60000 19.4 0.79 80-50-00 9/30/72 34 9 16 3.96 4.58 10 1.0 0.1 100 H-30 5/10/72 75000 23.9 0.79 80-50-00 9/30/72 34 9 16 4.25 3.87 11 1.0 0.1 100 H-30 5/30/72 60000 19.4 0.78 80-50-00 10/24/72 37 9 15 3.99 4.46 12 1.0 0.1 100 H-30 5/30/72 60000 19.4 0.78 80-75-00 10/24/72 37 9 15 5.02 4.46 13 1.0 0.1 100 H-30 5/30/72 60000 19.4 0.78 120-50-00 10/24/72 37 9 15 5.19 4.55 14 1.0 0.1 100 H-30 5/30/72 30000 10.2 0.78 40-25-00 10/24/72 37 9 15 3.69 3.80 15 1.0 0.1 100 H-30 5/30/72 45000 14.8 0.78 40-00-00 10/24/72 37 9 15 3.91 3.80 16 1.0 0.1 100 H-30 5/30/72 45000 14.8 0.78 80-50-00 10/24/72 37 9 15 4.28 4.46 17 1.0 0.1 100 H-30 5/30/72 60000 19.4 0.78 80-50-00 10/24/72 37 9 15 4.17 4.46 18 1.0 0.1 100 H-30 5/30/72 60000 19.4 0.78 80-25-00 10/24/72 37 9 15 4.20 4.46 19 1.0 0.1 100 H-30 5/30/72 45000 14.8 0.78 80-25-00 10/24/72 37 9 15 4.33 4.46 20 1.0 0.1 100 H-30 5/30/72 60000 19.4 0.78 40-50-00 10/24/72 37 9 15 3.74 3.80 21 1.0 0.1 100 H-30 5/30/72 45000 14.8 0.78 40-50-00 10/24/72 37 9 15 3.71 3.80 22 1.0 0.1 100 H-30 5/30/72 60000 19.4 0.78 40-25-00 10/24/72 37 9 15 3.99 3.80 23 1.0 0.1 100 H-30 5/30/72 45000 14.8 0.78 40-25-00 10/24/72 37 9 15 3.78 3.80 24 1.0 0.1 100 H-30 5/30/72 75000 23.9 0.78 80-50-00 10/24/72 37 9 15 3.94 4.45 25 1.0 0.1 100 H-30 6/2/72 60000 19.4 0.79 80-50-00 10/23/72 9 9 15 5.09 4.12 26 1.0 0.1 100 H-30 6/2/72 60000 19.4 0.79 120-50-00 10/23/72 9 9 15 5.13 4.32 27 1.0 0.1 100 H-30 6/2/72 45000 14.8 0.79 0-25-00 10/23/72 9 9 15 4.32 4.32 28 1.0 0.1 100 H-30 6/2/72 60000 19.4 0.79 80-50-00 10/23/72 9 9 15 4.65 4.32 29 1.0 0.1 100 H-30 6/2/72 60000 19.4 0.79 80-50-00 10/23/72 9 9 15 4.90 4.32 30 1.0 0.1 100 H-30 6/2/72 60000 19.4 0.79 80-50-00 10/23/72 9 9 15 5.10 4.12 144 31 1.0 0.1 100 H-30 6/2/72 75000 23.9 0.79 80-50-00 10/23/72 9 9 15 3.97 4.12 A1. Calibrated parcels evaluating grain productivity — Continued. Water- Distance to Slope Plant Seeding Ridge Fertili- Management & Corn Corn Num Corn Planting Harvesting Place CN2 shed Area Outlet Length Popula. rate interval zation observations Grain Grain
WSA CHL SL Variety Date SDW(2) RIN(2) N-P-K Date Code Code Code Obs. Sim. ha km m plt/ha kg/ha m kg/ha Table 2 Table 4 Table 4 t/ha t/ha 32 1.0 0.1 100 H-30 6/7/72 60000 19.4 0.81 80-50-00 11/1/72 44 9 15 3.72 4.16 33 1.0 0.1 100 H-30 6/7/72 60000 19.4 0.81 80-75-00 11/1/72 44 9 15 3.80 4.16 34 1.0 0.1 100 H-30 6/7/72 30000 10.2 0.81 40-25-00 11/1/72 44 9 15 3.59 3.39 35 1.0 0.1 100 H-30 6/7/72 45000 14.8 0.81 40-00-00 11/1/72 44 9 15 3.39 3.39 36 1.0 0.1 100 H-30 6/7/72 45000 14.8 0.81 80-50-00 11/1/72 44 9 15 3.58 4.16 37 1.0 0.1 100 H-30 6/7/72 60000 19.4 0.81 40-50-00 11/1/72 44 9 15 3.26 3.39 38 1.0 0.1 100 H-30 6/7/72 45000 14.8 0.81 40-50-00 11/1/72 44 9 15 3.30 3.39 39 1.0 0.1 100 H-30 6/7/72 60000 19.4 0.81 40-25-00 11/1/72 44 9 15 2.98 3.39 40 1.0 0.1 100 H-30 6/7/72 45000 14.8 0.81 40-25-00 11/1/72 44 9 15 3.25 3.39 41 1.0 0.1 100 H-30 6/5/72 45000 14.8 0.75 40-00-00 10/23/72 49 9 15 3.09 3.29 42 1.0 0.1 100 H-30 6/5/72 60000 19.4 0.75 80-50-00 10/23/72 49 9 15 3.39 3.76 43 1.0 0.1 100 H-30 6/12/73 50000 16.3 0.85 60-40-00 11/4/73 24 9 15 2.39 2.01 44 1.0 0.1 100 H-30 6/12/73 50000 16.3 0.85 60-60-00 11/4/73 24 9 15 2.46 2.01 45 1.0 0.1 100 H-30 6/12/73 50000 16.3 0.85 90-40-00 11/4/73 24 9 15 2.18 2.01 46 1.0 0.1 100 H-30 6/12/73 30000 10.2 0.85 30-20-00 11/4/73 24 9 15 1.85 1.85 47 1.0 0.1 100 H-30 6/12/73 40000 13.2 0.85 60-40-00 11/4/73 24 9 15 2.27 2.01 48 1.0 0.1 100 H-30 6/12/73 50000 16.3 0.85 60-40-00 11/4/73 24 9 15 2.49 2.01 49 1.0 0.1 100 H-30 6/12/73 50000 16.3 0.85 60-20-00 11/4/73 24 9 15 2.28 2.01 50 1.0 0.1 100 H-30 6/12/73 40000 13.2 0.85 60-20-00 11/4/73 24 9 15 2.28 2.01 51 1.0 0.1 100 H-30 6/12/73 40000 13.2 0.85 30-40-00 11/4/73 24 9 15 1.67 1.85 52 1.0 0.1 100 H-30 6/12/73 40000 13.2 0.85 30-20-00 11/4/73 24 9 15 1.81 1.85 53 1.0 0.1 100 H-30 6/12/73 60000 19.4 0.85 60-40-00 11/4/73 24 9 15 2.03 2.01 54 1.0 0.1 100 H-30 6/11/73 30000 10.2 0.8 30-20-00 11/5/73 7 9 15 2.40 1.93 55 1.0 0.1 100 H-30 6/11/73 40000 13.2 0.8 30-00-00 11/5/73 7 9 15 1.91 1.93 56 1.0 0.1 100 H-30 6/11/73 40000 13.2 0.8 0-20-00 11/5/73 7 9 15 2.49 2.13 57 1.0 0.1 100 H-30 6/11/73 50000 16.3 0.8 60-40-00 11/5/73 7 9 15 2.44 2.13 58 1.0 0.1 100 H-30 6/11/73 40000 13.2 0.8 60-40-00 11/5/73 7 9 15 2.55 2.13 59 1.0 0.1 100 H-30 6/11/73 40000 13.2 0.8 60-20-00 11/5/73 7 9 15 2.53 2.13 60 1.0 0.1 100 H-30 5/2/73 50000 16.3 0.88 60-60-00 10/4/73 10 9 13 2.82 3.15 61 1.0 0.1 100 H-30 5/2/73 50000 16.3 0.88 90-40-00 10/4/73 10 9 13 3.56 3.25 62 1.0 0.1 100 H-30 5/2/73 50000 16.3 0.88 60-40-00 10/4/73 10 9 13 2.74 3.25 63 1.0 0.1 100 H-30 5/2/73 40000 13.2 0.88 60-40-00 10/4/73 10 9 13 2.88 3.15 145 A1. Calibrated parcels evaluating grain productivity — Continued. Water- Distance to Slope Plant Seeding Ridge Fertili- Management & Corn Corn Num Corn Planting Harvesting Place CN2 shed Area Outlet Length Popula. rate interval zation observations Grain Grain
WSA CHL SL Variety Date SDW(2) RIN(2) N-P-K Date Code Code Code Obs. Sim. ha km m plt/ha kg/ha m kg/ha Table 2 Table 4 Table 4 t/ha t/ha 64 1.0 0.1 100 H-30 5/2/73 50000 16.3 0.88 60-20-00 10/4/73 10 9 13 2.83 3.15 65 1.0 0.1 100 H-30 5/2/73 40000 13.2 0.88 60-20-00 10/4/73 10 9 13 2.83 3.15 66 1.0 0.1 100 H-30 5/7/73 50000 16.3 0.88 60-60-00 10/12/73 12 9 15 3.69 3.20 67 1.0 0.1 100 H-30 5/7/73 30000 10.2 0.88 30-20-00 10/12/73 12 9 15 3.11 2.90 68 1.0 0.1 100 H-30 5/7/73 40000 13.2 0.88 30-00-00 10/12/73 12 9 15 3.20 2.90 69 1.0 0.1 100 H-30 5/7/73 50000 16.3 0.88 60-40-00 10/12/73 12 9 15 3.93 3.57 70 1.0 0.1 100 H-30 5/7/73 40000 13.2 0.88 60-40-00 10/12/73 12 9 15 3.82 3.20 71 1.0 0.1 100 H-30 5/7/73 50000 16.3 0.88 60-40-00 10/12/73 12 9 15 3.74 3.20 72 1.0 0.1 100 H-30 5/7/73 50000 16.3 0.88 30-40-00 10/12/73 12 9 15 2.78 2.90 73 1.0 0.1 100 H-30 5/7/73 40000 13.2 0.88 30-40-00 10/12/73 12 9 15 3.36 2.90 74 1.0 0.1 100 H-30 5/7/73 40000 13.2 0.88 30-20-00 10/12/73 12 9 15 3.06 2.90 75 1.0 0.1 100 H-30 5/7/73 60000 19.4 0.88 60-40-00 10/12/73 12 9 15 3.85 3.20 76 1.0 0.1 100 H-30 5/4/73 50000 16.3 0.82 60-40-00 10/10/73 36 9 15 5.27 4.63 77 1.0 0.1 100 H-30 5/4/73 50000 16.3 0.82 60-60-00 10/10/73 36 9 15 5.13 4.63 78 1.0 0.1 100 H-30 5/4/73 50000 16.3 0.82 90-40-00 10/10/73 36 9 15 5.70 5.28 79 1.0 0.1 100 H-30 5/4/73 30000 10.2 0.82 30-20-00 10/10/73 36 9 15 3.81 3.85 80 1.0 0.1 100 H-30 5/4/73 40000 13.2 0.82 30-00-00 10/10/73 36 9 15 3.78 3.85 81 1.0 0.1 100 H-30 5/4/73 40000 13.2 0.82 60-40-00 10/10/73 36 9 15 4.79 4.63 82 1.0 0.1 100 H-30 5/4/73 50000 16.3 0.82 60-40-00 10/10/73 36 9 15 4.71 4.63 83 1.0 0.1 100 H-30 5/4/73 50000 16.3 0.82 60-20-00 10/10/73 36 9 15 4.79 4.63 84 1.0 0.1 100 H-30 5/4/73 40000 13.2 0.82 60-20-00 10/10/73 36 9 15 4.45 4.63 85 1.0 0.1 100 H-30 5/4/73 50000 16.3 0.82 30-40-00 10/10/73 36 9 15 4.02 3.85 86 1.0 0.1 100 H-30 5/4/73 40000 13.2 0.82 30-40-00 10/10/73 36 9 15 4.19 3.85 87 1.0 0.1 100 H-30 5/4/73 50000 16.3 0.82 30-20-00 10/10/73 36 9 15 4.07 3.85 88 1.0 0.1 100 H-30 5/4/73 40000 13.2 0.82 30-20-00 10/10/73 36 9 15 4.49 3.85 89 1.0 0.1 100 H-30 5/4/73 60000 19.4 0.82 60-40-00 10/10/73 36 9 15 5.22 4.63 90 1.0 0.1 100 H-30 5/21/73 30000 10.2 0.76 30-20-00 10/28/73 8 9 15 3.77 3.87 91 1.0 0.1 100 H-30 5/21/73 50000 16.3 0.76 60-40-00 10/28/73 8 9 15 4.49 4.69 92 1.0 0.1 100 H-30 5/21/73 50000 16.3 0.76 30-40-00 10/28/73 8 9 15 4.59 3.88 93 1.0 0.1 100 H-30 5/21/73 40000 13.2 0.76 30-40-00 10/28/73 8 9 15 3.30 3.87 94 1.0 0.1 100 H-30 5/21/73 50000 16.3 0.76 30-20-00 10/28/73 8 9 15 4.33 3.88 95 1.0 0.1 100 H-30 5/21/73 40000 13.2 0.76 30-20-00 10/28/73 8 9 15 3.63 3.87 146 A1. Calibrated parcels evaluating grain productivity — Continued. Water- Distance to Slope Plant Seeding Ridge Fertili- Management & Corn Corn Num Corn Planting Harvesting Place CN2 shed Area Outlet Length Popula. rate interval zation observations Grain Grain
WSA CHL SL Variety Date SDW(2) RIN(2) N-P-K Date Code Code Code Obs. Sim. ha km m plt/ha kg/ha m kg/ha Table 2 Table 4 Table 4 t/ha t/ha 96 1.0 0.1 100 H-30 5/21/73 60000 19.4 0.76 60-40-00 10/28/73 8 9 15 4.30 4.70 97 1.0 0.1 100 H-30 6/18/73 30000 10.2 0.72 30-20-00 11/6/73 45 9 15 2.98 3.35 98 1.0 0.1 100 H-30 6/18/73 50000 16.3 0.72 30-40-00 11/6/73 45 9 15 2.93 3.35 99 1.0 0.1 100 H-30 6/18/73 40000 13.2 0.72 30-40-00 11/6/73 45 9 15 2.93 3.35 100 1.0 0.1 100 H-30 6/18/73 50000 16.3 0.72 30-20-00 11/6/73 45 9 15 2.82 3.35 101 1.0 0.1 100 Criollo 3/24/72 80000 25 0.83 120-60-00 10/10/72 47 9 15 5.04 4.05 102 1.0 0.1 100 H-28 4/18/72 50000 16 0.83 60-30-00 11/4/72 14 9 15 5.25 5.34 103 1.0 0.1 100 H-28 4/18/72 50000 16 0.83 120-60-00 11/4/72 14 9 15 6.71 6.75 104 1.0 0.1 100 H-28 4/18/72 65000 20 0.83 120-60-00 11/4/72 14 9 15 6.95 6.73 105 1.0 0.1 100 H-28 4/18/72 80000 25 0.83 120-60-00 11/4/72 14 9 15 7.26 6.73 106 1.0 0.1 100 H-28 4/18/72 65000 20 0.83 120-90-00 11/4/72 14 9 15 7.68 6.73 107 1.0 0.1 100 H-28 4/18/72 65000 20 0.83 150-60-00 11/4/72 14 9 15 7.87 7.27 108 1.0 0.1 100 H-28 4/18/72 50000 16 0.83 90-00-00 11/4/72 14 9 15 6.41 6.09 109 1.0 0.1 100 H-28 4/18/72 35000 11 0.83 90-30-00 11/4/72 14 9 15 5.08 6.09 110 1.0 0.1 100 H-28 4/18/72 50000 16 0.83 90-30-00 11/4/72 14 9 15 7.22 6.09 111 1.0 0.1 100 H-28 4/18/72 65000 20 0.83 90-30-00 11/4/72 14 9 15 5.83 6.07 112 1.0 0.1 100 H-28 4/18/72 50000 16 0.83 90-60-00 11/4/72 14 9 15 6.86 6.09 113 1.0 0.1 100 H-28 4/18/72 50000 16 0.83 120-30-00 11/4/72 14 9 15 6.92 6.75 114 1.0 0.1 100 H-28 4/18/72 65000 20 0.83 120-30-00 11/4/72 14 9 15 6.03 6.73 115 1.0 0.1 100 Criollo 3/24/72 50000 16 0.83 60-30-00 10/10/72 47 9 15 4.44 3.90 116 1.0 0.1 100 H-28 4/20/72 50000 16 0.83 60-30-00 11/6/72 16 9 15 6.37 5.92 117 1.0 0.1 100 H-28 4/20/72 50000 16 0.83 120-60-00 11/6/72 16 9 15 7.38 7.19 118 1.0 0.1 100 H-28 4/20/72 65000 20 0.83 120-60-00 11/6/72 16 9 15 7.47 7.17 119 1.0 0.1 100 H-28 4/20/72 80000 25 0.83 120-60-00 11/6/72 16 9 15 7.23 7.17 120 1.0 0.1 100 H-28 4/20/72 65000 20 0.83 120-90-00 11/6/72 16 9 15 7.85 7.17 121 1.0 0.1 100 H-28 4/20/72 65000 20 0.83 150-60-00 11/6/72 16 9 15 8.00 7.63 122 1.0 0.1 100 H-28 4/20/72 50000 16 0.83 90-00-00 11/6/72 16 9 15 6.43 6.60 123 1.0 0.1 100 H-28 4/20/72 50000 16 0.83 90-30-00 11/6/72 16 9 15 7.17 6.60 124 1.0 0.1 100 H-28 4/20/72 65000 20 0.83 90-30-00 11/6/72 16 9 15 7.14 6.58 125 1.0 0.1 100 H-28 4/20/72 50000 16 0.83 90-60-00 11/6/72 16 9 15 6.44 6.60 126 1.0 0.1 100 H-28 4/20/72 65000 20 0.83 90-60-00 11/6/72 16 9 15 6.74 6.58 127 1.0 0.1 100 H-28 4/20/72 50000 16 0.83 120-30-00 11/6/72 16 9 15 6.89 7.19 147 A1. Calibrated parcels evaluating grain productivity — Continued. Water- Distance to Slope Plant Seeding Ridge Fertili- Management & Corn Corn Num Corn Planting Harvesting Place CN2 shed Area Outlet Length Popula. rate interval zation observations Grain Grain
WSA CHL SL Variety Date SDW(2) RIN(2) N-P-K Date Code Code Code Obs. Sim. ha km m plt/ha kg/ha m kg/ha Table 2 Table 4 Table 4 t/ha t/ha 128 1.0 0.1 100 H-28 4/20/72 65000 20 0.83 120-30-00 11/6/72 16 9 15 7.59 7.17 129 1.0 0.1 100 Criollo 3/24/72 50000 16 0.83 90-00-00 10/10/72 47 9 15 3.72 4.02 130 1.0 0.1 100 Criollo 3/24/72 35000 11 0.83 90-30-00 10/10/72 47 9 15 3.99 4.02 131 1.0 0.1 100 Criollo 4/1/72 50000 16 0.83 60-30-00 10/18/72 22 9 15 5.93 5.11 132 1.0 0.1 100 Criollo 4/1/72 50000 16 0.83 120-60-00 10/18/72 22 9 15 6.48 6.06 133 1.0 0.1 100 Criollo 4/1/72 65000 20 0.83 120-60-00 10/18/72 22 9 15 6.99 6.05 134 1.0 0.1 100 Criollo 4/1/72 80000 25 0.83 120-60-00 10/18/72 22 9 15 7.00 6.06 135 1.0 0.1 100 Criollo 4/1/72 65000 20 0.83 120-90-00 10/18/72 22 9 15 7.02 6.05 136 1.0 0.1 100 Criollo 4/1/72 65000 20 0.83 150-60-00 10/18/72 22 9 15 6.83 6.14 137 1.0 0.1 100 Criollo 4/1/72 50000 16 0.83 90-00-00 10/18/72 22 9 15 5.13 5.71 138 1.0 0.1 100 Criollo 4/1/72 35000 11 0.83 90-30-00 10/18/72 22 9 15 4.82 5.70 139 1.0 0.1 100 Criollo 4/1/72 50000 16 0.83 90-30-00 10/18/72 22 9 15 5.72 5.71 140 1.0 0.1 100 Criollo 4/1/72 65000 20 0.83 90-30-00 10/18/72 22 9 15 5.83 5.69 141 1.0 0.1 100 Criollo 4/1/72 50000 16 0.83 90-60-00 10/18/72 22 9 15 5.75 5.71 142 1.0 0.1 100 Criollo 4/1/72 65000 20 0.83 90-60-00 10/18/72 22 9 15 6.80 5.69 143 1.0 0.1 100 Criollo 4/1/72 50000 16 0.83 120-30-00 10/18/72 22 9 15 5.72 6.06 144 1.0 0.1 100 Criollo 4/1/72 65000 20 0.83 120-30-00 10/18/72 22 9 15 5.83 6.05 145 1.0 0.1 100 Criollo 3/24/72 50000 16 0.83 90-30-00 10/10/72 47 9 15 4.96 4.02 146 1.0 0.1 100 Criollo 4/3/72 65000 20 0.83 150-60-00 10/20/72 19 9 15 5.68 6.13 147 1.0 0.1 100 Criollo 3/24/72 65000 20 0.83 90-30-00 10/10/72 47 9 15 4.43 4.01 148 1.0 0.1 100 Criollo 4/5/72 50000 16 0.83 60-30-00 10/22/72 20 9 15 3.72 4.37 149 1.0 0.1 100 Criollo 4/5/72 50000 16 0.83 120-60-00 10/22/72 20 9 15 4.93 5.07 150 1.0 0.1 100 Criollo 4/5/72 65000 20 0.83 120-60-00 10/22/72 20 9 15 5.01 5.06 151 1.0 0.1 100 Criollo 4/5/72 80000 25 0.83 120-60-00 10/22/72 20 9 15 4.74 5.06 152 1.0 0.1 100 Criollo 4/5/72 65000 20 0.83 120-90-00 10/22/72 20 9 15 5.27 5.06 153 1.0 0.1 100 Criollo 4/5/72 65000 20 0.83 150-60-00 10/22/72 20 9 15 4.63 5.30 154 1.0 0.1 100 Criollo 4/5/72 50000 16 0.83 90-00-00 10/22/72 20 9 15 4.04 4.77 155 1.0 0.1 100 Criollo 4/5/72 35000 11 0.83 90-30-00 10/22/72 20 9 15 4.66 4.77 156 1.0 0.1 100 Criollo 4/5/72 50000 16 0.83 90-30-00 10/22/72 20 9 15 4.09 4.77 157 1.0 0.1 100 Criollo 4/5/72 65000 20 0.83 90-30-00 10/22/72 20 9 15 4.35 4.76 158 1.0 0.1 100 Criollo 4/5/72 50000 16 0.83 90-60-00 10/22/72 20 9 15 4.58 4.77 159 1.0 0.1 100 Criollo 4/5/72 65000 20 0.83 90-60-00 10/22/72 20 9 15 4.89 4.76 148 A1. Calibrated parcels evaluating grain productivity — Continued. Water- Distance to Slope Plant Seeding Ridge Fertili- Management & Corn Corn Num shed Corn Planting Harvesting Place CN2 Outlet Length Popula. rate interval zation observations Grain Grain Area WSA CHL SL Variety Date SDW(2) RIN(2) N-P-K Date Code Code Code Obs. Sim. ha km m plt/ha kg/ha m kg/ha Table 2 Table 4 Table 4 t/ha t/ha 160 1.0 0.1 100 Criollo 4/5/72 50000 16 0.83 120-30-00 10/22/72 20 9 15 4.23 5.07 161 1.0 0.1 100 Criollo 4/5/72 65000 20 0.83 120-30-00 10/22/72 20 9 15 4.34 5.06 162 1.0 0.1 100 Criollo 3/24/72 50000 16 0.83 90-60-00 10/10/72 47 9 15 4.30 4.02 163 1.0 0.1 100 Criollo 3/24/72 65000 20 0.83 90-60-00 10/10/72 47 9 15 4.60 4.01 164 1.0 0.1 100 Criollo 4/12/72 50000 16 0.83 90-00-00 10/29/72 21 9 15 4.93 4.48 165 1.0 0.1 100 Criollo 4/12/72 65000 20 0.83 90-30-00 10/29/72 21 9 15 5.57 4.46 166 1.0 0.1 100 Criollo 3/24/72 50000 16 0.83 120-30-00 10/10/72 47 9 15 4.42 4.05 167 1.0 0.1 100 Criollo 4/13/72 65000 20 0.83 120-90-00 10/29/72 41 9 15 3.53 3.33 168 1.0 0.1 100 Criollo 3/24/72 65000 20 0.83 120-30-00 10/10/72 47 9 15 4.31 4.05 169 1.0 0.1 100 Criollo 4/14/72 35000 11 0.83 0-00-00 10/29/72 41 9 15 3.18 3.18 170 1.0 0.1 100 Criollo 4/14/72 50000 16 0.83 120-60-00 10/31/72 13 9 13 6.03 6.78 171 1.0 0.1 100 Criollo 4/14/72 65000 20 0.83 120-60-00 10/31/72 13 9 13 5.83 6.76 172 1.0 0.1 100 Criollo 4/14/72 80000 25 0.83 120-60-00 10/31/72 13 9 13 6.01 6.76 173 1.0 0.1 100 Criollo 4/14/72 65000 20 0.83 120-90-00 10/31/72 13 9 13 5.95 6.76 174 1.0 0.1 100 Criollo 4/14/72 65000 20 0.83 150-60-00 10/31/72 13 9 13 6.37 7.24 175 1.0 0.1 100 Criollo 4/14/72 50000 16 0.83 90-00-00 10/31/72 13 9 13 5.49 6.14 176 1.0 0.1 100 Criollo 4/14/72 35000 11 0.83 90-30-00 10/31/72 13 9 13 5.26 6.13 177 1.0 0.1 100 Criollo 4/14/72 50000 16 0.83 90-60-00 10/31/72 13 9 13 5.64 6.14 178 1.0 0.1 100 Criollo 4/14/72 50000 16 0.83 120-30-00 10/31/72 13 9 13 5.75 6.78 179 1.0 0.1 100 Criollo 4/14/72 65000 20 0.83 120-30-00 10/31/72 13 9 13 5.67 6.76 180 1.0 0.1 100 Criollo 3/25/72 50000 16 0.83 60-30-00 10/11/72 46 9 15 3.60 3.81 181 1.0 0.1 100 Criollo 3/25/72 50000 16 0.83 120-60-00 10/11/72 46 9 15 4.87 4.01 182 1.0 0.1 100 Criollo 4/14/72 50000 16 0.83 60-30-00 10/31/72 43 9 15 3.08 3.00 183 1.0 0.1 100 Criollo 4/14/72 80000 25 0.83 120-60-00 10/31/72 43 9 15 3.97 3.27 184 1.0 0.1 100 Criollo 4/14/72 35000 11 0.83 90-30-00 10/31/72 43 9 15 3.21 3.23 185 1.0 0.1 100 Criollo 4/14/72 65000 20 0.83 90-30-00 10/31/72 43 9 15 3.61 3.22 186 1.0 0.1 100 Criollo 4/14/72 65000 20 0.83 90-60-00 10/31/72 43 9 15 3.90 3.22 187 1.0 0.1 100 Criollo 3/25/72 50000 16 0.83 90-00-00 10/11/72 46 9 15 4.11 3.96 188 1.0 0.1 100 H-28 4/17/72 50000 16 0.83 60-30-00 11/3/72 42 9 15 1.73 1.78 189 1.0 0.1 100 H-28 4/17/72 65000 20 0.83 120-90-00 11/3/72 42 9 15 2.68 2.23 190 1.0 0.1 100 H-28 4/17/72 50000 16 0.83 90-00-00 11/3/72 42 9 15 1.79 2.08 191 1.0 0.1 100 H-28 4/17/72 35000 11 0.83 90-30-00 11/3/72 42 9 15 2.33 2.08 149 A1. Calibrated parcels evaluating grain productivity — Continued. Water- Distance to Slope Plant Seeding Ridge Fertili- Management & Corn Corn Num Corn Planting Harvesting Place CN2 shed Area Outlet Length Popula. rate interval zation observations Grain Grain
WSA CHL SL Variety Date SDW(2) RIN(2) N-P-K Date Code Code Code Obs. Sim. ha km m plt/ha kg/ha m kg/ha Table 2 Table 4 Table 4 t/ha t/ha 192 1.0 0.1 100 H-28 4/17/72 50000 16 0.83 90-30-00 11/3/72 42 9 15 2.03 2.08 193 1.0 0.1 100 H-28 4/17/72 50000 16 0.83 90-60-00 11/3/72 42 9 15 1.84 2.08 194 1.0 0.1 100 H-28 4/17/72 65000 20 0.83 90-60-00 11/3/72 42 9 15 1.83 2.07 195 1.0 0.1 100 H-28 4/17/72 50000 16 0.83 120-30-00 11/3/72 42 9 15 2.29 2.24 196 1.0 0.1 100 Criollo 3/25/72 35000 11 0.83 90-30-00 10/11/72 46 9 15 4.66 3.96 197 1.0 0.1 100 H-129 4/20/72 50000 16 0.83 60-30-00 11/6/72 5 9 13 4.75 5.25 198 1.0 0.1 100 H-129 4/20/72 50000 16 0.83 120-60-00 11/6/72 5 9 13 6.52 6.60 199 1.0 0.1 100 H-129 4/20/72 65000 20 0.83 120-60-00 11/6/72 5 9 13 5.62 6.59 200 1.0 0.1 100 H-129 4/20/72 80000 25 0.83 120-60-00 11/6/72 5 9 13 5.50 6.59 201 1.0 0.1 100 H-129 4/20/72 65000 20 0.83 120-90-00 11/6/72 5 9 13 6.91 6.59 202 1.0 0.1 100 H-129 4/20/72 65000 20 0.83 150-60-00 11/6/72 5 9 13 6.66 7.09 203 1.0 0.1 100 H-129 4/20/72 50000 16 0.83 90-00-00 11/6/72 5 9 13 5.86 5.98 204 1.0 0.1 100 H-129 4/20/72 35000 11 0.83 90-30-00 11/6/72 5 9 13 5.31 5.98 205 1.0 0.1 100 H-129 4/20/72 50000 16 0.83 90-30-00 11/6/72 5 9 13 5.16 5.98 206 1.0 0.1 100 H-129 4/20/72 65000 20 0.83 90-30-00 11/6/72 5 9 13 5.13 5.96 207 1.0 0.1 100 H-129 4/20/72 50000 16 0.83 90-60-00 11/6/72 5 9 13 5.22 5.98 208 1.0 0.1 100 H-129 4/20/72 50000 16 0.83 120-30-00 11/6/72 5 9 13 6.13 6.60 209 1.0 0.1 100 H-129 4/20/72 65000 20 0.83 120-30-00 11/6/72 5 9 13 5.91 6.59 210 1.0 0.1 100 Criollo 3/25/72 50000 16 0.83 90-30-00 10/11/72 46 9 15 4.59 3.96 211 1.0 0.1 100 Criollo 3/25/72 65000 20 0.83 90-30-00 10/11/72 46 9 15 4.28 3.96 212 1.0 0.1 100 Criollo 4/26/72 50000 16 0.83 60-30-00 11/12/72 6 9 15 5.62 5.57 213 1.0 0.1 100 Criollo 4/26/72 50000 16 0.83 120-60-00 11/12/72 6 9 15 7.46 6.84 214 1.0 0.1 100 Criollo 4/26/72 65000 20 0.83 120-60-00 11/12/72 6 9 15 6.65 6.82 215 1.0 0.1 100 Criollo 4/26/72 80000 25 0.83 120-60-00 11/12/72 6 9 15 5.99 6.82 216 1.0 0.1 100 Criollo 4/26/72 65000 20 0.83 120-90-00 11/12/72 6 9 15 6.76 6.82 217 1.0 0.1 100 Criollo 4/26/72 65000 20 0.83 150-60-00 11/12/72 6 9 15 6.97 7.37 218 1.0 0.1 100 Criollo 4/26/72 50000 16 0.83 90-00-00 11/12/72 6 9 15 5.63 6.24 219 1.0 0.1 100 Criollo 4/26/72 50000 16 0.83 90-30-00 11/12/72 6 9 15 5.98 6.24 220 1.0 0.1 100 Criollo 4/26/72 65000 20 0.83 90-30-00 11/12/72 6 9 15 5.59 6.22 221 1.0 0.1 100 Criollo 4/26/72 50000 16 0.83 90-60-00 11/12/72 6 9 15 5.86 6.24 222 1.0 0.1 100 Criollo 4/26/72 65000 20 0.83 90-60-00 11/12/72 6 9 15 6.34 6.22 223 1.0 0.1 100 Criollo 4/26/72 50000 16 0.83 120-30-00 11/12/72 6 9 15 6.39 6.84 150 A1. Calibrated parcels evaluating grain productivity — Continued. Water- Distance to Slope Plant Seeding Ridge Fertili- Management & Corn Corn Num Corn Planting Harvesting Place CN2 shed Area Outlet Length Popula. rate interval zation observations Grain Grain
WSA CHL SL Variety Date SDW(2) RIN(2) N-P-K Date Code Code Code Obs. Sim. ha km m plt/ha kg/ha m kg/ha Table 2 Table 4 Table 4 t/ha t/ha 224 1.0 0.1 100 Criollo 4/26/72 65000 20 0.83 120-30-00 11/12/72 6 9 15 6.12 6.82 225 1.0 0.1 100 Criollo 3/25/72 65000 20 0.83 90-60-00 10/11/72 46 9 15 4.55 3.96 226 1.0 0.1 100 Criollo 3/25/72 65000 20 0.83 120-30-00 10/11/72 46 9 15 4.84 4.00 227 1.0 0.1 100 Criollo 4/8/72 50000 16 0.83 120-60-00 10/25/72 1 9 13 4.03 4.12 228 1.0 0.1 100 Criollo 4/8/72 65000 20 0.83 120-60-00 10/25/72 1 9 13 4.52 4.11 229 1.0 0.1 100 Criollo 4/8/72 80000 25 0.83 120-60-00 10/25/72 1 9 13 4.61 4.12 230 1.0 0.1 100 Criollo 4/8/72 65000 20 0.83 120-90-00 10/25/72 1 9 13 4.86 4.11 231 1.0 0.1 100 Criollo 4/8/72 65000 20 0.83 150-60-00 10/25/72 1 9 13 4.80 4.53 232 1.0 0.1 100 Criollo 4/8/72 50000 16 0.83 90-00-00 10/25/72 1 9 13 3.23 3.71 233 1.0 0.1 100 Criollo 4/8/72 35000 11 0.83 90-30-00 10/25/72 1 9 13 3.89 3.71 234 1.0 0.1 100 Criollo 4/8/72 50000 16 0.83 90-30-00 10/25/72 1 9 13 3.70 3.71 235 1.0 0.1 100 Criollo 4/8/72 65000 20 0.83 90-30-00 10/25/72 1 9 13 3.86 3.71 236 1.0 0.1 100 Criollo 4/8/72 50000 16 0.83 90-60-00 10/25/72 1 9 13 3.81 3.71 237 1.0 0.1 100 Criollo 4/8/72 65000 20 0.83 90-60-00 10/25/72 1 9 13 3.55 3.71 238 1.0 0.1 100 Criollo 4/8/72 50000 16 0.83 120-30-00 10/25/72 1 9 13 4.24 4.12 239 1.0 0.1 100 Criollo 4/8/72 65000 20 0.83 120-30-00 10/25/72 1 9 13 4.20 4.11 240 1.0 0.1 100 Criollo 4/10/72 50000 16 0.83 60-30-00 10/27/72 2 9 15 2.39 2.84 241 1.0 0.1 100 Criollo 4/10/72 50000 16 0.83 120-60-00 10/27/72 2 9 15 3.84 3.72 242 1.0 0.1 100 Criollo 4/10/72 65000 20 0.83 120-60-00 10/27/72 2 9 15 3.94 3.72 243 1.0 0.1 100 Criollo 4/10/72 80000 25 0.83 120-60-00 10/27/72 2 9 15 3.74 3.72 244 1.0 0.1 100 Criollo 4/10/72 65000 20 0.83 120-90-00 10/27/72 2 9 15 4.45 3.72 245 1.0 0.1 100 Criollo 4/10/72 65000 20 0.83 150-60-00 10/27/72 2 9 15 4.37 3.84 246 1.0 0.1 100 Criollo 4/10/72 50000 16 0.83 90-00-00 10/27/72 2 9 15 3.08 3.38 247 1.0 0.1 100 Criollo 4/10/72 35000 11 0.83 90-30-00 10/27/72 2 9 15 2.98 3.38 248 1.0 0.1 100 Criollo 4/10/72 50000 16 0.83 90-30-00 10/27/72 2 9 15 3.44 3.38 249 1.0 0.1 100 Criollo 4/10/72 65000 20 0.83 90-30-00 10/27/72 2 9 15 3.75 3.36 250 1.0 0.1 100 Criollo 4/10/72 50000 16 0.83 90-60-00 10/27/72 2 9 15 3.43 3.38 251 1.0 0.1 100 Criollo 4/10/72 65000 20 0.83 90-60-00 10/27/72 2 9 15 3.78 3.36 252 1.0 0.1 100 Criollo 4/10/72 65000 20 0.83 120-30-00 10/27/72 2 9 15 4.29 3.72 253 1.0 0.1 100 H-28 4/1/72 50000 16 0.83 60-30-00 10/18/72 4 9 15 4.28 5.06 254 1.0 0.1 100 H-28 4/1/72 65000 20 0.83 120-60-00 10/18/72 4 9 15 4.90 5.36 255 1.0 0.1 100 H-28 4/1/72 65000 20 0.83 120-90-00 10/18/72 4 9 15 5.93 5.36 151 A1. Calibrated parcels evaluating grain productivity — Continued. Water- Distance to Slope Plant Seeding Ridge Fertili- Management & Corn Corn Num Corn Planting Harvesting Place CN2 shed Area Outlet Length Popula. rate interval zation observations Grain Grain
WSA CHL SL Variety Date SDW(2) RIN(2) N-P-K Date Code Code Code Obs. Sim. ha km m plt/ha kg/ha m kg/ha Table 2 Table 4 Table 4 t/ha t/ha 256 1.0 0.1 100 H-28 4/1/72 65000 20 0.83 150-60-00 10/18/72 4 9 15 5.09 5.36 257 1.0 0.1 100 H-28 4/1/72 50000 16 0.83 90-60-00 10/18/72 4 9 15 4.67 5.26 258 1.0 0.1 100 H-28 4/1/72 65000 20 0.83 90-60-00 10/18/72 4 9 15 4.47 5.26 259 1.0 0.1 100 H-28 4/1/72 65000 20 0.83 120-30-00 10/18/72 4 9 15 4.51 5.36 260 1.0 0.1 100 H-28 4/4/72 50000 16 0.83 120-60-00 10/21/72 3 9 15 5.78 5.13 261 1.0 0.1 100 H-28 4/4/72 50000 16 0.83 90-00-00 10/21/72 3 9 15 5.15 4.97 262 1.0 0.1 100 H-28 4/4/72 50000 16 0.83 90-60-00 10/21/72 3 9 15 6.09 4.97 263 1.0 0.1 100 H-28 4/4/72 50000 16 0.83 120-30-00 10/21/72 3 9 15 6.28 5.13 264 1.0 0.1 100 H-28 4/13/72 35000 11 0.83 0-00-00 10/30/72 15 9 15 6.87 7.96 265 1.0 0.1 100 H-28 4/13/72 35000 11 0.83 90-30-00 10/30/72 15 9 15 7.50 6.42 266 1.0 0.1 100 H-32 6/2/88 82600 25 0.85 144-83-00 10/18/88 40 9 16 3.55 4.04 267 1.0 0.1 100 H-32 6/2/88 34000 11 0.85 60-34-00 10/19/88 40 9 16 3.13 3.66
268 1.0 0.1 100 H-30 6/9/72 60000 19.4 0.83 80-50-00 11/4/72 23 8 (10 t/ha) 11 3.36 4.06 269 1.0 0.1 100 H-30 5/11/72 60000 19.4 0.76 80-50-00 10/5/72 50 8 (10 t/ha) 12 4.22 3.96 270 1.0 0.1 100 H-30 6/7/72 60000 19.4 0.81 80-50-00 11/1/72 44 8 (10 t/ha) 12 3.53 4.29 271 1.0 0.1 100 H-30 5/9/73 50000 16.3 0.8 60-40-00 10/16/73 17 8 (10 t/ha) 12 5.72 5.74 272 1.0 0.1 100 H-30 5/4/73 50000 16.3 0.82 60-40-00 10/10/73 36 8 (10 t/ha) 12 6.56 6.08 273 1.0 0.1 100 H-28 4/20/72 65000 20 0.83 120-60-00 11/6/72 16 8 (10 t/ha) 12 8.51 7.95 274 1.0 0.1 100 Criollo 4/3/72 65000 20 0.83 120-60-00 10/20/72 19 8 (10 t/ha) 12 7.16 6.13 275 1.0 0.1 100 Criollo 4/14/72 65000 20 0.83 120-60-00 10/31/72 13 8 (10 t/ha) 11 8.70 7.83 276 1.0 0.1 100 H-129 4/20/72 65000 20 0.83 120-60-00 11/6/72 5 8 (10 t/ha) 11 7.47 7.64 277 1.0 0.1 100 Criollo 4/26/72 65000 20 0.83 120-60-00 11/12/72 6 8 (10 t/ha) 12 7.83 7.91 278 1.0 0.1 100 Criollo 4/6/72 65000 20 0.83 120-60-00 10/23/72 48 8 (10 t/ha) 11 4.03 3.81 279 1.0 0.1 100 H-28 4/1/72 65000 20 0.83 120-60-00 10/18/72 4 8 (10 t/ha) 12 5.71 5.36
280 1.0 0.1 100 H-149 4/9/90 65000 20 0.8 150-50-00 11/28/90 18 6 (10 t/ha) 9 7.09 6.19 281 1.0 0.1 100 H-149 4/9/90 65000 20 0.8 150-50-00 11/28/90 18 6 (20 t/ha) 9 7.38 6.44
282 1.0 0.1 100 H-149 4/9/90 65000 20 0.8 150-50-00 11/28/90 18 7 (5 t/ha, 0 t/ha) 10 6.15 5.93 283 1.0 0.1 100 H-149 4/9/90 65000 20 0.8 150-50-00 11/28/90 18 7 (5 t/ha, 10 t/ha) 10 7.17 6.19 284 1.0 0.1 100 H-149 4/9/90 65000 20 0.8 150-50-00 11/28/90 18 7 ( 5 t/ha, 20 t/ha) 10 6.37 6.44 152 A1. Calibrated parcels evaluating grain productivity — Continued. Water- Distance to Slope Plant Seeding Ridge Fertili- Management & Corn Corn Num shed Corn Planting Harvesting Place CN2 Outlet Length Popula. rate interval zation observations Grain Grain Area WSA CHL SL Variety Date SDW(2) RIN(2) N-P-K Date Code Code Code Obs. Sim. ha km m plt/ha kg/ha m kg/ha Table 2 Table 4 Table 4 t/ha t/ha 285 1.0 0.1 100 H-149 4/9/90 65000 20 0.8 150-50-00 11/28/90 18 5 8 6.68 5.93 286 1.0 0.1 100 H-30 3/9/92 30000 10 0.8 80-40-00 9/29/92 33 5 (70% FC-U) 8 7.89 7.33 287 1.0 0.1 100 H-30 3/9/92 30000 10 0.8 80-40-00 9/29/92 33 5 (45% FC-U) 8 7.68 7.33 288 1.0 0.1 100 H-30 3/9/92 30000 10 0.8 80-40-00 9/29/92 33 5 (20% FC-U) 8 7.21 7.33
289 1.0 0.1 100 H-32 7/3/89 43000 13.5 0.8 39-50-00 11/15/89 39 4 (1.25 m) 7 2.41 2.54 290 1.0 0.1 100 H-32 7/3/89 62000 19 0.8 55-71-00 11/15/89 39 4 (1.25 m) 7 2.39 2.66 291 1.0 0.1 100 H-32 7/3/89 80000 24.5 0.8 72-92-00 11/15/89 39 4 (1.25mm) 7 2.72 2.67
292 1.0 0.1 100 H-32 7/3/89 43000 13.5 0.8 39-50-00 11/15/89 39 4 (2.50 m) 7 2.32 2.54 293 1.0 0.1 100 H-32 7/3/89 62000 19 0.8 55-71-00 11/15/89 39 4 (2.50 m) 7 2.30 2.66 294 1.0 0.1 100 H-32 7/3/89 80000 24.5 0.8 72-92-00 11/15/89 39 4 (2.50 m) 7 2.64 2.67
295 0.01 0.1 7 H-30 5/25/77 67000 21 0.8 80-60-00 11/16/77 32 13 (5 t/ha) 23 3.93 4.73 296 0.01 0.1 7 H-30 5/25/77 67000 21 0.8 80-60-00 11/16/77 32 13 (10 t/ha) 23 4.08 4.73 297 0.01 0.1 7 H-30 6/9/77 67000 21 0.8 80-60-00 12/4/77 32 13 (5 t/ha) 23 3.32 3.96
298 0.0126 0.1 7 H-30 5/28/80 45700 15 0.9 80-60-40 11/18/80 27 14 (5 t/ha) 24 3.65 3.73 299 0.0126 0.1 7 H-30 5/28/80 54700 17 0.9 80-60-40 11/18/80 27 14 (5 t/ha) 24 3.71 3.74 300 0.0126 0.1 7 H-30 5/28/80 45700 15 0.9 80-60-40 11/18/80 27 11 (5 t/ha) 21 3.74 3.80 301 0.0126 0.1 7 H-30 5/28/80 54700 17 0.9 80-60-40 11/18/80 27 11 (5 t/ha) 21 3.14 3.80 302 0.0126 0.1 7 H-30 5/28/80 45700 15 0.9 80-60-40 11/18/80 27 11 (10 t/ha) 21 3.62 3.97 303 0.0126 0.1 7 H-30 5/28/80 54700 17 0.9 80-60-40 11/18/80 27 11 (10 t/ha) 21 3.42 3.94
304 0.0126 0.1 7 H-30 5/28/80 45700 15 0.9 80-60-40 11/18/80 27 15 (5 t/ha, 5 t/ha) 25 3.39 3.88 305 0.0126 0.1 7 H-30 5/28/80 54700 17 0.9 80-60-40 11/18/80 27 15 (5 t/ha, 5 t/ha) 25 3.17 3.89 306 0.0126 0.1 7 H-30 5/28/80 45700 15 0.9 80-60-40 11/18/80 27 15 (5 t/ha, 10 t/ha) 25 3.26 4.03 307 0.0126 0.1 7 H-30 5/28/80 54700 17 0.9 80-60-40 11/18/80 27 15 (5 t/ha, 10 t/ha) 25 3.42 4.04 308 0.0126 0.1 7 H-30 5/28/80 45700 15 0.9 80-60-40 11/18/80 27 16 26 3.36 3.68 153 A2. Calibrated plots evaluating soil erosion and runoff. Water- Distance Slope Plant Seeding Ridge Fertili- Management & Corn Corn Num shed Corn Planting Harvesting Place CN2 USLE USLE Q Q to Outlet Length Popula. rate interval zation observations Grain Grain Area WSA CHL SL Variety Date SDW(2) RIN(2) N-P-K Date Code Code Code Obs. Sim. Obs. Sim. Obs. Sim. ha km m plt/ha kg/ha m kg/ha Table 2 Table 4 Table 4 t/ha t/ha mm mm 309 0.005 0.03 25 Criolla 6/20/90 50000 15.5 0.8 80-45-00 10/25/90 30 1 3 2.75 3.61 80.86 87.15 310 0.005 0.03 25 H-30 6/6/84 60000 19 0.8 80-40-00 11/15/84 28 1 1 2.26 2.74 2.23 3.30 65.62 72.36 311 0.005 0.03 25 H-30 5/19/85 60000 19 0.8 80-40-00 11/15/85 28 1 2 3.65 3.18 64.70 55.88 312 0.7013 0.1 74 H-30 5/21/83 42000 16 0.92 80-40-00 12/20/83 26 1 3 2.13 2.68 29.39 25.18 313 0.12 0.1 20 H-28 6/22/76 60000 19 0.92 80-40-00 12/21/76 31 1 3 2.43 2.83 0.24 0.38 314 0.12 0.1 20 H-30 6/25/77 60000 24 0.92 80-40-00 12/13/77 31 2 (10 t/ha) 6 0.87 0.26 315 0.703 0.1 74 H-28 6/25/76 60000 19 0.92 80-40-00 12/20/76 25 1 4 2.61 2.24 0.40 0.58 316 0.703 0.1 74 H-28 6/25/76 60000 19 0.92 80-40-00 12/20/76 25 1 3 2.90 2.20 0.36 0.58 317 0.703 0.1 74 H-30 6/25/77 60000 19 0.92 80-40-00 12/13/77 25 1 4 0.49 0.30 318 0.703 0.1 74 H-30 6/25/77 60000 19 0.92 80-40-00 12/13/77 25 1 3 2.12 2.92 0.43 0.30 319 0.005 0.03 25 H-30 6/29/83 60000 19 0.8 80-40-00 11/15/83 28 1 13 3.19 3.64 53.36 46.77
320 0.4464 0.1 25 H-28 6/22/76 60000 19 0.92 80-40-00 12/21/76 31 3 (wide base) 6 2.36 2.76 0.06 0.27 321 0.2592 0.1 20 H-28 6/22/76 60000 19 0.92 80-40-00 12/21/76 31 3 (alternated banks) 6 2.80 2.76 0.27 0.25 322 0.3006 0.1 20 H-28 6/22/76 60000 19 0.92 80-40-00 12/21/76 31 3 (Zingg) 6 0.24 0.25 323 0.3012 0.1 12.5 H-28 6/22/76 60000 19 0.92 80-40-00 12/21/76 31 3 (1.6 % declined bank) 6 2.57 2.76 0.38 0.21 3 (SARH, succesive 324 0.703 0.1 25.76 H-28 6/25/76 60000 19 0.92 80-40-00 12/20/76 25 6 2.73 2.09 0.29 0.27 formation) 3 (CP, succesive 325 0.703 0.1 34.96 H-28 6/25/76 60000 19 0.92 80-40-00 12/20/76 25 6 2.71 2.09 0.27 0.30 formation) 326 0.703 0.1 22.4 H-28 6/25/76 60000 19 0.92 80-40-00 12/20/76 25 3 (leveled bank) 6 2.13 2.09 0.06 0.25 327 0.703 0.1 22.4 H-30 6/25/77 60000 19 0.92 80-40-00 12/13/77 25 3 (leveled bank) 6 2.34 2.88 0.07 0.13
328 0.005 0.025 25 H-30 4/19/81 60000 19 0.8 90-45-00 10/15/81 35 9 17 5.67 6.69 167.60 155.60 329 0.005 0.025 25 H-30 4/17/81 60000 19 0.8 90-45-00 10/15/81 38 9 14 2.94 3.05 75.25 68.79 330 0.01 0.03 25 H-30 6/5/87 60000 19 0.8 80-40-00 11/15/87 29 9 16 8.22 8.32 104.60 101.46 331 0.01 0.03 25 H-30 6/5/87 65000 20 0.8 80-40-00 11/15/87 29 9 16 5.77 5.88 102.90 97.96 332 0.01 0.03 25 H-30 6/5/87 70000 22 0.8 80-40-00 11/15/87 29 9 16 5.73 5.88 97.20 97.96 333 0.01 0.03 25 H-30 6/5/87 75000 23 0.8 80-40-00 11/15/87 29 9 14 3.93 4.42 67.50 58.86
334 0.703 0.1 74 H-30 5/21/83 43000 16 0.92 80-40-00 12/20/83 26 9 15 3.27 3.98 2.79 3.33 34.68 154 30.91 A2. Calibrated plots evaluating soil erosion and runoff — Continued. Water- Distance to Slope Plant Seeding Ridge Fertili- Management & Corn Corn Num shed Corn Planting Harvesting Place CN2 USLE USLE Q Q Outlet Length Popula. rate interval zation observations Grain Grain Area WSA CHL SL Variety Date SDW(2) RIN(2) N-P-K Date Code Code Code Obs. Sim. Obs. Sim. Obs. Sim. ha km m plt/ha kg/ha m kg/ha Table 2 Table 4 Table 4 t/ha t/ha t/ha t/ha mm mm
335 0.3012 0.1 12.5 H-28 6/22/76 60000 19 0.92 80-40-00 12/21/76 31 10 (leveled bank) 19 2.47 2.76 0.67 0.64 10 (wide base, beef 336 0.4464 0.1 25 H-30 6/25/77 60000 24 0.92 80-40-00 12/13/77 31 19 0.26 0.43 manure) 10 (leveled bank, beef 337 0.3012 0.1 12.5 H-30 6/25/77 60000 24 0.92 80-40-00 12/13/77 31 19 1.15 2.10 manure) 10 (alternated banks, 338 0.2592 0.1 20 H-30 6/25/77 60000 24 0.92 80-40-00 12/13/77 31 19 0.42 0.40 beef m) 10 (Zingg, beef 339 0.3006 0.1 20 H-30 6/25/77 60000 24 0.92 80-40-00 12/13/77 31 19 0.74 0.40 manure) 10 (1.6 % declined 340 0.3012 0.1 12.5 H-30 6/25/77 60000 24 0.92 80-40-00 12/13/77 31 19 1.23 2.10 bank, beef m.) 10 (SARH, succesive 341 0.703 0.1 25.76 H-30 6/25/77 60000 19 0.92 80-40-00 12/13/77 25 19 0.34 0.42 formation) 10 (CP, succesive 342 0.703 0.1 34.96 H-30 6/25/77 60000 19 0.92 80-40-00 12/13/77 25 19 2.01 2.88 0.34 0.47 formation) 10 (SARH, succesive 343 0.703 0.1 25.76 H-30 5/21/83 39000 16 0.92 80-40-00 12/20/83 26 20 2.99 3.97 2.17 1.54 29.74 24.28 formation) 10 (CP, succesive 344 0.703 0.1 34.96 H-30 5/21/83 40000 16 0.92 80-40-00 12/20/83 26 18 3.55 3.98 1.31 1.71 14.42 13.69 formation) 345 0.703 0.1 22.4 H-30 5/21/83 42000 16 0.92 80-40-00 12/20/83 26 10 (leveled bank) 20 3.34 3.96 1.26 1.46 30.86 27.94
346 0.005 0.03 25 Criolla 6/20/90 50000 15.5 0.8 80-45-00 10/25/90 30 12 22 2.69 3.31 143.16 154.61
347 0.005 0.03 25 Criolla 6/20/90 50000 15.5 0.8 80-45-00 10/25/90 30 17 28 2.05 1.70 98.50 93.03 348 0.703 0.1 74 H-30 5/21/83 48000 16 0.92 80-40-00 10/21/83 26 17 27 0.94 1.34 33.35 32.36 349 0.703 0.1 74 H-30 5/21/83 51000 16 0.92 80-40-00 10/21/83 26 17 27 3.31 3.95 1.20 1.34 35.90 31.64
18 (SARH, succesive 350 0.703 0.1 25.76 H-30 5/21/83 42000 16 0.92 80-40-00 10/21/83 26 30 1.11 0.77 20.18 18.84 formation) 18 (CP, succesive 351 0.703 0.1 34.96 H-30 5/21/83 47000 16 0.92 80-40-00 10/21/83 26 29 3.05 3.93 0.81 0.85 18.18 16.56 formation) 352 0.703 0.1 22.4 H-30 5/21/83 47000 16 0.92 80-40-00 10/21/83 26 18 (bank) 31 3.42 3.94 0.61 0.73 23.19 21.41 155 A3. Soil parameters by layer for plot calibration. Soil Profile Layer Z BD U FC SAN SIL WN pH SMB CBN CAC CEC ROK WNO3 AP RSD BDD SC Salb m t/m3 m/m m/m % % g/t cmol/kg % % cmol/kg % g/m3 g/t t/ha t/m3 mm/h Amecameca 1 0.01 1.54 0.050 0.190 83.0 14.0 390 6.0 0.45 4.5 2 8 61.21 0.15 2 0.30 1.54 0.050 0.190 83.0 14.0 390 6.0 0.45 4.5 2 8 61.21 3 0.41 1.45 0.136 0.279 64.1 17.6 520 7.9 14.8 0.41 14.8 2 5 10 0.248 1.55 4 0.48 1.45 0.134 0.275 62.3 19.6 630 8.0 14.8 0.52 1 14.8 2 5 10 0.111 1.55 5 0.58 1.45 0.155 0.291 59.1 17.7 670 8.2 13.2 0.54 10 13.2 2 5 10 0.042 1.55 6 0.76 1.50 0.163 0.293 60.2 14.3 380 8.4 8.7 0.31 19 8.7 2 5 10 0.013 1.60 Amecameca II 1 0.01 1.60 0.030 0.110 89.0 8.0 350 6.0 0.41 7.3 2 10 210.06 0.13 2 0.30 1.60 0.030 0.110 89.0 8.0 350 6.0 0.41 7.3 2 10 210.06 3 0.41 1.45 0.136 0.279 64.1 17.6 520 7.9 14.8 0.41 14.8 2 5 10 0.248 1.55 4 0.48 1.45 0.134 0.275 62.3 19.6 630 8.0 14.8 0.52 1 14.8 2 5 10 0.111 1.55 5 0.58 1.45 0.155 0.291 59.1 17.7 670 8.2 13.2 0.54 10 13.2 2 5 10 0.042 1.55 6 0.76 1.50 0.163 0.293 60.2 14.3 380 8.4 8.7 0.31 19 8.7 2 5 10 0.013 1.60 1 0.01 1.39 0.095 0.240 52.0 33.0 1455 5.6 1.69 54.2 2 4 19.56 0.13 Amecameca III 2 0.30 1.39 0.095 0.240 52.0 33.0 1455 5.6 1.69 54.2 2 4 19.56 3 0.51 1.50 0.227 0.319 60.6 25.6 660 6.0 6.3 0.53 11.2 2 5 10 0.373 1.60 4 0.75 1.36 0.236 0.362 59.2 26.2 460 6.1 6.9 0.48 11.8 2 5 3 0.161 1.46 5 0.99 1.36 0.236 0.362 58.2 26.6 460 6.2 7.4 0.44 12.1 2 5 0.161 1.46 6 1.37 1.50 0.213 0.315 57.0 27.0 300 6.3 7.9 0.40 12.6 2 5 0.016 1.60 1 0.01 1.30 0.110 0.260 45.0 35.0 1950 5.8 2.27 26.3 2 1 13.21 0.13 Amecameca IV 2 0.30 1.30 0.110 0.260 45.0 35.0 1950 5.8 2.27 26.3 2 1 13.21 3 0.51 1.50 0.227 0.319 60.6 25.6 660 6.0 6.3 0.53 11.2 2 5 10 0.373 1.60 4 0.75 1.36 0.236 0.362 59.2 26.2 460 6.1 6.9 0.48 11.8 2 5 3 0.161 1.46 5 0.99 1.36 0.236 0.362 58.2 26.6 460 6.2 7.4 0.44 12.1 2 5 0.161 1.46 6 1.37 1.50 0.213 0.315 57.0 27.0 300 6.3 7.9 0.40 12.6 2 5 0.016 1.60
Z = Depth from the surface to the bottom of the soil layer CBN = Organic carbon (%) (m) CAC = Calcium carbonate (%) BD = Bulk density of the soil layer (t/m3) CEC = Cation exchange capacity (cmol/kg) U = Wilting point (1500 kPa) (m/m) ROK = Coarse fragment content (%) FC = Field capacity1 (33kPa) (m/m) WNO3= Initial Nitrate concentration (g/t) SAN = Sand content (%) AP = Labile P concentration (g/t) SIL = Silt content (%) RSD = Crop residue1 (t/ha) WN= Organic N concentration (g/t) BDD = Bulk density (oven dry) (t/m3) PH = Soil pH SC = Saturated conductivity (mm/h) SMB = Sum of bases (cmol/kg) SALB = Soil albedo 156 A3. Soil parameters by layer for plot calibration — Continued. Soil Profile Layer Z BD U FC SAN SIL WN pH SMB CBN CAC CEC Rock WNO3 AP RSD BDD SC Salb m t/m3 m/m m/m % % g/t cmol/kg % % cmol/kg % g/m3 g/t t/ha t/m3 mm/h Chalco 1 0.01 1.60 0.030 0.110 92.0 5.0 450 6.9 0.52 8.7 2 5 210.06 0.13 2 0.30 1.60 0.030 0.110 92.0 5.0 450 6.9 0.52 8.7 2 5 210.06 3 0.58 1.46 0.118 0.265 61.9 17.7 110 6.1 11.0 0.20 6.5 2 5 3 0.079 1.56 4 0.84 1.53 0.095 0.236 62.7 15.8 60 6.2 12.1 0.14 6.8 2 5 0.005 1.64 5 1.12 1.60 0.094 0.219 63.4 14.4 40 6.3 12.9 0.09 7.0 2 5 0.001 1.71 6 1.52 1.60 0.062 0.204 64.1 12.9 20 6.4 13.7 0.04 7.3 2 10 0.001 1.71 Chalco II 1 0.01 1.48 0.080 0.220 72.0 23.0 850 6.6 0.99 18.6 2 2 25.91 0.13 2 0.30 1.48 0.080 0.220 72.0 23.0 850 6.6 0.99 18.6 2 2 25.91 3 0.58 1.46 0.118 0.265 61.9 17.7 110 6.1 11.0 0.20 6.5 2 5 3 0.079 1.56 4 0.84 1.53 0.095 0.236 62.7 15.8 60 6.2 12.1 0.14 6.8 2 5 0.005 1.64 5 1.12 1.60 0.094 0.219 63.4 14.4 40 6.3 12.9 0.09 7.0 2 5 0.001 1.71 6 1.52 1.60 0.062 0.204 64.1 12.9 20 6.4 13.7 0.04 7.3 2 10 0.001 1.71 Chicoloapan 1 0.01 1.15 0.161 0.300 25.0 62.0 963 6.9 1.12 48.1 0 6.86 0.14 2 0.20 1.15 0.161 0.300 25.0 62.0 963 6.9 1.12 48.1 0 6.86 3 0.40 1.25 0.250 0.390 25.5 39.5 660 6.7 0.77 48.1 0 2.29 4 0.70 1.30 0.268 0.408 35.5 37.0 560 7.0 0.65 48.1 0 13.21 5 1.00 1.30 0.270 0.410 35.5 39.5 715 6.9 0.83 48.1 0 13.21 6 1.30 1.40 0.053 0.333 51.0 32.0 615 7.4 0.72 48.1 0 21.00 Chiconcuac 1 0.01 1.30 0.163 0.304 41.0 45.0 494 7.7 0.57 48.2 1 13.21 0.13 2 0.20 1.30 0.163 0.304 41.0 45.0 494 7.7 0.57 48.2 1 13.21 3 0.40 1.30 0.081 0.351 46.0 39.0 885 8.3 1.03 48.2 1 13.21 4 0.70 1.30 0.080 0.370 47.5 35.0 515 9.1 0.60 48.2 1 13.21 5 1.00 1.40 0.083 0.323 52.0 33.0 345 9.0 0.40 48.2 1 20.00 Chiconcuac II 1 0.01 1.30 0.278 0.530 28.5 47.0 519 6.8 0.60 35.6 1 13.21 0.13 2 0.20 1.30 0.278 0.530 28.5 47.0 519 6.8 0.60 35.6 1 13.21 3 0.40 1.25 0.250 0.390 25.5 39.5 660 6.7 0.77 1 2.29 4 0.70 1.30 0.268 0.408 35.5 37.0 560 7.0 0.65 1 13.21 5 1.00 1.30 0.250 0.410 35.5 39.5 715 6.9 0.83 1 13.21 6 1.30 1.30 0.053 0.333 51.0 32.0 615 7.4 0.72 1 13.21 Chimalhuacán 1 0.01 1.60 0.051 0.094 92.0 6.7 259 7.1 0.30 18.8 0 210.06 0.13 2 0.20 1.60 0.051 0.094 92.0 6.7 259 7.1 0.30 18.8 0 210.06 3 0.40 1.60 0.026 0.096 89.5 5.5 215 7.5 0.25 18.8 0 210.06 4 0.70 1.60 0.031 0.111 92.0 3.0 145 8.0 0.17 18.8 0 121.06 5 1.00 1.60 0.039 0.129 89.5 5.5 70 8.2 0.08 18.8 0 121.06 6 1.30 1.60 0.036 0.136 92.0 5.5 160 8.6 0.19 18.8 0 121.06 Coatlinchán 1 0.01 1.54 0.194 0.361 80.0 15.2 324 7.2 0.38 21.3 1 61.21 0.16 2 0.20 1.54 0.194 0.361 80.0 15.2 324 7.2 0.38 21.3 1 61.21 3 0.40 1.54 0.048 0.128 77.0 19.0 435 7.7 0.51 1 61.21 4 0.70 1.54 0.034 0.144 82.0 8.0 400 8.2 0.47 1 61.21 Coatlinchán II 1 0.01 1.48 0.066 0.127 72.0 23.2 534 7.2 0.62 23.2 0 25.91 0.13 2 0.20 1.48 0.066 0.127 72.0 23.2 534 7.2 0.62 23.2 0 25.91
3 0.40 1.54 0.038 0.118 82.0 10.5 275 7.4 0.32 23.2 0 61.21 157 4 0.70 1.54 0.044 0.264 84.5 11.0 400 8.2 0.47 23.2 0 61.21 A3. Soil parameters by layer for plot calibration — Continued. Soil Profile Layer Z BD U FC SAN SIL WN pH SMB CBN CAC CEC Rock WNO3 AP RSD BDD SC Salb m t/m3 m/m m/m % % g/t cmol/kg % % cmol/kg % g/m3 g/t t/ha t/m3 mm/h Cocotitlán 1 0.01 1.54 0.050 0.190 77.0 17.0 555 6.7 0.65 15.0 2 4 61.21 0.15 2 0.30 1.54 0.050 0.190 77.0 17.0 555 6.7 0.65 15.0 2 4 61.21 3 0.58 1.46 0.118 0.265 61.9 17.7 110 6.1 11.0 0.20 6.5 2 5 3 0.079 1.56 4 0.84 1.53 0.095 0.236 62.7 15.8 60 6.2 12.1 0.14 6.8 2 5 0.005 1.64 5 1.12 1.60 0.094 0.219 63.4 14.4 40 6.3 12.9 0.09 7.0 2 5 0.001 1.71 6 1.52 1.60 0.062 0.204 64.1 12.9 20 6.4 13.7 0.04 7.3 2 10 0.001 1.71 Cocotitlán II 1 0.01 1.30 0.110 0.260 49.0 41.0 750 6.4 0.87 15.5 2 2 13.21 0.13 2 0.30 1.30 0.110 0.260 49.0 41.0 750 6.4 0.87 15.5 2 2 13.21 3 0.58 1.46 0.118 0.265 61.9 17.7 110 6.1 11.0 0.20 6.5 2 5 3 0.079 1.56 4 0.84 1.53 0.095 0.236 62.7 15.8 60 6.2 12.1 0.14 6.8 2 5 0.005 1.64 5 1.12 1.60 0.094 0.219 63.4 14.4 40 6.3 12.9 0.09 7.0 2 5 0.001 1.71 6 1.52 1.60 0.062 0.204 64.1 12.9 20 6.4 13.7 0.04 7.3 2 10 0.001 1.71 Cocotitlán III 1 0.01 1.48 0.080 0.220 59.0 32.0 1050 6.8 1.22 15.9 2 3 25.91 0.13 2 0.30 1.48 0.080 0.220 59.0 32.0 1050 6.8 1.22 15.9 2 3 25.91 3 0.58 1.46 0.118 0.265 61.9 17.7 110 6.1 11.0 0.20 6.5 2 5 3 0.079 1.56 4 0.84 1.53 0.095 0.236 62.7 15.8 60 6.2 12.1 0.14 6.8 2 5 0.005 1.64 5 1.12 1.60 0.094 0.219 63.4 14.4 40 6.3 12.9 0.09 7.0 2 5 0.001 1.71 6 1.52 1.60 0.062 0.204 64.1 12.9 20 6.4 13.7 0.04 7.3 2 10 0.001 1.71 Cocotitlán IV 1 0.01 1.30 0.110 0.260 42.0 48.0 2080 7.5 2.42 3.1 2 1 13.21 0.13 2 0.30 1.30 0.110 0.260 42.0 48.0 2080 7.5 2.42 3.1 2 1 13.21 3 0.58 1.46 0.118 0.265 61.9 17.7 110 6.1 11.0 0.20 6.5 2 5 3 0.079 1.56 4 0.84 1.53 0.095 0.236 62.7 15.8 60 6.2 12.1 0.14 6.8 2 5 0.005 1.64 5 1.12 1.60 0.094 0.219 63.4 14.4 40 6.3 12.9 0.09 7.0 2 5 0.001 1.71 6 1.52 1.60 0.062 0.204 64.1 12.9 20 6.4 13.7 0.04 7.3 2 10 0.001 1.71 Colonia 1 0.01 1.48 0.090 0.171 63.0 34.0 599 6.3 0.70 35.2 0 25.91 0.13 2 0.20 1.48 0.090 0.171 63.0 34.0 599 6.3 0.70 35.2 0 25.91 3 0.40 1.48 0.050 0.190 64.0 16.0 615 6.6 0.72 35.2 0 25.91 4 0.70 1.48 0.088 0.308 55.0 26.0 275 6.7 0.32 35.2 0 25.91 INIFAP 1 0.01 1.17 0.136 0.307 18.7 26.4 755 7.1 6.3 0.88 22.9 10 1 1.280 1.21 0.51 0.12 2 0.20 1.17 0.136 0.307 18.7 26.4 755 7.1 6.3 0.88 22.9 10 1 1.280 1.21 0.51 3 0.40 1.20 0.146 0.305 17.2 25.9 570 7.2 6.3 0.66 28.4 5 1 0.391 1.35 0.51 4 0.60 1.28 0.141 0.283 14.9 26.5 475 7.4 6.6 0.55 30.8 5 1 0.202 1.46 0.51 5 0.86 1.11 0.156 0.361 49.5 19.1 2050 7.7 31.9 2.38 5 32.5 5 0.056 1.19 8.00 6 1.17 1.24 0.145 0.327 50.9 17.9 280 7.9 32.7 0.40 11 34.2 5 0.008 1.33 8.00 7 1.52 1.33 0.153 0.294 52.0 16.9 100 8.1 33.4 0.28 18 35.6 5 0.001 1.42 8.00 Juchitepec 1 0.01 1.48 0.080 0.220 70.0 22.0 575 6.1 0.67 9.7 2 1 25.91 0.13 2 0.30 1.48 0.080 0.220 70.0 22.0 575 6.1 0.67 9.7 2 1 25.91 3 0.64 1.63 0.217 0.278 50.9 28.5 490 7.3 11.3 0.41 14.5 2 5 0.471 1.74 4 1.02 1.76 0.145 0.188 47.1 30.6 430 7.6 12.2 0.23 6 15.9 2 5 0.133 1.88 5 1.42 1.60 0.127 0.230 44.4 32.0 160 7.7 12.9 0.11 1 16.9 2 5 0.012 1.71 6 1.82 1.60 0.127 0.230 42.4 33.1 160 7.9 13.4 0.02 1 17.6 2 5 0.012 1.71 158 A3. Soil parameters by layer for plot calibration — Continued. Soil Profile Layer Z BD U FC SAN SIL WN pH SMB CBN CAC CEC Rock WNO3 AP RSD BDD SC Salb m t/m3 m/m m/m % % g/t cmol/kg % % cmol/kg % g/m3 g/t t/ha t/m3 mm/h Juchitepec II 1 0.01 1.48 0.080 0.220 72.0 21.0 805 6.2 0.94 13.2 2 2 25.91 0.13 2 0.30 1.48 0.080 0.220 72.0 21.0 805 6.2 0.94 13.2 2 2 25.91 3 0.53 1.50 0.141 0.273 66.9 22.2 430 6.5 6.4 0.41 7.9 2 5 2 0.100 1.60 4 0.84 1.50 0.220 0.324 61.5 24.7 510 6.7 7.5 0.31 8.9 2 5 0.038 1.60 5 1.22 1.50 0.166 0.269 57.2 26.8 100 6.9 8.3 0.22 9.6 2 5 0.006 1.60 6 1.57 1.50 0.192 0.301 54.2 28.2 170 7.0 8.9 0.16 10.1 2 5 0.001 1.60 Juchitepec III 1 0.01 1.30 0.110 0.260 38.0 45.0 1000 6.5 1.16 27.0 2 2 13.21 0.13 2 0.30 1.30 0.110 0.260 38.0 45.0 1000 6.5 1.16 27.0 2 2 13.21 3 0.64 1.63 0.217 0.278 50.9 28.5 490 7.3 11.3 0.41 14.5 2 5 0.471 1.74 4 1.02 1.76 0.145 0.188 47.1 30.6 430 7.6 12.2 0.23 6 15.9 2 5 0.133 1.88 5 1.42 1.60 0.127 0.230 44.4 32.0 160 7.7 12.9 0.11 1 16.9 2 5 0.012 1.71 6 1.82 1.60 0.127 0.230 42.4 33.1 160 7.9 13.4 0.02 1 17.6 2 5 0.012 1.71 1 0.01 1.48 0.202 0.383 71.0 24.5 778 7.9 0.90 27.1 0 25.91 0.13 Loma de Guadalupe 2 0.20 1.48 0.202 0.383 71.0 24.5 778 7.9 0.90 27.1 0 25.91 3 0.40 1.30 0.151 0.301 51.0 37.0 580 7.4 0.67 0 13.21 4 0.70 1.30 0.061 0.351 44.0 45.0 585 7.6 0.68 0 13.21 5 1.00 1.30 0.063 0.353 48.0 35.5 940 7.9 1.09 0 13.21 6 1.30 1.30 0.080 0.370 43.5 40.0 360 8.1 0.42 0 13.21 Loma de Guadalupe 1 0.01 1.15 0.172 0.326 34.0 53.5 748 7.2 0.87 42.7 0 6.86 0.14 II 2 0.20 1.15 0.172 0.326 34.0 53.5 748 7.2 0.87 42.7 0 6.86 3 0.40 1.30 0.096 0.286 51.0 38.0 650 7.1 0.76 42.7 0 13.21 4 0.70 1.30 0.116 0.316 43.5 39.5 1100 7.2 1.28 42.7 0 13.21 5 1.00 1.30 0.225 0.385 43.0 36.0 670 7.5 0.78 42.7 0 13.21 6 1.30 1.30 0.306 0.446 41.0 39.0 650 7.5 0.76 42.7 0 13.21 1 0.01 1.38 0.099 0.206 56.3 24.2 500 7.1 15.6 1.50 30 19.3 5 5 28 0.300 1.50 30.10 0.13 Lomas de San Juan 2 0.20 1.56 0.089 0.197 66.0 12.0 500 7.1 11.7 0.58 35 14.5 5 4 27 0.013 1.50 30.10 3 0.40 1.50 0.105 0.228 54.8 22.5 1190 7.2 15.6 1.29 35 19.3 4 25 0.010 1.45 20.80 Lomas de San Juan 1 0.01 1.38 0.099 0.206 56.3 24.2 550 7.1 15.6 1.50 30 19.3 2 5 28 0.300 1.50 30.10 0.13 II 2 0.25 1.56 0.089 0.197 50.2 40.2 550 6.7 11.7 0.64 35 14.5 2 4 27 0.013 1.50 30.10 3 0.40 1.28 0.105 0.223 54.8 25.0 1190 7.2 15.6 1.29 35 19.3 4 25 0.010 1.45 20.80 Lomas de San Juan 1 0.01 1.38 0.099 0.206 56.3 24.2 555 7.1 15.6 0.65 30 19.3 3 5 28 0.300 1.50 28.00 0.13 III 2 0.20 1.36 0.110 0.216 59.0 23.0 555 6.6 10.8 0.65 35 14.5 3 4 27 0.013 1.50 28.00 3 0.40 1.28 0.091 0.200 58.0 28.0 1190 7.2 15.6 1.29 35 19.3 4 25 0.010 1.45 20.80 Lomas de San Juan 1 0.01 1.38 0.099 0.206 56.3 18.9 770 7.1 10.8 0.90 30 13.3 3 5 28 0.013 1.50 21.54 0.13 IV 2 0.20 1.36 0.102 0.194 55.3 18.9 770 7.2 10.8 0.90 35 13.3 3 8 27 0.013 1.50 21.54 3 0.40 1.28 0.105 0.223 54.8 25.0 1190 7.2 15.6 1.29 35 19.3 4 25 0.010 1.45 20.80 Lomas de San Juan 1 0.01 1.38 0.099 0.206 56.3 18.9 770 7.1 10.8 0.90 30 13.3 3 5 28 0.013 1.50 24.50 0.13 V 2 0.20 1.36 0.102 0.194 55.3 18.9 770 7.2 10.8 0.90 35 13.3 3 8 27 0.013 1.50 24.50 3 0.40 1.28 0.105 0.223 54.8 25.0 1190 7.2 15.6 1.29 35 19.3 4 25 0.010 1.45 20.80 Lomas de San Juan 1 0.01 1.38 0.099 0.206 56.3 24.2 650 6.9 15.6 1.50 30 19.3 5 5 28 0.300 1.50 26.00 0.13 VI 2 0.22 1.56 0.089 0.197 66.0 12.0 650 6.9 11.7 0.76 35 14.5 5 4 27 0.013 1.50 26.00
3 0.31 1.50 0.105 0.228 56.5 22.3 1190 6.9 15.6 0.39 35 19.3 4 25 0.010 1.45 20.80 159 4 0.45 1.50 0.105 0.228 73.5 24.0 1190 6.9 15.6 0.15 35 19.3 4 25 0.010 1.45 0.10 A3. Soil parameters by layer for plot calibration — Continued. Soil Profile Layer Z BD U FC SAN SIL WN pH SMB CBN CAC CEC Rock WNO3 AP RSD BDD SC Salb m t/m3 m/m m/m % % g/t cmol/kg % % cmol/kg % g/m3 g/t t/ha t/m3 mm/h Lomas de San Juan 1 0.01 1.38 0.099 0.206 56.3 24.2 1850 6.9 15.6 1.50 30 19.3 5 5 28 0.300 1.50 24.50 0.13 VII 2 0.20 1.39 0.104 0.216 62.0 22.0 1850 6.9 10.8 2.15 35 13.3 5 8 35 0.013 1.50 24.50 3 0.40 1.32 0.118 0.223 59.0 25.0 1190 6.5 15.6 1.69 35 19.3 1 4 25 0.010 1.45 20.80 4 0.60 1.25 0.125 0.230 58.0 27.0 1190 8.9 15.6 1.22 6 19.3 4 25 0.010 1.45 20.80 Montecillo, CP 1 0.01 1.28 0.099 0.194 57.6 25.3 1145 7.6 46.0 1.33 20.9 206 17 0.434 1.34 4.00 0.13 2 0.15 1.28 0.099 0.194 57.6 25.3 1145 7.6 46.0 1.33 20.9 206 17 0.434 1.34 4.00 3 0.30 1.28 0.103 0.196 59.0 23.2 1005 7.7 39.5 1.17 24.3 28 15 0.518 1.34 8.00 4 0.60 1.32 0.103 0.197 52.1 29.2 815 7.7 38.0 0.95 27.3 49 13 0.403 1.34 7.00 5 0.90 1.27 0.124 0.219 55.8 24.0 330 8.0 39.7 0.34 6 30.4 5 1 0.039 1.66 7.00 6 1.20 1.25 0.129 0.236 56.0 23.5 250 8.0 20.9 0.11 11 32.0 5 0.006 1.66 7.00 7 1.33 1.60 0.163 0.253 36.3 19.0 200 8.6 43.3 25 32.9 5 0.001 1.71 1.00 Nativitas 1 0.01 1.48 0.245 0.457 63.5 33.5 224 6.8 0.26 24.8 1 25.91 0.13 2 0.20 1.48 0.245 0.457 63.5 33.5 224 6.8 0.26 24.8 1 25.91 3 0.40 1.42 0.047 0.227 57.0 21.5 615 6.7 0.72 1 1.52 Nativitas II 1 0.01 1.16 0.152 0.245 52.1 19.2 1080 6.5 15.6 1.26 30 19.3 3 5 28 0.300 1.50 26.00 0.13 2 0.20 1.16 0.152 0.245 52.1 19.2 1080 6.5 11.7 1.26 35 14.5 3 4 35 0.013 1.50 26.00 3 0.45 0.93 0.105 0.223 56.5 22.3 1190 6.6 15.6 0.39 35 19.3 4 25 0.010 1.45 20.80 Papalotla 1 0.01 1.30 0.119 0.227 51.0 39.0 459 7.2 0.53 34.8 0 13.21 0.13 2 0.20 1.30 0.119 0.227 51.0 39.0 459 7.2 0.53 34.8 0 13.21 3 0.40 1.48 0.090 0.280 57.0 36.0 760 8.0 0.88 34.8 0 25.91 4 0.70 1.25 0.081 0.331 41.5 49.0 430 8.0 0.50 34.8 0 11.00 5 1.00 1.20 0.221 0.431 28.0 49.5 270 8.4 0.31 34.8 0 9.00 6 1.30 1.25 0.150 0.460 28.0 41.0 330 8.5 0.38 34.8 0 2.29 Papalotla II 1 0.01 1.48 0.215 0.368 52.5 35.5 793 7.8 0.92 54.6 3 25.91 0.13 2 0.20 1.48 0.215 0.368 52.5 35.5 793 7.8 0.92 54.6 3 25.91 3 0.40 1.48 0.068 0.248 57.0 34.0 825 8.4 0.96 3 25.91 4 0.70 1.48 0.081 0.341 53.5 34.0 620 8.5 0.72 3 25.91 5 1.00 1.30 0.167 0.367 46.0 44.5 480 8.6 0.56 3 13.21 6 1.30 1.30 0.079 0.369 46.0 36.5 360 8.7 0.42 3 13.21 San Dieguito 1 0.01 1.19 0.134 0.238 45.6 27.5 1015 6.3 11.7 1.18 30 14.5 3 5 27 0.013 1.50 26.00 0.13 2 0.20 1.19 0.134 0.238 45.6 27.5 1015 6.3 11.7 1.18 35 14.5 3 8 27 0.013 1.50 26.00 3 0.45 1.09 0.105 0.223 54.8 25.0 1190 7.7 15.6 1.29 35 19.3 4 25 0.010 1.45 20.80 Tecamac 1 0.01 1.19 0.222 0.321 33.7 21.3 1375 7.1 30.7 1.60 37.3 0 13.55 0.12 2 0.15 1.19 0.222 0.321 33.7 21.3 1375 7.1 30.7 1.60 37.3 0 13.55 3 0.30 1.39 0.181 0.274 34.1 19.2 1265 7.5 30.7 1.47 36.5 0 13.55 4 0.60 1.17 0.249 0.381 21.9 20.8 1250 6.7 34.3 1.45 43.7 1 13.55 Tecamac II 1 0.01 1.38 0.206 0.308 24.1 23.4 2050 7.1 30.7 2.38 37.3 0 13.55 0.12 2 0.15 1.38 0.206 0.308 24.1 23.4 2050 7.1 30.7 2.38 37.3 0 13.55 3 0.30 1.19 0.247 0.384 21.0 23.0 1500 7.3 37.0 1.74 46.2 2 13.55 4 0.60 1.17 0.249 0.381 21.9 20.8 1250 6.7 34.3 1.45 43.7 1 13.55 160 A3. Soil parameters by layer for plot calibration — Continued. Soil Profile Layer Z BD U FC SAN SIL WN pH SMB CBN CAC CEC Rock WNO3 AP RSD BDD SC Salb m t/m3 m/m m/m % % g/t cmol/kg % % cmol/kg % g/m3 g/t t/ha t/m3 mm/h 1 0.01 1.48 0.080 0.220 68.0 25.0 505 6.8 0.59 1.1 2 3 25.91 0.13 Tenango del Aire 2 0.30 1.48 0.080 0.220 68.0 25.0 505 6.8 0.59 1.1 2 3 25.91 3 0.53 1.50 0.141 0.273 66.9 22.2 430 6.5 6.4 0.41 7.9 2 5 2 0.100 1.60 4 0.84 1.50 0.220 0.324 61.5 24.7 510 6.7 7.5 0.31 8.9 2 5 0.038 1.60 5 1.22 1.50 0.166 0.269 57.2 26.8 100 6.9 8.3 0.22 9.6 2 5 0.006 1.60 6 1.57 1.50 0.192 0.301 54.2 28.2 170 7.0 8.9 0.16 10.1 2 5 0.001 1.60 Tenango del Aire 1 0.01 1.48 0.080 0.220 67.0 23.0 750 6.3 0.87 13.8 2 2 25.91 0.13 II 2 0.30 1.48 0.080 0.220 67.0 23.0 750 6.3 0.87 13.8 2 2 25.91 3 0.56 1.55 0.127 0.250 71.8 18.9 367 5.9 4.0 0.35 6.0 2 5 4 0.351 1.66 4 0.91 1.55 0.144 0.264 70.0 20.0 392 6.1 4.6 0.28 6.5 2 5 0.124 1.66 5 1.14 1.60 0.133 0.249 69.1 20.5 190 6.1 4.9 0.25 0.2 6.7 2 5 0.015 1.71 6 1.37 1.60 0.115 0.233 68.4 20.9 138 6.2 5.1 0.22 1.7 6.9 2 5 0.001 1.71 Tenango del Aire 1 0.01 1.54 0.050 0.190 84.0 12.0 630 6.5 0.73 2.2 2 3 61.21 0.15 III 2 0.30 1.54 0.050 0.190 84.0 12.0 630 6.5 0.73 2.2 2 3 61.21 3 0.64 1.63 0.217 0.278 50.9 28.5 490 7.3 11.3 0.41 14.5 2 5 0.471 1.74 4 1.02 1.76 0.145 0.188 47.1 30.6 430 7.6 12.2 0.23 6 15.9 2 5 0.133 1.88 5 1.42 1.60 0.127 0.230 44.4 32.0 160 7.7 12.9 0.11 1 16.9 2 5 0.012 1.71 6 1.82 1.60 0.127 0.230 42.4 33.1 160 7.9 13.4 0.02 1 17.6 2 5 0.012 1.71 Tezoyuca 1 0.01 1.30 0.210 0.448 37.0 46.0 678 7.5 0.79 42.5 0 13.21 0.13 2 0.20 1.30 0.210 0.448 37.0 46.0 678 7.5 0.79 42.5 0 13.21 3 0.40 1.30 0.211 0.331 43.5 39.5 535 7.8 0.62 0 13.21 4 0.70 1.30 0.286 0.426 25.5 47.5 440 8.0 0.51 0 13.21 5 1.00 1.00 0.356 0.496 23.0 33.5 480 8.2 0.56 0 0.51 6 1.30 1.30 0.120 0.410 39.5 36.5 585 8.6 0.68 0 13.21 Tezoyuca II 1 0.01 1.15 0.084 0.159 23.0 64.5 574 7.1 0.67 55.6 0 6.86 0.14 2 0.20 1.15 0.084 0.159 23.0 64.5 574 7.1 0.67 55.6 0 6.86 3 0.40 1.25 0.277 0.417 25.5 40.0 785 7.3 0.91 55.6 0 2.29 4 0.70 1.25 0.303 0.563 25.5 35.5 625 7.8 0.73 55.6 0 2.29 5 1.00 1.30 0.255 0.395 37.0 41.0 515 8.4 0.60 55.6 0 13.21 6 1.30 1.40 0.055 0.325 55.5 22.5 515 8.0 0.60 55.6 0 10.00 Tlalmanalco 1 0.01 1.48 0.080 0.220 68.0 22.0 600 5.4 0.70 1.5 2 3 25.91 0.13 2 0.30 1.48 0.080 0.220 68.0 22.0 600 5.4 0.70 1.5 2 3 25.91 3 0.51 1.50 0.227 0.319 60.6 25.6 660 6.0 6.3 0.53 11.2 2 5 10 0.373 1.60 4 0.75 1.36 0.236 0.362 59.2 26.2 460 6.1 6.9 0.48 11.8 2 5 3 0.161 1.46 5 0.99 1.36 0.236 0.362 58.2 26.6 460 6.2 7.4 0.44 12.1 2 5 0.161 1.46 6 1.37 1.50 0.213 0.315 57.0 27.0 300 6.3 7.9 0.40 12.6 2 5 0.016 1.60 Tlalmanalco II 1 0.01 1.30 0.110 0.260 48.0 38.0 1090 6.0 1.27 2.3 2 1 13.21 0.13 2 0.30 1.30 0.110 0.260 48.0 38.0 1090 6.0 1.27 2.3 2 1 13.21 3 0.51 1.50 0.227 0.319 60.6 25.6 660 6.0 6.3 0.53 11.2 2 5 10 0.373 1.60 4 0.75 1.36 0.236 0.362 59.2 26.2 460 6.1 6.9 0.48 11.8 2 5 3 0.161 1.46 5 0.99 1.36 0.236 0.362 58.2 26.6 460 6.2 7.4 0.44 12.1 2 5 0.161 1.46 6 1.37 1.50 0.213 0.315 57.0 27.0 300 6.3 7.9 0.40 12.6 2 5 0.016 1.60 161 A3. Soil parameters by layer for plot calibration — Continued. Soil Profile Layer Z BD U FC SAN SIL WN pH SMB CBN CAC CEC Rock WNO3 AP RSD BDD SC Salb m t/m3 m/m m/m % % g/t cmol/kg % % cmol/kg % g/m3 g/t t/ha t/m3 mm/h Tlalmanalco III 1 0.01 1.54 0.050 0.190 83.0 8.0 1035 6.1 1.20 9.3 2 6 61.21 0.13 2 0.30 1.54 0.050 0.190 83.0 8.0 1035 6.1 1.20 9.3 2 6 61.21 3 0.51 1.50 0.227 0.319 60.6 25.6 660 6.0 6.3 0.53 11.2 2 5 10 0.373 1.60 4 0.75 1.36 0.236 0.362 59.2 26.2 460 6.1 6.9 0.48 11.8 2 5 3 0.161 1.46 5 0.99 1.36 0.236 0.362 58.2 26.6 460 6.2 7.4 0.44 12.1 2 5 0.161 1.46 6 1.37 1.50 0.213 0.315 57.0 27.0 300 6.3 7.9 0.40 12.6 2 5 0.016 1.60 Totolzingo 1 0.01 1.48 0.186 0.358 58.5 29.0 863 7.6 1.00 41.3 2 25.91 0.13 2 0.20 1.48 0.186 0.358 58.5 29.0 863 7.6 1.00 41.3 2 25.91 3 0.40 1.48 0.031 0.352 58.5 26.5 860 8.3 1.00 2 25.91 4 0.70 1.30 0.323 0.483 41.0 48.0 930 8.5 1.08 2 13.21 5 1.00 1.30 0.077 0.357 49.0 32.0 695 9.0 0.81 2 13.21 6 1.30 1.48 0.077 0.347 53.5 34.5 605 9.0 0.70 2 25.91 Xaltepa 1 0.01 1.48 0.133 0.274 66.5 25.0 284 7.0 0.33 29.7 0 25.91 0.13 2 0.20 1.48 0.133 0.274 66.5 25.0 284 7.0 0.33 29.7 0 25.91 3 0.40 1.48 0.058 0.168 64.0 17.0 660 6.8 0.77 0 25.91 4 0.70 1.48 0.046 0.186 61.0 19.5 865 7.0 1.01 0 25.91 162 163
APPENDIX B: MODEL VALIDATION 164
B1. Climatic data for the meteorological stations around the Texcoco District. Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Col. Agrícola Oriental, D. F. (9009) OBMX 22.2 24.2 26.6 28.1 28.1 26.5 25.3 25.5 24.7 24.5 24 22.4 °c OBMN 5.1 6.3 8.5 10.3 11.4 12.5 12.1 12.1 12.3 10.9 8 5.7 °c SDTMX 2.9 3 3 3.4 3.4 3.5 2.8 3 3.4 3.1 2.5 2.6 °c SDTMN 2 2.4 2.7 2.2 1.7 1.4 1.3 1.3 1.5 2 2.9 2.2 °c SMY 8.4 6.7 10.8 25.5 49.7 107.4 103.3 99.6 87.4 40.9 4.9 3.5 mm RST2 8.9 5.1 7.3 6.6 7.4 8.7 6.1 6.8 7.3 7.6 4.3 3.3 mm RST3 2.97 2.4 2.33 2.4 3.01 1.78 1.18 1.61 2.04 2.14 1.99 2.23 mm PRW1 0.03 0.041 0.049 0.108 0.218 0.319 0.431 0.336 0.335 0.139 0.04 0.023 PRW2 0.306 0.262 0.298 0.445 0.398 0.595 0.641 0.597 0.538 0.391 0.111 0.387 DAYP 1.3 1.5 2 4.9 8.3 13.2 16.9 14.1 12.6 5.8 1.3 1.1 d Col. Del Valle (SMN), D. F. (9011) OBMX 22.6 24.6 27.2 28 26.9 25.7 24.1 24.6 23.6 23.7 23 22 °c OBMN 5.1 6.4 9.1 10.9 12 13.3 12.5 12.5 12.4 10.4 7.6 5.6 °c SDTMX 3 2.7 2.9 2.8 2.8 2.7 2 1.7 2.5 2.5 3 2.7 °c SDTMN 2.2 2.3 2 2 1.8 1.4 1.3 1.3 1.7 2.7 2.9 2.5 °c SMY 9.9 3.3 5.4 21 65.5 101.9 146.1 135.4 105.1 44.8 18.8 3.4 mm RST2 8.4 2.2 3.1 4.6 7.6 8.7 7.9 8.2 9.1 7.9 6.1 4.3 mm RST3 1.63 0.37 1.63 2.26 2.43 2.91 3.37 2.44 2.71 2.4 1.38 2.76 mm PRW1 0.025 0.032 0.043 0.119 0.291 0.339 0.582 0.543 0.369 0.145 0.072 0.034 PRW2 0.478 0.176 0.286 0.44 0.472 0.638 0.696 0.674 0.619 0.53 0.34 0.158 DAYP 1.4 1.1 1.8 5.3 11 14.5 20.4 19.4 14.8 7.3 2.9 1.2 d Col. Escandón, D. F. (9012) OBMX 22.3 24.2 27 27.7 27.2 25.50 23.9 24.2 23.3 23.3 23 21.9 °c OBMN 7.3 8.5 10.9 12.7 13.6 14.10 13.2 13.3 13.2 11.6 9.5 8.1 °c SDTMX 3.2 3.2 3 3.3 3.1 O 3.102.3 2 2.6 2.9 3 3.1 °c SDTMN 2.3 2.2 2.2 1.9 1.7 O 1.301.1 1.1 1.4 2.2 2.3 2.3 °c SMY 12.1 5.6 9.9 25.6 58.5 5 131.4163.3 145 131 55.9 11.3 6 mm RST2 9 6.5 5.9 5.9 7.2 2 9.9 8.8 8.9 10.4 10.3 6.2 4.2 mm RST3 2.18 4.33 2.62 3.97 2.99 9 2.47 1.9 2 2.21 3.1 3.87 1.93 mm PRW1 0.036 0.041 0.053 0.138 0.266 6 0.363 0.604 0.51 0.372 0.161 0.065 0.044 PRW2 0.391 0.241 0.349 0.443 0.494 4 0.663 0.688 0.65 0.636 0.509 0.258 0.273 DAYP 1.7 1.5 2.3 6 10.7 0 15.6020.4 18.4 15.2 7.6 2.4 1.8 d Col. Moctezuma (SMN), D. F. (9013) OBMX 23.9 25.5 28.2 29.3 29.1 27.2 26 26.3 25.5 25.5 25 24.1 °c OBMN 5.7 7.1 9.4 11.6 13 13.5 12.8 13 12.8 11 8.4 6.8 °c SDTMX 3.2 2.8 3.2 2.9 2.7 2.9 2.1 2 2.6 2.7 2.5 2.5 °c SDTMN 2.3 2.6 2.4 1.9 1.7 1.3 1.2 1.1 1.6 2.1 2.5 2.6 °c SMY 12.5 5.8 11.8 26.9 67 131.5 138.3 121.1 106.4 41.8 6.9 7.2 mm RST2 8.6 3.3 7.4 5.9 8.3 10.5 8 8 10.9 6.4 3.9 16.7 mm RST3 1.32 1.48 2.76 2.46 2.93 2.3 2.2 1.84 3.28 1.05 1.61 3.4 mm PRW1 0.039 0.053 0.063 0.137 0.311 0.332 0.52 0.459 0.356 0.157 0.052 0.022 PRW2 0.273 0.293 0.24 0.452 0.391 0.639 0.68 0.573 0.543 0.314 0.205 0.176 DAYP 1.6 2 2.4 6 10.5 14.4 19.2 16 13.1 5.8 1.9 0.8 d
OBMX = the average monthly maximum air temperature (°C); OBMN = the average monthly minimum air temperature (°C); SDTMX = the monthly standard deviation maximum air temperature (°C); SDTMN = the monthly standard deviation minimum air temperature (°C); SMY = the average monthly precipitation (mm); RST2 = the monthly standard deviation of daily precipitation (mm); RST3 = the monthly skew coefficient for daily precipitation; PRW1 = the monthly probability of wet day after dry day; PRW2 = the monthly probability of wet day after wet day; DAYP = the average number of days of rain per month (d). 165
B1. Climatic data for the meteorological stations around the Texcoco District — Continued. Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Col. Sn. Juan de Aragón, D. F. (9043) OBMX 22.8 24.6 27.1 27.90 27.4 26 24.50 24.9 24.4 24.3 23.7 22.5 °c OBMN 2.3 3.1 5.5 8 9.9 11.6 11.2 11.2 11.1 8.7 5.2 3.5 °c SDTMX 2.9 2.8 3 3 2.8 2.7 2 1.9 2.6 2.8 2.7 2.7 °c SDTMN 2.6 2.8 2.6 2.3 2.1 1.9 1.5 1.5 2 2.7 2.9 2.8 °c SMY 10.6 5.4 8.9 25.3 51.1 102.4 116.3 112 93.5 46.1 8.4 5.6 mm RST2 8 3.5 5.5 4.9 5.6 9.3 6.8 8.3 8.5 7.4 4.4 4.4 mm RST3 2.43 2.98 2.83 3.31 2.16 3.75 2.1 2.74 2.42 2.17 3.2 1.85 mm PRW1 0.035 0.052 0.057 0.164 0.255 0.369 0.536 0.488 0.363 I 0.172 0.071 0.034 PRW2 0.386 0.266 0.321 0.425 0.515 0.64 0.663 0.602 0.582 I 0.479 0.298 0.346 DAYP 1.7 1.9 2.4 6.60 10.7 15.2 19 17.1 13.9 7.7 2.8 1.5 d Col. Sta. Ursula Coapa, D. F. (9014) OBMX 22.3 24 26.9 27.7 27.2 25 23.9 24.3 24.1 23.9 23.1 22.1 °c OBMN 4.6 5.7 8.5 10.4 11.1 12.4 11.8 11.6 11.6 10.4 7.2 5.4 °c SDTMX 3.3 3.3 3.6 3.6 3.1 3.3 2.5 2.3 2.8 3.3 3 3 °c SDTMN 2.2 2.3 2.6 2.1 1.9 1.5 1.4 1.3 1.7 2.1 2.4 2.4 °c SMY 7.7 5.8 11.8 23.7 72.1 149.7 159 148.5 130.5 67.8 7.8 6.3 mm RST2 6.7 2.2 8.1 5.3 7.7 9.6 8 9.2 8.3 11.2 6.6 6.8 mm RST3 1.21 0.97 2.59 2.14 2.12 1.9 2.33 2.74 1.92 3.04 3.16 1.5 mm PRW1 0.028 0.044 0.042 0.105 0.261 0.364 0.523 0.49 0.453 0.166 0.047 0.024 PRW2 0.286 0.276 0.405 0.422 0.447 0.652 0.608 0.581 0.557 0.424 0.172 0.188 DAYP 1.2 1.6 2.1 4.6 9.9 15.3 17.7 16.7 15.2 6.9 1.6 0.9 d Coyoacán I.N.I.F, D. F. (9070) OBMX 21.3 22.4 24.6 25.9 26.5 24.7 23.3 22.8 21.9 23.1 22.2 20.8 °c OBMN 4.7 5.6 7.2 9.3 11.1 11.9 11.7 11.7 11.5 10 7.3 5.9 °c SDTMX 2.9 3 3.3 3.8 3.9 3.4 3 2.6 3 3.1 2.7 2.9 °c SDTMN 2.1 1.9 2.1 1.6 1.7 1.7 1.4 1.3 2 2.2 2.3 2 °c SMY 9 7.9 8.6 27.9 52 164.9 183.4 144 136.2 61.6 4.8 7.2 mm RST2 5.3 4.2 5.3 6.1 6.1 10.3 8.7 9.2 9.1 8.6 3.3 4.5 mm RST3 0.89 2.29 4.06 4.55 2.41 1.66 1.3 2.12 1.91 2.55 2.32 1.12 r mm PRW1 0.041 0.062 0.047 0.151 0.256 0.401 0.468 0.408 0.423 0.207 0.043 0.039 PRW2 0.217 0.294 0.429 0.419 0.497 0.658 0.682 0.689 0.659 0.477 0.4 0.292 DAYP 1.5 2.3 2.3 6.2 10.5 16.2 18.5 17.6 16.6 8.8 2 1.6 d Cuatepec Barrio Bajo, D. F. (9017) OBMX 21.2 23.1 26.1 26.8 27.3 25.4 24.2 24.5 23.7 23.1 22.1 21 °c OBMN 5.4 6.6 9.1 10.5 11.6 11.6 11.2 11.1 10.9 9.7 7.3 6 °c SDTMX 3 2.4 2.8 3.3 2.9 3.1 2.4 2.4 2.4 2.6 2.3 2.5 °c SDTMN 1.9 1.7 2.3 2.3 1.7 1.6 1.3 1.1 1.4 2 1.7 1.8 °c SMY 4.7 4.9 6.4 19.7 56.8 129.3 139.3 124.9 79.4 48.3 5.5 4 mm RST2 7.2 4.9 11.1 6.4 13.4 11.2 10.6 10 9.2 11.7 9 7.8 mm RST3 1.16 1.68 3.28 2.85 3.92 1.51 2.42 1.89 1.61 2.37 2.35 3.4 mm PRW1 0.014 0.022 0.026 0.072 0.161 0.26 0.344 0.327 0.23 0.085 0.022 0.02 PRW2 0.364 0.412 0.316 0.522 0.402 0.593 0.613 0.502 0.421 0.525 0.313 0.333 DAYP 0.6 1 1.1 3.9 6.6 11.7 14.6 12.3 8.5 4.7 0.9 0.9 d Km. 6+250, Gran Canal, D. F. (9029) OBMX 22.9 24.5 I 27.30 28 , 27.70 25.9 24.3 24.7 24.1 24 23.5 22.5 °c OBMN 3.5 4.7 7.1 9.2 I 10.80 12.2 11.7 11.7 11.5 9.3 6.5 4.8 °c SDTMX 3.1 2.8 3.1 3 2.8 2.8 2 1.8 2.4 2.7 2.8 2.6 °c SDTMN 2.5 2.5 2.4 2.2 2 1.6 1.3 1.3 1.9 2.8 2.8 2.6 °c SMY 10.6 5.9 9.1 23 50.5 103.8 115.8 115 93.9 44.8 11.6 5.3 mm RST2 8 4.1 6.7 4.8 5.7 9.3 7.2 8.4 8 7.3 5.7 3.2 mm RST3 2.3 2.79 2.84 3.05 2.07 3.01 2.23 3.02 1.9 2.24 2.42 1.62 mm PRW1 0.037 0.05 0.047 0.14 0.255 0.35 0.521 0.484 0.328 0.162 0.065 0.042 PRW2 0.365 0.246 0.359 0.416 0.499 0.654 0.641 0.607 0.6 0.474 0.32 0.27 DAYP 1.7 1.8 2.1 5.8 10.5.015.1 18.4 17.1 13.5 7.3 2.6 1.7 d 166
B1. Climatic data for the meteorological stations around the Texcoco District — Continued. Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Milpa Alta, Milpa Alta, D. F. (9032) OBMX 20.5 21.6 23.7 24.9 24.9 23.1 21.9 21.9 21.3 21.4 21.3 20.4 °c OBMN 6.2 7.1 9.3 10.8 11.7 12.1 11.3 11.3 11.3 9.8 7.9 6.8 °c SDTMX 2.8 2.6 2.8 2.8 2.8 2.7 2 2.1 2.5 2.9 2.5 2.4 °c SDTMN 2.1 2.3 2.3 1.9 1.6 1.2 1.2 1.1 1.4 2.1 2.1 2.1 °c SMY 10.3 6.9 12.2 27.8 65.2 124.2 141.8 132.3 101.1 44.1 9.8 5.3 mm RST2 7.8 4.1 6.8 5.7 9.2 8 7.2 7.6 6.8 7.7 5.5 3.9 mm RST3 2.49 2.63 2.49 2.18 3.87 2.13 2.26 2.19 2.07 2.64 2.06 2.24 mm PRW1 0.037 0.051 0.06 0.137 0.249 0.39 0.614 0.524 0.457 0.165 0.057 0.043 PRW2 0.392 0.361 0.355 0.429 0.483 0.687 0.7 0.687 0.659 0.477 0.179 0.196 DAYP 1.8 2.1 2.6 5.80 1 10.1 16.7 20.8 19.4 17.2 7.4 1.9 1.6 d Morelos 77, Sn. Pablo Barrio, Ixtapalapa, D. F. (9026) Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec OBMX 23 24.4 26.3 27.7 27.9 26.3 24.7 24.9 24.2 24.3 23.8 22.8 °c OBMN 3.7 4.3 6.4 8.6 10.4 11.8 11.5 11.3 11.3 9.3 6 4.9 °c SDTMX 2.7 2.4 2.6 3.2 2.7 3.2 2.2 2.2 2.4 2.5 2.6 2.6 °c SDTMN 2.5 2.4 2.4 2.6 2.1 1.9 1.5 1.8 1.9 2.4 2.6 2.5 °c SMY 11 3.9 9.2 21 50.4 104.1 127 114.3 100.4 44.7 5 10 mm RST2 9.6 2.3 6.7 5.3 6.4 7.8 7 7.7 8.7 7.5 2.8 9.9 mm RST3 2.92 2.5 3.77 2.69 2.85 2.57 1.92 2.84 2.78 2.93 2.09 2.97 mm PRW1 0.038 0.047 0.053 0.133 0.263 0.363 0.511 0.449 0.35 0.166 0.055 0.036 PRW2 0.333 0.234 0.371 0.36 0.439 0.622 0.674 0.624 0.642 0.447 0.182 0.34 DAYP 1.7 1.6 2.4 5.2 9.9 14.7 18.9 16.9 14.8 7.2 1.9 1.6 d Moyoguarda, Xochimilco, D. F. (9034) OBMX 21.1 23.1 25 25.7 25.8 24.4 23.3 23.2 22.8 22.7 21.9 21.2 °c OBMN 2.3 3.6 6.1 8 9.6 11.2 10.8 10.6 10.7 8 4.9 2.9 °c SDTMX 3.4 3.2 3.1 3.2 2.8 2.9 2.3 2.2 2.5 2.7 2.8 3.1 °c SDTMN 3.4 3.4 J 3.20 2.8 2.5 2.3 2 2.2 2.5 3.6 3.8 3.1 °c SMY 8 8.3 3 9.9 38.9 74.9 143.4 161.4 152.5 128.3 52.7 13.7 8.6 mm RST2 5.7 6.4 4 5.5 7.3 8.5 9.4 8.7 9.1 9.7 9 3.9 8.9 mm RST3 2.78 4.05 5 5.66 3.24 3.1 3.07 2.41 2.64 2.85 3.14 1.51 3.32 mm PRW1 0.037 0.041 0.061 0.144 0.244 0.422 0.533 0.443 0.347 0.155 0.066 0.033 PRW2 0.387 0.405 0.383 0.505 0.556 0.674 0.67 0.69 0.673 0.484 0.415 0.311 DAYP 1.7 1.8 2.8 6.8 11 16.9 19.1 18.2 15.4 7.2 3 1.4 d Puente La Llave, Pantitlán, D. F. (9068) OBMX 22.5 24.3 26.6 28.1 28.3 26.7 25.4 25.4 23.7 24.7 22.9 22.1 °c OBMN 3.9 5.5 7.6 9.9 11 11.6 11 11.2 10.7 8.8 6.3 5 °c SDTMX 2.8 2.7 3.3 2.7 2.6 2.8 2.2 2.2 3.6 2.7 2.9 2.5 °c SDTMN 2.2 2.2 2.6 2.1 2.3 2.4 2.1 1.8 2.3 2.7 2.5 2.4 °c SMY 7.8 8 10.7 20.2 42.4 112.3 108.5 88.8 68.2 42.3 5.2 4.5 mm RST2 6.3 4.2 9.5 5.5 3.5 9.1 5.7 6.9 7.5 8.6 3 5.3 mm RST3 1.15 2.47 2.3 2.85 1.39 2.58 1.76 2.67 2.26 1.26 1.15 2.26 mm PRW1 0.031 0.056 0.043 0.097 0.241 0.353 0.493 0.366 0.228 0.13 0.041 0.031 PRW2 0.333 0.333 0.176 0.491 0.486 0.625 0.673 0.517 0.574 0.339 0.133 0.286 DAYP 1.4 2.2 1.5 4.8 9.9 14.5 18.6 13.4 10.5 5.1 1.4 1.3 d Sn. Gregorio, Atlapulco, Xochimilco, D. F. (9042) OBMX 20.8 23.2 26.5 27.8 28.3 26.5 25.5 25 23.8 23.2 21.7 20.3 °c OBMN 1.9 3.6 7.2 8.7 10.5 11.6 11.4 11.4 11.1 9.2 5.6 4 °c SDTMX 4.6 3.8 3 3.1 2.7 3.2 2.2 2 2.6 3.1 3.9 4.1 °c SDTMN 4.1 4.1 3.6 3.2 3.2 2.7 2.3 2.5 2.4 3.3 4.7 4.3 °c SMY 14.2 8.6 9 33.3 68.9 136.5 161.4 143.3 113.9 51.1 7.8 6.7 mm RST2 10.6 4.5 4.3 7.3 7.2 8.9 8 8.3 8.2 8 4.3 3.8 mm RST3 3.19 1.92 1.72 1.68 1.38 1.7 1.76 1.96 2.39 2 1.28 0.94 mm PRW1 0.037 0.051 0.049 0.099 0.19 0.336 0.508 0.439 0.306 0.157 0.046 0.034 PRW2 0.359 0.205 0.214 0.437 0.468 0.593 0.593 0.564 0.597 0.39 0.189 0.233 DAYP 1.7 1.7 1.8 4.5 8.2 13.60 17.2 15.6 13 6.3 1.6 1.3 d 167
B1. Climatic data for the meteorological stations around the Texcoco District — Continued. Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Santa Ana Tlacotenco, Milpa Alta, D. F. (9045) OBMX 19.4 20.8 22.8 24.4 24.3 22.2 21 21.10 20.7 20.7 20.6 19.7 °c OBMN 5.2 6.3 7.8 9.6 10.6 10.7 10 10.2 10.3 8.5 7.3 6.4 °c SDTMX 3.3 3.2 3.5 3.7 3.8 3.4 2.7 2.7 3.1 3.2 2.5 2.5 °c SDTMN 2.2 1.9 2.5 1.9 1.8 1.1 1 1.1 1.1 2 1.8 2 °c SMY 10.7 9.1 15.1 28.9 70.7 127.7 140.5 142.7 107 43.4 10.9 3 mm RST2 8.8 3.9 7.2 5.7 6.8 8.1 6.8 7.1 7.2 9.4 5.1 2.4 mm RST3 2.63 1.52 2.84 3.31 2.07 2.45 1.93 1.77 2.63 3.85 1.79 0.76 mm PRW1 0.036 0.056 0.066 0.159 0.297 0.38 0.548 0.578 0.441 0.151 0.064 0.032 PRW2 0.417 0.383 0.373 0.452 0.549 0.729 0.72 0.675 0.661 0.471 0.314 0.174 DAYP 1.8 2.3 3 6.8 12.3 17.5 20.5 19.9 17 6.9 2.5 1.1 d Tlahuac, Xochimilco, D. F. (9051) OBMX 23.4 24.7 26.5 28 28.1 25.8 24.1 24 24.1 24.4 23.5 22.9 OBMN 2 3.1 5.4 7.3 9 10.6 10.5 10.6 10.5 8.2 4.8 3.4 SDTMX 3.1 3.1 3.6 3.5 3.5 3.4 2.8 2.6 2.9 3.2 3.6 2.8 SDTMN 3.2 3 2.6 2.2 2.5 2 1.8 1.7 2 2.9 3.3 3.3 SMY 11.7 4.6 11.8 20.5 56.5 105.5 123.4 117 95.5 46.5 5.9 4.4 RST2 12.1 3.4 7.3 6.3 7.1 7.5 7.2 7.8 8.1 8.3 3.5 5.1 RST3 2.33 2.65 2.63 3.16 2.29 2.38 2.4 2.29 2.81 1.88 1.85 2.23 PRW1 0.028 0.035 0.045 0.091 0.198 0.32 0.435 0.414 0.268 0.122 0.038 0.017 PRW2 0.324 0.316 0.315 0.343 0.406 0.602 0.632 0.561 0.598 0.442 0.25 0.364 DAYP 1.2 1.4 1.9 3.6 7.8 13.4 16.8 15 12 5.6 1.4 0.8 La Vencedora 144, Industrial, D. F. (9062) OBMX 22.5 24.7 26.7 2B.10 28.7 26.9 25 25.1 24.3 23.9 23.8 22.6 °c OBMN 4.4 5.5 8.30 1 10.4 11.8 12.6 11.9 12 11.9 9.3 6.4 5.4 °c SDTMX 3 3.3 3.6 3.9 4 3.9 3 3.3 3.2 3 3.1 3.2 °c SDTMN 2 2.4 2.4 1.7 1.6 1.5 1.2 1.4 1.6 2.7 2.7 2.2 °c SMY 19.8 3.6 19.3 29.6 44.2 107.4 93.2 130 131 45.7 10.4 7.5 mm RST2 11 4.5 11.5 7.8 5.3 8 5.9 8.5 9 8.6 5.3 3.8 mm RST3 2.18 3.09 3.39 3.17 1.42 1.66 2.36 1.73 1.57 2.69 1.4 1.53 mm PRW1 0.056 0.037 0.07 0.121 0.186 0.331 0.557 0.414 0.331 0.181 0.065 0.046 PRW2 0.333 0.118 0.231 0.423 0.4 0.583 0.576 0.588 0.626 0.347 0.156 0.259 DAYP 2.4 1.1 2.6 5.2 7.3 13.3 17.6 15.5 14.1 6.7 2.1 1.8 d Vertedor, Milpa Alta, D. F. (9058) OBMX 20.3 21.3 24.1 24.9 24.8 22.5 21.1 21.1 20.8 21.3 20.5 20.1 °c OBMN 5.6 6.1 8.8 10.1 10.8 11.1 10.4 10.5 10.6 9.1 6.7 5.9 °c SDTMX 2.8 2.4 2.5 2.9 2.6 2.7 1.7 1.7 2.1 2.4 2.3 2.2 °c SDTMN 2.1 2.3 2.1 2.2 1.6 1.3 1.3 1.2 1.7 1.8 2.1 2.1 °c SMY 13.2 8.7 10.5 26.1 70.2 125.1 138.4 142 100.5 50.7 10.8 6 mm RST2 8.5 3.5 5.4 4.8 7.8 8.3 6.8 7.2 6.4 9 4.9 3.7 mm RST3 2.72 1.82 2.86 2.14 2.58 2.14 2.01 1.78 2.18 2.66 1.4 2.38 mm PRW1 0.05 0.064 0.069 0.15 0.307 0.377 0.639 0.581 0.469 0.199 0.061 0.056 PRW2 0.378 0.395 0.295 0.477 0.531 0.701 0.731 0.705 0.688 0.463 0.289 0.188 DAYP 2.3 2.7 2.8 6.7 12.3 16.8 21.8 20.6 18 8.4 2.4 2 d Montecillos, C.P., México. (15000) OBMX 20.7 22.1 24.1 25.6 26.1 24.4 22.5 22.8 21.9 22.6 22.3 21.4 °c OBMN 2.2 2.6 4.8 6.9 8.8 10.6 10.4 10.0 9.4 6.8 3.8 2.0 °c SDTMX 2.7 2.9 2.5 2.9 2.8 2.8 1.9 1.9 2.3 2.5 2.3 2.3 °c SDTMN 3.6 3.2 3.0 2.6 2.6 2.5 2.0 1.9 2.7 3.5 3.5 3.1 °c SMY 17.8 10.0 12.7 34.4 42.7 77.7 123.5 102.4 91.5 50.1 11.9 2.5 mm RST2 9.40 3.73 5.91 10.76 8.90 6.08 6.81 6.34 8.50 7.98 3.88 2.93 mm RST3 2.383 0.505 4.261 4.458 4.526 1.270 1.631 2.055 2.596 1.679 0.793 1.227 mm PRW1 0.057 0.058 0.101 0.146 0.177 0.308 0.500 0.495 0.331 0.210 0.055 0.025 PRW2 0.278 0.368 0.290 0.417 0.468 0.546 0.715 0.671 0.647 0.371 0.429 0.001 DAYP 2.3 2.4 3.9 6.0 7.8 12.1 19.8 18.6 14.5 7.8 2.6 d 168
B1. Climatic data for the meteorological stations around the Texcoco District — Continued. Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Amecameca de Juárez, México. (15007) OBMX 20.1 20.9 23 24.2 24.1 21.5 20.5 20.80 220.6 21.2 20.9 20.3 °c OBMN 0.4 1.7 3.5 5.6 7.3 9 8.6 8.4 8.6 6.2 3 1.6 °c SDTMX 2.2 2.2 2.5 2.2 2.3 2.5 1.8 1.7 1.9 2.1 2.1 1.9 °c SDTMN 2.6 2.5 2.6 2.1 2 2.3 1.9 1.9 2.3 2.7 3.2 2.7 °c SMY 14.4 12.3 19.2 35.4 88 161.5 181.2 168.2 186.3 64.3 13.2 6 mm RST2 13.7 4.7 8.3 4.8 7.2 8.2 7.3 7.3 8.5 6.9 7.3 4.1 mm RST3 2.27 1.95 3.06 1.39 1.85 2.06 1.96 1.81 1.59 1.98 4.22 1.65 mm PRW1 0.028 0.058 0.057 0.132 0.289 0.445 0.662 0.558 0.455 0.18 0.065 0.034 PRW2 0.4 0.413 0.396 0.513 0.542 0.714 0.768 0.732 0.755 0.567 0.304 0.182 DAYP 1.4 2.6 2.7 6.4 12 18.3 22.9 20.9 19.5 9.1 2.6 1.2 d Atenco (CFE), Atenco, México. (15138) OBMX 22.5 23.8 26.4 27.6 28.2 25.7 23.8 23.8 23.7 23.9 23.4 22.5 °c OBMN 1.2 2 4.9 7.6 9.9 10.6 10.2 9.7 9.6 7.5 4.3 2.5 °c SDTMX 3.1 2.8 2.9 2.8 2.7 3.3 2.1 2.1 2.2 2.6 2.5 2.3 °c SDTMN 2.7 3.3 3.1 2.3 2.2 2.4 1.9 2 2.8 2.9 3.2 3.1 °c SMY 8.4 6.2 13.7 33.8 59.8 75.5 111.9 105.2 72.1 43.1 12.4 6.6 mm RST2 6.2 2.5 4.1 4.8 7.7 5.7 6.8 6.4 6.6 6 4.7 4.6 mm RST3 1.6 1.5 2.25 2 1.7 2.1 2.44 1.84 2.51 2.84 2.16 2.53 mm PRW1 0.045 0.065 0.089 0.178 0.258 0.313 0.448 0.486 0.365 0.19 0.087 0.059 PRW2 0.316 0.19 0.385 , 0.414 0.465 0.628 0.697 0.61 0.589 0.488 0.361 0.19 DAYP 1.9 2.1 3.9 7 10.1 13.7 18.5 17.2 14.1 8.4 3.6 2.1 d Atenco (DGE), Atenco, México. (15008) OBMX 22.2 23.5 26.1 27.1 27.2 24.9 23.5 23.6 23.3 23.6 23.2 22.3 °c OBMN 1 2 4.7 7.1 9.1 10.7 10.1 9.8 9.6 7 3.3 1.8 °c SDTMX 2.9 2.7 2.9 3 3 3.1 2.1 2.1 2.4 2.6 2.4 2.2 °c SDTMN 2.8 3.2 2.8 2.5 2.4 2.3 1.8 2 2.6 3.1 3.4 3 °c SMY 11.4 5.6 14.9 27.4 54 106.6 110.7 113.3 87.2 49.5 7.4 4.8 mm RST2 8.1 3.2 5.2 4.5 6.6 7.9 6.8 7 6.9 8.6 4.1 3.7 mm RST3 3.83 3.19 2.66 2.11 2.1 2.1 2.19 1.9 1.97 3.62 3.29 2.87 mm PRW1 0.053 0.059 0.085 0.163 0.246 0.346 0.49 0.478 0.363 0.182 0.071 0.046 PRW2 0.321 0.204 0.363 0.4 0.496 0.674 0.68 0.636 0.598 0.467 0.222 0.19 DAYP 2.2 2 3.6 6.4 10.2 15.4 18.8 17.6 14.2 7.9 2.5 1.7 d Atlautla E-9, Atlautla, México. (15252) OBMX 18.6 19 22.2 23.3 24.3 19.7 17.2 18.1 17.7 15.5 17 15.9 °c OBMN 2.9 3.9 4.9 7.8 10.3 9.7 10 10.4 10.5 7.9 5.1 5.9 °c SDTMX 3.3 3.8 2.5 4.6 3.9 4.7 3.6 3.5 3.1 4.7 4.5 4.5 °c SDTMN 2.6 2.2 3.2 2.5 3.1 2.7 1.6 1.7 1.6 2.4 1.5 3.7 °c SMY 11.3 8.8 6 21.7 65.3 194 182.7 135.6 89.7 64.1 26 2.8 mm RST2 0.7 5.2 4.1 6.9 6.5 14.1 8.1 7.7 7.2 11.3 6.1 1 mm RST3 0 1.07 -0.11 3.6 1.52 2.75 1.31 1.16 2.13 1.04 0.45 1.03 mm PRW1 0.009 0.032 0.024 0.087 0.205 0.427 0.494 0.45 0.336 0.1 0.079 0.043 PRW2 0 0.455 0.167 0.385 0.475 0.727 0.729 0.546 0.585 0.514 0.286 0 DAYP 0.3 1.6 0.9 3.7 8.7 18.3 20 15.4 13.4 5.3 3 1.3 d Repetidora de T.V., Amecameca, México. (15080) OBMX 10.1 9 9.2 11 9.4 8.9 8.7 8.5 9.1 9.2 8.4 9.4 °c OBMN 0.1 1.2 1.5 2.5 2.5 3.6 2.8 2.6 2.7 2 1.9 1.6 °c SDTMX 3.3 3.2 3.7 3.4 2.5 2.5 1.8 2.4 2.5 2.3 1.8 2.6 °c SDTMN 2.5 2.1 2.2 1.4 1.4 1.9 1.9 1.8 1.8 1.9 1.1 1.8 °c SMY 3.5 4.4 7.6 40.8 84.5 177.1 186.5 149.9 147.5 50.2 24.3 7 mm RST2 3 4.6 3.2 4.8 7.6 9.4 7.7 7.8 6.7 6.5 7.7 4.3 mm RST3 1.64 2.06 3.31 2.32 3.08 1.81 3.3 2.13 1.83 3.03 1.9 1.29 mm PRW1 0.024 0.023 0.044 0.144 0.225 0.396 0.564 0.419 0.485 0.206 0.077 0.033 PRW2 0.345 0.37 0.452 0.615 0.685 0.73 0.837 0.738 0.71 0.49 0.45 0.316 DAYP 1.1 1 2.3 8.2 12.9 17.9 24 19.1 18.8 8.9 3.7 1.4 d 169
B1. Climatic data for the meteorological stations around the Texcoco District — Continued. Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Plan Lago de Tex. (campto.), México. (15145) OBMX 22.8 24.1 26.6 27.6 27.7 25.3 23.6 24.1 23.9 23.4 23.1 22.5 °c OBMN 1.7 3 5.8 8 9.9 11.3 11.2 11.1 10.9 8.6 5.4 3.3 °c SDTMX 2.7 2.6 3.5 3.2 2.8 2.8 2 2 2.2 2.5 2.5 2.6 °c SDTMN 2.8 3 3 2.3 2 2.1 1.5 1.5 2.3 2.8 3.3 2.9 °c SMY 8.9 9 13.3 21.8 38.8 102.2 107.8 95.3 81.5 37.1 9.7 5.1 mm RST2 6.2 4.9 7.6 4.5 4.6 7.5 6.6 6.6 8.6 6.1 4 5.3 mm RST3 1.45 1.93 2.88 2.25 2.17 2.02 2.25 1.94 3.17 1.82 1.29 2.69 mm PRW1 0.036 0.064 0.073 0.139 0.262 0.365 0.486 0.466 0.399 0.171 0.073 0.041 PRW2 0.304 0.242 0.225 0.451 0.439 0.645 0.084 0.57 0.552 0.398 0.25 0.182 DAYP 1.5 2.2 2.7 6.1 9.9 15.2 18.8 16.1 14.1 6.9 2.7 1.5 d Chalco, Chalco, México. (15020) OBMX 21.7 22.7 24.9 26.3 26.6 24.2 22.9 22.9 22.7 23.2 22.6 21.9 °c OBMN 0.8 2 4.9 7.2 9 10.5 10.1 9.8 9.7 7.1 3.5 1.7 °c SDTMX 2.6 2.6 3 2.7 2.6 2.9 1.9 2 2.3 2.2 2.5 2.3 °c SDTMN 2.9 3.4 3.1 2.5 2.4 2.3 2 2.1 2.6 3.2 3.4 3.2 °c SMY 9.3 8.2 12.1 26.3 56.5 103.4 132.9 127.2 96.2 43.5 7.7 4.6 mm RST2 5.9 3.6 8.5 4.9 7.8 6.5 7.5 8.2 7.7 7.4 3.4 4 mm RST3 1.26 1.22 3.42 2.16 5.27 1.92 1.91 3.11 2.3 2.11 1.56 1.6 mm PRW1 0.029 0.054 0.057 0.155 0.279 0.368 0.565 0.59 0.367 0.177 0.055 0.035 PRW2 0.406 0.295 0.333 0.341 0.477 0.69 0.663 0.618 0.633 0.404 0.19 0.233 DAYP 1.5 2 2.5 5.7 10.8 16.3 19.4 18.8 15 7.1 1.9 1.5 d Chapingo, Texcoco, México. (15170) OBMX 22.5 23.8 26.2 27.2 27.3 24.9 23.5 23.7 23.4 23.8 23.6 22.4 °c OBMN 2.5 3.6 6.2 8.3 9.7 11 10.5 10.3 10.1 7.6 4.6 3.3 °c SDTMX 2.8 2.7 2.8 2.9 2.8 3.1 2.1 2.1 2.5 2.7 2.6 2.6 °c SDTMN 2.7 3 2.7 2.2 2.2 2.2 1.8 1.9 2.4 2.8 3.1 2.7 °c SMY 12.5 6.7 14.1 28 48.9 107.6 123.8 111.6 91.5 39.4 8.6 5 mm RST2 7.7 3.3 6 5 5.7 6.9 7.1 6.9 7.5 6.4 4.8 4 mm RST3 3.78 2.79 3.63 3.17 3.01 1.63 2.58 2.29 2.72 2.19 2.84 2.37 mm PRW1 0.047 0.048 0.092 0.173 0.246 0.401 0.58 0.503 0.381 0.163 0.07 0.038 PRW2 0.5 0.333 0.327 0.462 0.541 0.651 0.696 0.673 0.6 0.453 0.2 0.25 DAYP 2.7 1.9 3.7 7.3 10.8 16 20.3 18.8 14.6 7.1 2.4 1.5 d Chiconautla, Ecatepec, México. (15022) OBMX 21.9 23.5 25.9 27 27 24.9 I 23.50 23.5 23.3 23.1 23 22.1 °c OBMN 0.2 1.3 3.8 6 8 9.5 9.2 8.9 9 6.4 2.9 1.5 °c SDTMX 3 2.6 3.1 2.9 2.6 2.9 1.8 1.8 2.1 2.5 2.4 2.4 °c SDTMN 3.1 3.1 2.8 2.3 2.2 2.5 2.1 2 2.4 3.1 3.5 3.2 °c SMY 10.8 6.5 17.3 22.4 50.2 12.5 113.8 108.6 87.2 44.1 8.6 7.1 mm RST2 8.1 3.6 7.7 4.5 6.9 9.7 7.3 7.6 8.4 8.8 3.5 5.3 mm RST3 2.34 2.22 2.97 3.88 3.33 3.54 2.24 1.94 2.24 2.77 2.07 2.73 mm PRW1 0.037 0.055 0.072 0.157 0.225 0.311 0.478 0.431 0.31 0.154 0.058 0.037 PRW2 0.341 0.25 0.398 0.377 0.502 0.665 0.624 0.579 0.563 0.43 0.355 0.325 DAYP 1.6 1.9 3.3 6 9.6 14.4 17.4 15.7 12.4 6.6 2.5 1.6 d Coatepec de los Olivos, Ixtapaluca, Edo de Méx. (15017) OBMX 21 22.3 24.8 26 26 23.5 22.1 22.2 22.1 22.50 2 22.1 21.3 °c OBMN 5.2 6.2 8.4 10.3 11.3 11.3 10.6 10.5 10.3 8.6 6.8 5.9 °c SDTMX 2.8 2.6 2.8 2.8 2.7 3 2.1 2 2.2 2.4 2.4 2.4 °c SDTMN 2.3 2.5 2.6 2.1 1.8 1.5 1.2 1.3 1.7 2.2 2.3 2.2 °c SMY 12 11 18.1 30.3 66.5 113.1 122.8 118.6 92.9 49.5 9.2 5.5 mm RST2 10.1 6.2 8.4 4.8 7.4 7.8 7.4 6.7 6.9 6.5 5.5 7.2 mm RST3 3.21 1.81 2.88 2.49 2.43 1.92 2.52 2.07 2.97 2.21 1.96 4.27 mm PRW1 0.034 0.056 0.085 0.184 0.281 0.363 0.556 0.558 0.403 0.179 0.055 0.031 PRW2 0.385 0.3 0.26 0.409 0.529 0.696 0.684 0.657 0.649 0.536 0.26 0.313 DAYP 1.6 2.1 3.2 7.1 11.6 16.3 19.8 19.2 16 8.6 2.1 1.3 d 170
B1. Climatic data for the meteorological stations around the Texcoco District — Continued. Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Col. Avila Camacho, Ixtapaluca, Edo de Méx. (15018) OBMX 16.3 17.3 19.4 20.5 20.4 17.3 16.7 16.6 16.70 17.10. 17.1 16.3 °c OBMN 2.5 3.1 4.8 5.8 6.4 5.8 5.4 5.2 5.3 4.4 3.5 2.7 °c SDTMX 3 2.7 3.4 3.2 3.4 2.9 2.4 2.6 2.6 3.1 2.7 2.7 °c SDTMN 2.2 2 2.2 1.9 1.7 1.5 1.2 1.2 1.4 1.8 1.9 2 °c SMY 15.5 13.7 23.4 36.5 83.8 154.7 132.8 123.6 106.6 53.4 13 10 mm RST2 10 6.2 7.8 5.9 8.1 9.7 9.1 8.2 8 6.9 4.4 5.2 mm RST3 2.21 2.26 4.32 2.94 3.03 2.75 4.12 3.08 3.4 2.6 2.09 1.88 mm PRW1 0.046 0.062 0.09 0.186 0.308 0.373 0.536 0.457 0.445 0.216 0.09 0.054 PRW2 0.327 0.463 0.376 0.498 0.582 0.759 0.71 0.71 0.678 0.513 0.272 0.377 DAYP 2 2.9 3.9 8.1 13.1 18.2 20.10 19 17.4 9.5 3.3 2.5 d E.T.A. 32, Tlalpizahuác, la Paz, Edo de Méx. (15141) OBMX 21.7 22.6 25.1 26.2 26.4 24.1 22.8 23 22.9 23 22.7 21.7 °c OBMN 2.8 4.3 5.6 8.3 10.4 11.7 10.9 10.6 10.4 8.5 5.1 3.4 °c SDTMX 2.4 2.4 2.7 2.6 2.6 2.6 2 1.9 2.3 2.3 2.5 2.9 °c SDTMN 2.8 3 4.4 4.5 2.3 1.8 1.8 1.8 2.4 3 3.1 3.1 °c SMY 4.6 7.5 10.7 39 50.4 102.8 124.2 120.9 79.9 52.7 4.7 7.4 mm RST2 4.5 3.2 9 11.7 5.9 8.9 8.5 8.9 7.7 8.5 3.4 6.8 mm RST3 2.61 1.43 2.72 3.75 1.88 3.43 3.83 2.61 2.85 2.95 2.08 2.06 mm PRW1 0.024 0.057 0.05 0.15 0.221 0.382 0.494 0.44 0.306 0.157 0.035 0.034 PRW2 0.35 0.308 0.297 0.387 0.426 0.612 0.646 0.579 0.565 0.485 0.25 0.217 DAYP 1.1 2.2 2.1 5.9 8.6 14.9 18.1 15.8 12.4 7.2 1.3 1.3 d Ecatzingo, Ecatzingo, México. (15288) OBMX 21.1 22.6 24.4 25.7 25.1 22.1 20.8 22.5 21.9 21.7 22 21.8 °c OBMN 4.3 4.9 6 7.5 8.8 9.8 9AO 9.1 9.3 7.4 6.9 5.3 °c SDTMX 2.3 3.1 3.1 3.6 4.1 3.6 3 3.6 3.4 3.2 3.1 2.7 °c SDTMN 1.4 1.7 2.4 2.2 1.8 1.2 1.4 1.1 1.2 1.4 1.5 1.6 °c SMY 14.3 9.5 5.1 41.2 1 102.8 215.8 162.8 144.6 192.1 71.3 53.7 1.6 mm RST2 12.4 6.5 2.5 5.5 9.1 9.9 9.3 8 10.4 14.9 13.3 1.8 mm RST3 -2.83 1.44 0.28 0.37 1.28 1.48 1.86 1.04 1.49 2.99 1.62 0.75 mm PRW1 0.015 0.037 0.026 0.126 0.224 0.487 0.451 0.424 0.38 0.151 0.098 0.022 PRW2 0 0.25 0.176 0.266 0.47 0.65 0.615 0.455 0.609 0.412 0.352 0 DAYP 0.4 1.3 0.9 4.4 9.20 1 17.4 16.7 13.6 14.8 6.3 3.9 0.7 d El Tejocote, Texcoco, México. (15167) OBMX 21.1 22.5 25.1 26.3 26.7 24.4 22.9 23 22.6 22.5 22.1 21.1 °c OBMN 1.2 2.4 4.8 7 8.6 9.8 9.3 9.2 9 6.4 3.2 2.1 °c SDTMX 2.7 2.6 2.7 2.9 2.9 3.1 2.3 2.3 2.4 2.6 2.5 2.4 °c SDTMN 2.7 3 2.7 2.5 2.3 2.4 1.9 1.9 2.5 3.1 3.2 2.9 °c SMY 9.4 6.5 12.6 24.6 51 96.3 120 98.5 81.7 42.4 7.6 3.9 mm RST2 7.9 4.2 7 4.7 6.2 7.3 7.3 6.6 7.2 6.7 5.6 2.9 mm RST3 4.08 1.63 3.18 3.01 2.43 2.12 2.55 2.17 2.35 1.7 2.73 1.92 mm PRW1 0.042 0.04 0.069 0.14 0.227 0.348 0.535 0.442 0.333 0.154 0.048 0.033 PRW2 0.346 0.286 0.257 0.44 0.471 0.648 0.667 0.617 0.573 0.429 0.24 0.289 DAYP 1.9 1.5 2.6 6 9.3 14.9 19.1 16.6 13.1 6.6 1.8 1.4 d Juchitepec, Juchitepec, México. (15039) OBMX 18.1 19.3 20.4 22.3 23.3 20.8 20 20 19.6 20 19.6 18.3 °c OBMN 4 5.2 6 7.8 9.6 9.7 9.3 9.2 9.3 7.7 6.2 5.2 °c SDTMX 3 3 3.8 3.7 3.7 3.3 2.5 2.6 2.9 2.9 2.8 2.6 °c SDTMN 2.6 2.2 2.6 2.1 2 1.8 1.1 1.3 1.5 1.9 2 2.4 °c SMY 17 12.5 14.1 26.3 73 134.6 145.8 139.7 120.9 50.1 12.9 5.2 mm RST2 17.4 6 9.4 4.9 8.2 8.4 7.6 8.2 8 9 7.2 6 mm RST3 2.71 1.53 3.02 2.38 2.98 1.79 2.03 2.98 2.53 2.19 3.44 2.88 mm PRW1 0.034 0.054 0.056 0.134 0.266 0.431 0.592 0.596 0.451 0.175 0.071 0.035 PRW2 0.412 0.364 0.304 0.484 0.523 0.701 0.726 0.72 0.691 0.428 0.25 0.16 DAYP 1.7 2.2 2.3 6.2 11.1 17.7 21.2 21.1 17.8 7.3 2.6 1.3 d 171
B1. Climatic data for the meteorological stations around the Texcoco District — Continued. Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Km. 2+120 (bombas), Ecatepec, Méx. (15040) OBMX 22.6 23.9 26.6 27.4 27.2 25.3 24.1 24.3 23.9 23.9 23.6 22.7 °c OBMN 2.5 3.8 6 8.4 10.6 11.6 11.1 11.1 10.9 8.9 5.4 4 °c SDTMX 2.8 2.7 2.8 2.8 2.7 2.6 1.9 1.8 2.3 2.2 2.3 2.2 °c SDTMN 2.6 2.6 2.6 2.4 2 1.8 1.6 1.6 2.1 2.7 2.9 2.7 °c SMY 6.7 6.4 12.5 23 53.9 108.3 114.8 95.3 88.3 43 11 5.2 mm RST2 5.2 2.8 5.1 4.8 7 8.3 7.3 6 8.7 8.5 5.7 3.2 mm RST3 1.67 2.19 2.64 2.93 2.55 2.27 1.58 1.62 2.53 2.95 1.47 1.25 mm PRW1 0.037 0.074 0.083 0.144 0.242 0.312 0.429 0.461 0.325 0.143 0.062 0.037 PRW2 0.333 0.192 0.239 0.424 0.474 0.669 0.607 0.552 0.51 0.469 0.191 0.226 DAYP 1.6 2.4 3 6 9.8 14.5 16.2 15.7 12 6.6 2.1 1.4 d Km. 27+250 Gran Canal, Ecatepec, Edo de Méx. (15041) OBMX 21.3 22.9 25.3 26.5 26.5 24.4 23 23.2 23 22.5 22.3 21.4 °c OBMN -0.6 0.6 3.2 5.6 7.7 9.7 9.5 9.3 9.2 6.4 2.3 0.8 °c SDTMX 3.2 2.6 3.2 3.2 2.9 3 2.2 2.3 2.4 2.8 2.7 2.5 °c SDTMN 3.2 3.4 2.9 2.7 2.5 2.6 2 2.1 2.5 3.3 3.6 3.4 °c SMY 10.5 7.7 17.9 22.4 53.7 110.8 126.3 110 105.7 46.5 10.1 5 mm RST2 8.3 4.8 7.9 5.6 7.8 8.4 7.7 7.9 9.9 7.8 4.6 2.6 mm RST3 2.4 2.34 3.23 5.05 3.12 2.22 1.97 2.11 2.39 1.9 1.82 1.46 mm PRW1 0.035 0.057 0.085 0.145 0.252 0.333 0.443 0.458 0.356 0.15 0.064 0.038 PRW2 0.372 0.22 0.33 0.374 0.453 0.645 0.668 0.564 0.544 0.444 0.27 0.356 DAYP 1.7 1.9 3.5 5.7 9.8 14.5 17.7 15.9 13.2 6.6 2.4 1.7 d La Grande, Atenco, México. (15044) OBMX 22.3 23.9 26.2 27.3 27.1 24.9 23.5 23.6 23.2 23.2 23.1 22.1 °c OBMN 0.4 1.5 4.2 6.5 8.3 10.1 9.5 9.2 8.9 6.4 2.9 1.5 °c SDTMX 2.8 2.7 2.9 3.1 2.8 2.9 2 2 2.5 2.6 2.5 2.5 °c SDTMN 2.7 3 2.7 2.3 2.3 2.2 1.8 1.8 2.4 3 3.5 3 °c SMY 9 5.5 17 27.6 55.1 106.1 112.5 119.9 96.2 39.7 11.3 6.2 mm RST2 7.7 4.3 7.5 5.8 7 7.7 7.5 8.6 9 7.9 6.2 5.3 mm RST3 3.31 4.4 3.01 3.52 2.41 1.97 2.54 2.54 2.66 3.29 2.94 2.99 mm PRW1 0.04 0.054 0.086 0.158 0.243 0.308 0.449 0.471 0.339 0.159 0.073 0.037 PRW2 0.313 0.273 0.359 0.452 0.521 0.679 0.645 0.603 I 0.578 0.454 0.253 0.244 DAYP 1.7 2 3.7 6.7 10.4 14.7 17.3 16.8 13.4 7 2.7 1.5 d Los Reyes, La Páz, México. (15050) OBMX 21.1 23 25.7 27.3 27.4 24.8 23.9 23.9 23.5 23.3 22.6 21.6 °c OBMN 4 5 8.2 10.2 11 .30 1 11.9 11.4 11.5 11 9 5.8 4.8 °c SDTMX 3.5 2.6 3.4 2.6 2.6 2.8 2 1.9 2.1 2.4 2.3 2.5 °c SDTMN 2.5 2.6 2.5 2.2 1.9 1.6 1.4 1.4 1.9 2.6 2.8 2.7 °c SMY 9.5 6.2 9.7 25.4 44.8 107.8 115 103.1 82.7 42.3 6.8 3 mm RST2 6.6 3.7 6.8 5 6.3 8.2 7.3 6.3 7.6 7.3 3.9 3.7 mm RST3 2.03 2.2 3.1 1.62 2.89 2.31 2.5 1.73 2.7 2.24 2.19 2.22 mm PRW1 0.038 0.05 0.053 0.15 0.235 0.351 0.52 0.49 0.311 0.147 0.053 0.025 PRW2 0.348 0.182 0.317 0.34 0.393 0.624 0.637 0.573 0.611 0.475 0.273 0.2 DAYP 1.7 1.6 2.2 5.6 8.7 14.5 18.3 16.6 13.3 6.8 2 0.9 d Nepantla, Tepetixpla (SMN), Edo de Méx (15060) OBMX 26.4 27.2 27.9 28.3 28.4 27.2 26.9 26.8 26.4 27.1 26.8 26.9 °c OBMN 8.9 9.5 10 10.3 10.4 9.7 9.1 9.3 9.3 9.8 9.2 8.9 °c SDTMX 1.7 1.6 1.2 1.5 1.3 2.4 2.7 2.2 2.8 2.2 1.3 1.9 °c SDTMN 1.7 1.7 1.5 1.6 1.7 2.1 2.1 2.1 2 1.7 1.6 1.5 °c SMY 19.1 6.4 7.4 12 39.6 184.1 161.7 171.6 186.9 -81.9 14.9 10.2 mm RST2 9.7 4.3 3.8 5.6 6.1 10.6 8.4 10.4 12.3 13.4 8.3 10.3 mm RST3 0.77 2.17 -0.26 1.47 0.83 1.94 1.82 2.9 2.52 2.09 1.3 2.85 mm PRW1 0.029 0.032 0.024 0.039 0.082 0.252 0.281 0.315 0.304 0.091 0.036 0.03 PRW2 0.368 0.25 0.167 0.279 0.451 0.695 0.679 0.644 0.632 0.61 0.171 0.107 DAYP 1.4 1.1 0.9 1.5 4 13.6 14.5 14.5 13.6 5.9 1.3 1 d 172
B1. Climatic data for the meteorological stations around the Texcoco District — Continued. Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Otumba, Otumba, México. (15065) OBMX 22.5 24.4 26.3 28.6 28.4 25.3 24.8 24.5 23.9 24 22.9 22 °c OBMN -0.5 1.3 3 5.7 7.9 9 8.4 8 8 5.1 2.3 0.9 °c SDTMX 3.9 4.3 5.8 5.5 5.5 5 4.3 4.4 4.1 4.7 4.6 4.1 °c SDTMN 2.9 3 2.9 2.1 2.4 2.7 2.2 2.4 2.9 3.5 3.2 3 °c SMY 8.8 11.2 24.3 38.4 54.3 91.1 89.3 89.3 63 37.2 2 9.6 6.6 mm RST2 6.1 6.4 8.1 7 6.8 7.6 7.3 9 8 9.1 2.6 5.7 mm RST3 1.44 3.3 4.13 2.97 2.09 2.17 1.6 2.35 3.01 2.34 1.53 3.18 mm PRW1 0.037 0.062 0.096 0.144 0.201 0.239 0.287 0.289 0.205 50.11 0.064 0.036 PRW2 0.225 0.25 0.4 0.469 0.479 0.622 0.55 0.498 0.504 0.403 0.338 0.25 DAYP 1.4 2.1 4.3 6.4 8.6 11.6 12.1 11.3 8.8 4.8 2.6 1.4 d Río Frio, Ixtapaluca, México (15082) OBMX 16.9 17.7 20.1 20.4 20.2 18.7 17.8 18 17.8 17.8 17.5 16.6 °c OBMN 2.4 1.7 0 1.5 3.3 4.6 4.2 4 3.9 1.5 0.9 2 °c SDTMX 2.6 2.7 2.8 3.4 2.9 2.8 2 1.8 2.1 2.5 2.3 2.4 °c SDTMN 2.4 2.6 2.6 2.1 2 2.4 2.2 2 2.5 2.7 2.6 2.4 °c SMY 12.8 13.2 18.6 48.6 108.2 182.4 178.3 168.1 149 72.4 13.6 9.9 mm RST2 9.1 6.5 7.3 6 7.3 8.3 7.6 6.9 7.5 8 3.1 4.1 mm RST3 2.6 2.57 2.99 1.95 1.8 1.42 1.84 1.47 1.82 4.3 2.06 1.76 mm PRW1 0.036 0.063 0.075 0.174 0.299 0.401 0.487 0.519 0.459 0.174 0.085 0.042 PRW2 0.39 0.316 0.375 0.572 0.65 0.761 0.782 0.718 0.703 0.667 0.379 0.463 DAYP 1.7 2.4 3.3 8.7 14.3 18.8 21.4 20.1 18.2 10.6 3.6 2.3 d Sn. Andrés, Chiautla, Chiaulta, Edo de Méx. (15083) OBMX 21.4 22.8 25.2 26.6 26.9 24.8 23.4 23.4 23 22.6 22.2 21.3 °c OBMN 1.1 2.2 4.8 7.1 8.9 10.3 9.7 9.5 9.2 6.8 3.7 2.2 °c SDTMX 2.7 2.5 2.7 2.8 2.7 2.9 2.1 1.8 2.2 2.4 2.5 2.2 °c SDTMN 2.7 3.1 2.6 2.3 2.2 2.2 1.8 1.8 2.4 2.9 3.3 2.9 °c SMY 10.7 5 13.7 27.1 52.7 99.7 114.9 102.1 83.7 41.6 7 4.8 mm RST2 9.8 3.5 6.4 4.6 6.5 7.4 7.1 6.4 7.4 6.9 4.6 2.9 mm RST3 3.1 2.95 3.43 1.86 2.25 2.32 2.26 2.52 3.25 2.47 2.75 1.24 mm PRW1 0.038 0.051 0.075 0.168 0.25 0.365 0.497 0.488 0.34 0.17 0.063 0.041 PRW2 0.354 0.208 0.314 0.432 0.477 0.655 0.664 0.646 0.585 0.441 0.169 0.171 DAYP 1.7 1.7 3.1 6.9 10 15.4 18.5 18 13.5 7.2 2.1 1.5 d Sn. Juan Ixhuatepec, Tlanaplantla, Edo de Méx. (15092) OBMX 21.7 23.3 25.8 27 26.7 24.8 23.4 23.4 22.9 22.7 22.4 21.6 °c OBMN 3.7 4.8 7.3 9.4 10.9 12 11.5 11.4 11.3 9.1 5.9 4.6 °c SDTMX 2.7 2.5 2.6 2.9 2.7 2.7 1.8 1.8 2.2 2.4 2.4 2.4 °c SDTMN 2.3 2.5 2.3 2 1.9 1.6 1.4 1.5 1.9 2.7 2.9 2.4 °c SMY 9.7 6.1 11.6 24.9 53.5 116.4 123.2 122.8 95.9 39.3 8.2 5.9 mm RST2 6.7 3.2 6 5.8 5.8 8.4 7.8 8.7 8.4 8.6 3.7 3.9 mm RST3 2.45 2.73 2.67 2.97 1.73 1.74 2.17 2.46 1.99 3.42 1.95 1.84 mm PRW1 0.039 0.064 0.074 0.157 0.25 0.378 0.512 0.471 0.338 0.168 0.072 0.038 PRW2 0.367 0.274 0.317 0.419 0.514 0.651 0.67 0.616 0.578 0.426 0.254 0.318 DAYP 1.8 2.3 3 6.4 10.5 15.6 18.9 17.1 13.3 7 2.6 1.6 d Sn. Juan Totolapán, Tepetlaoxtóc, Edo Méx. (15210) OBMX 18.5 19.3 21.5 22.5 22.7 20 19.7 19.5 19 19.6 19.3 18.5 °c OBMN 4.4 5 6.9 8.2 9.2 8.8 7.9 7.9 7.8 6.6 6 5.3 °c SDTMX 2.8 2.7 3.2 2.6 2.6 2.9 2.6 2.2 2.7 2.8 2.4 2.7 °c SDTMN 1.8 2.1 2.4 2.1 1.7 1.6 1.4 1.2 1.6 1.8 1.9 1.7 °c SMY 10.1 12.4 20.9 27 57.5 98.8 96.8 96.6 73.7 34.7 15.3 3.5 mm RST2 7 4.3 11.2 4.4 6.6 7.2 7.4 7.8 8.2 7.1 3.7 2.2 mm RST3 1.06 2.51 2.74 2.38 2.58 2.02 2.4 1.95 2.56 2.78 2.01 0.21 mm PRW1 0.031 0.074 0.067 0.155 0.267 0.295 0.435 0.383 0.293 0.13 0.064 0.02 PRW2 0.4 0.296 0.296 0.393 0.5 0.681 0.571 0.504 0.509 0.5 0.528 0.333 DAYP 1.5 2.7 2.7 6.1 10.8 14.4 15.6 13.5 11.2 6.4 3.6 0.9 d 173
B1. Climatic data for the meteorological stations around the Texcoco District — Continued. Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Sn. Luis Ameca, Tenango del Aire, Edo de Méx. (15094) OBMX 21.3 22.3 24.7 26.1 26 23.7 22.4 22.5 22.3 22.9 22.4 21.6 °c OBMN 1.9 3.2 5.9 7.8 9.2 10.5 10 9.7 9.7 7 3.5 2.5 °c SDTMX 2.5 2.4 2.6 2.6 2.7 2.9 2 2 2.3 2.5 2.4 2.6 °c SDTMN 3.3 3.6 3.1 2.6 2.5 2.4 2.1 2.1 2.4 3.3 3.9 3.4 °c SMY 15.4 7.5 11.6 26.4 65.7 113 135.4 132.7 104.7 48.6 8.4 5.3 mm RST2 12.5 4.5 6.5 5.6 7.3 7.8 6.9 7.4 7.1 8.1 4 5.1 mm RST3 3.36 2.5 3.09 2.18 2.79 2.15 2.08 2.03 2.3 2.24 2.2 2.53 mm PRW1 0.038 0.047 0.059 0.139 0.281 0.388 0.622 0.565 0.43 0.176 0.061 0.03 PRW2 0.442 0.347 0.394 0.405 0.471 0.688 0.735 0.717 0.707 0.438 0.279 0.378 DAYP 2 1.9 2.7 5.7 10.8 16.6 21.7 20.7 17.8 7.4 2.3 1.4 d Sn. Martín de las Pirámides, Méx. (15097) OBMX 24 25.6 28.1 29.2 29 26.6 25.2 25.5 25.6 25.1 24.5 24.2 °c OBMN 1.7 2 3.7 5.9 8.6 9 8.8 8.6 8.5 6.7 3.9 2.5 °c SDTMX 3 3.4 3.3 3.4 3.5 3.4 2.9 3.2 3.5 3.7 3.5 3.4 °c SDTMN 2.6 2.7 2.6 3 2.5 3 2.8 2.7 3.1 3.2 3.2 3 °c SMY 9.4 6.5 19.5 31 76.2 110.3 120.4 99.6 73.6 43 12.5 8.1 mm RST2 6.3 4.8 8.3 5.2 9.3 9.9 8.2 7.5 8.6 6.6 5.9 6.8 mm RST3 1.16 3.21 2.76 1.88 3.02 2.21 1.75 2.21 2.26 1.81 2.67 2.72 mm PRW1 0.025 0.053 0.077 0.128 0.271 0.233 0.361 0.376 0.242 0.133 0.058 0.038 PRW2 0.45 0.222 0.385 0.489 0.494 0.663 0.602 0.543 0.526 0.495 0.368 0.261 DAYP 1.3 1.8 3.5 6 10.8 12.3 14.7 14 10.1 6.5 2.5 1.5 d Sn. Miguel Tlaixpán, Texcoco, México. (15101) OBMX 20.2 21.3 23.8 25.3 25.2 22.9 21.6 21.5 21.1 21.4 20.9 20.1 °c OBMN 3.7 4.7 6.9 8.5 9.5 10.2 9.5 9.5 9.4 7.7 5.3 4.7 °c SDTMX 2.7 2.6 2.8 2.9 2.8 3 2.2 2.1 2.2 2.4 2.3 2.3 °c SDTMN 2.2 2.5 2.6 2.2 1.7 1.8 1.5 1.5 1.9 2.5 2.6 2.6 °c SMY 13.6 7 14.3 29.9 60.3 115.5 111.8 109 88.4 49.3 9.7 5 mm RST2 10.7 4.6 5.8 6.5 7.3 8.3 6.9 6.6 7.2 8.1 4.3 4.3 mm RST3 4.25 2.32 1.84 3.93 2.55 2.47 2.62 2.03 2.91 3.63 1.75 2.15 mm PRW1 0.039 0.047 0.064 0.133 0.217 0.317 0.466 0.43 0.325 0.175 0.074 0.032 PRW2 0.38 0.19 0.269 0.409 0.496 0.664 0.609 0.593 0.569 0.384 0.141 0.278 DAYP 1.9 1.6 2.5 5.5 9.3 14.6 16.9 15.9 12.9 6.9 2.4 1.3 d Sn. Pedro Nexapa, Amecameca, Edo de Méx. (15103) OBMX 19 20.3 22.4 23.7 23.3 20.3 19.1 19.2 18.8 19.6 19.7 18.8 °c OBMN 4.1 4.8 6.6 7.9 9 9.1 8.3 8.3 8.2 6.6 5.4 4.3 °c SDTMX 2.7 2.3 3 2.8 3.3 2.9 2.3 2.2 2.3 2.5 2.6 2.4 °c SDTMN 2.1 2.2 2.5 1.9 1.5 1.6 1.4 1.4 1.7 1.9 2 2.2 °c SMY 18.5 9.6 17.2 43 92 178.9 151.3 149.6 149.8 63.8 15.1 7.5 mm RST2 14.2 6.5 7.1 8.8 7.8 9.5 7.8 8.3 8.3 7.4 4.8 4.1 mm RST3 2.3 2.4 3.67 2.66 2.34 2.43 2.78 2.71 2.28 2.25 2.14 1.54 mm PRW1 0.042 0.053 0.062 0.14 0.302 0.494 0.596 0.574 0.527 0.207 0.088 0.045 PRW2 0.37 0.291 0.41 0.516 0.632 0.777 0.8 0.787 0.769 0.594 0.363 0.288 DAYP 1.9 2 3 6.7 14 20.7 23.2 22.6 20.9 10.5 3.6 1.9 d Sn. Rafael, Tlalmanalco, México. (15106) OBMX 19.4 20.5 23.1 24.7 24.1 21.2 19.7 19.7 19.5 20 20.1 19.5 °c OBMN 4.1 4.8 7.1 8.4 9.2 9.7 9.2 9.2 9.3 7.7 5.6 4.6 °c SDTMX 2.4 2.2 2.6 2.7 2.8 2.8 1.8 1.9 2.1 2.2 2.2 2.2 °c SDTMN 1.9 2.2 2.1 1.8 1.6 1.5 1.2 1.2 1.4 2 2.3 2 °c SMY 21.9 11.3 17.7 39 82.6 192.2 217.5 216.2 162.8 86.6 11.9 7.9 mm RST2 15.6 6.2 7.8 6.6 7.1 10.8 9.6 10.2 9.2 9.8 3.8 5 mm RST3 2.34 3.09 2.77 2.39 2.09 2.59 2.18 2.1 2.67 2.85 2.26 2.37 mm PRW1 0.05 0.049 0.071 0.171 0.287 0.436 0.659 0.617 0.52 0.24 0.085 0.044 PRW2 0.321 0.439 0.443 0.47 0.632 0.794 0.823 0.809 0.772 0.611 0.378 0.304 DAYP 2.1 2.3 3.5 7.3 13.6 20.4 24.4 23.7 20.8 11.8 3.6 1.8 d 174
B1. Climatic data for the meteorological stations around the Texcoco District — Continued. Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Sn. Jerónimo, Xonacahuacán, Edo de Méx. (15090) OBMX 24 25.5 28.1 29.3 29 26.6 24.9 25 24.7 25 24.9 24 °c OBMN 2.5 3.3 5.3 7.3 9.1 10.2 10 9.8 9.4 7.2 4.3 3.1 °c SDTMX 3.1 3 3.3 3 2.9 3.1 2 2.1 2.7 2.7 2.8 2.7 °c SDTMN 3.4 3.4 3 2.4 2.2 2.5 1.9 1.9 2.6 2.9 3.2 2.9 °c SMY 11.5 7.2 18 32.3 55.9 108.4 116.6 112.6 87 43.4 13.2 6.4 mm RST2 8.1 3.6 7.7 5.7 7.4 9.9 8.6 9.7 9.6 7.5 6.8 5.3 mm RST3 2.6 2.19 2.6 2.32 2.31 2.57 2.73 3.24 2.61 1.56 2.66 1.42 mm PRW1 0.044 0.051 0.072 0.139 0.215 0.262 0.378 0.368 0.265 0.133 0.065 0.027 PRW2 0.279 0.304 0.279 0.444 0.447 0.649 0.556 0.512 0.531 0.433 0.246 0.321 DAYP 1.8 1.9 2.8 6 8.7 12.8 14.3 13.3 10.8 5.9 2.4 1.2 d Sn. José Tepetlaoxtoc, México (15091) OBMX 17.6 18.9 21.4 22.1 21.5 19 17.8 18.1 18.1 18.4 18.4 I 17.70 °c OBMN 2.8 3.4 5.8 6.8 7.8 8.4 7.7 7.6 7.7 6.5 4.3 3.1 °c SDTMX 2.6 2.4 2.5 2.8 2.5 3 1.9 1.8 2.2 2.9 3 2.8 °c SDTMN 1.8 2.1 1.7 2.2 1.7 1.7 1.6 1.6 2 2 2.3 2.2 °c SMY 5.4 4.6 20.3 25.7 74 124.3 129.6 114.1 96.7 60.6 13 6.4 mm RST2 2.6 2.4 5.8 4.9 6 8.3 8.2 6.6 9.1 7.4 3.6 5.8 mm RST3 1.33 1.58 2.93 1.81 2.01 2.05 2.38 1.47 3.05 1.44 2.14 1.25 mm PRW1 0.052 0.047 0.12 0.158 0.382 0.327 0.46 0.491 0.324 0.16 0.11 0.03 PRW2 0.333 0.333 0.359 0.4 0.471 0.723 0.689 0.621 0.577 0.567 0.233 0.364 DAYP 2.3 1.9 4.9 6.3 13 16.3 18.5 17.5 13 8.4 3.8 1.4 d Tepexpán, Acolmán, México. (15124) OBMX 22.7 24.4 27 28.2 28.1 25.7 24.2 24.5 24.1 24.2 23.9 22.8 °c OBMN 0.8 1.8 4.3 6.6 8.6 10.2 9.6 9.3 9 6.4 3.1 1.8 °c SDTMX 2.9 2.8 3.1 3.1 2.8 3.2 2.1 2.1 2.6 2.8 2.5 2.4 °c SDTMN 2.6 2.8 2.5 2.2 2.1 2.2 1.8 1.9 2.6 3 3.2 2.8 °c SMY 9.8 5.3 15.1 24.8 51.6 108.4 120.7 118.7 91.7 39.8 12.2 6.6 mm RST2 7.2 4.2 6.4 4.6 6.8 8.4 7.5 5 8.5 8.9 7.8 7.4 5.4 mm RST3 3.39 3.88 2.68 2.23 2.24 2.71 2.29 3.38 3.27 3.09 3.4 1.65 mm PRW1 0.042 0.055 0.092 0.16 0.229 0.347 0.494 4 0.500 0.324 0.149 0.079 0.031 PRW2 0.346 0.212 290 0.426 0.514 0.661 0.653 3 0.629 0.596 0.471 0.268 0.333 DAYP 1.9 1.9 3.6 6.5 9.9 15.2 18.2 17.8 13.4 6.8 2.9 1.4 d Texcoco, Texcoco (DGE), México (15125) OBMX 23.3 25 27.3 28.7 29.1 26.5 24.7 25 24.4 25.2 24.8 23.7 °c OBMN 0.2 2.1 3.9 6.5 8.5 9.3 8.6 8.7 8.3 5.5 2.5 1.3 °c SDTMX 3.6 3.2 4.3 4.7 4.7 4.3 3.7 3.8 3.9 4.2 3.2 3.1 °c SDTMN 2.5 2.8 2.7 2.1 2.2 2.4 1.9 2.1 2.5 3 3.1 2.6 °c SMY 10.3 6.1 13.7 26.2 48.1 108.9 120.6 110.6 86 38.6 8.8 6.7 mm RST2 8.1 3.6 6.4 4.5 5.5 7.2 7.1 6.6 7.3 6 5.2 4 mm RST3 3.8 2.45 3.1 2.89 2.31 2.36 3.38 2.11 2.76 2.36 2.79 1.28 mm PRW1 0.042 0.05 0.082 0.157 0.234 0.383 0.538 0.477 0.339 0.155 0.065 0.041 PRW2 0.358 0.26 0.281 0.443 0.493 0.67 0.695 0.654 0.611 0.477 0.242 0.244 DAYP 1.9 1.8 3.2 6.6 9.8 16.1 19.8 18 14 7.1 2.4 1.6 d Texcoco, Texcoco (SMN), México (15163) OBMX 22.7 24.4 27.6 28.9 28.8 26.9 25.1 25.1 24.5 23.8 23.3 21.9 °c OBMN 3.3 3.9 6.7 8.5 9.7 10.3 10 9.9 9.5 7.4 4.9 3.7 °c SDTMX 2.8 2.8 2.8 2.7 2.9 3 2.6 2.3 2.3 2.7 2.7 2.6 °c SDTMN 2.6 2.9 2.8 2.4 2 2.8 2.5 2.1 2.7 3 3.1 2.6 °c SMY 15.7 5.6 14.6 41.2 75.8 104.2 130 153.4 87.3 47.4 4.2 5 mm RST2 10.9 4.5 4.8 7.1 8.5 8 7.4 8.9 7.5 8.9 2 2.3 mm RST3 3.58 2.17 1.79 2.04 1.81 1.6 2.51 1.2 1.9 1.9 0.67 1.13 mm PRW1 0.039 0.026 0.074 0.132 0.206 0.267 0.418 0.396 0.268 0.131 0.037 0.031 PRW2 0.385 0.286 0.275 0.352 0.421 0.52 0.57 0.585 0.486 0.373 0.167 0.235 DAYP 1.9 1 2.9 5.1 8.1 10.1 15.3 15.1 10.3 5.4 1.3 1.2 d 175
B1. Climatic data for the meteorological stations around the Texcoco District — Continued. Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Tlalmanalco, Tlalmanalco, Méx. (15280) OBMX 19.9 22 24.3 25 25 23.5 21.9 22.3 22.4 21.5 21.5 20.9 °c OBMN 2.2 5.3 7.8 8.4 9.8 10.3 9.9 9.8 9.1 6.9 4.9 4.5 °c SDTMX 2.5 2.7 2.7 3 3.9 3.4 2.5 2.8 2.5 3.1 2.5 2.6 °c SDTMN 3.2 2.9 3.6 2.8 2.4 2.2 2.1 2.1 2.2 2.6 2.6 2.6 °c SMY 0 5.3 7.7 35.4 45.8 166 162 134.6 109.1 34.1 7.6 3.7 mm RST2 0 3.2 17.3 7.7 5.2 7 9.8 5.9 7.4 4.1 3.8 4 mm RST3 0 1.6 2.38 2.63 0.94 1.31 2.97 1.08 3.54 1.67 0.15 1.42 mm PRW1 0 0.042 0.017 0.063 0.186 0.407 0.495 0.556 0.328 0.265 0.022 0.021 PRW2 0 0.25 0.333 0.606 0.333 0.752 0.624 0.631 0.625 0.153 0.444 0.286 DAYP 0 1.5 0.8 4.1 6.8 18.6 17.6 18.6 14 7.4 1.1 0.9 d Tultepéc, Tultepéc, México (15129) OBMX 22.4 23.7 25.6 27.7 27.4 25.5 24.4 24.3 23.8 23.6 23.4 22.6 °c OBMN 1.6 2.6 5 7.1 9 10.1 9.7 9.8 9.8 7.6 4.4 2.9 °c SDTMX 3.4 3 3.5 3.6 3.7 3.6 3.1 2.9 2.8 3.1 2.8 2.6 °c SDTMN 2.5 2.6 2.7 2.8 2.6 2.4 1.9 1.8 2.2 2.8 2.9 2.9 °c SMY 9 5.4 12.4 27.2 51.7 116 123.5 121.1 106.3 49.6 13.7 5.7 mm RST2 7.2 4.2 7.5 9.3 7.6 9.9 8.1 8.5 9.8 11.1 7.7 3.4 mm RST3 1.29 2.36 2.58 3.48 3.54 2.27 2.04 2.19 1.79 2.68 2.01 1.64 mm PRW1 0.03 0.039 0.047 0.098 0.196 0.26 0.392 0.372 0.269 0.092 0.05 0.029 PRW2 0.286 0.31 0.377 0.398 0.44 0.639 0.58 0.542 0.528 0.531 0.278 0.415 DAYP 1.3 1.5 2.2 4.2 8 12.6 15 13.9 10.9 5.1 1.9 1.5 d Xochihuacán, Otumba, México (15135) OBMX 17.8 18.6 21.3 21.7 21.5 19.2 18 18.20 118 18.6 18.20 . 17.6 °c OBMN 2.4 3.2 5.2 6.5 7.7 8.4 7.6 7.5 7.6 5.9 3.8 3.2 °c SDTMX 2.7 2.7 3.2 2.8 2.4 2.6 1.9 1.9 2.3 2.5 2.3 2.6 °c SDTMN 2 2.1 2.4 2.1 1.7 1.9 1.8 1.8 2.2 2.3 2.3 2.4 °c SMY 8.5 9.2 25.8 35 73.4 116.5 109.4 103.9 85.9 45.9 9.4 5.8 mm RST2 6.5 5.6 7.4 7.1 7.5 7.7 7.1 7.4 8.6 7.2 4.8 3.3 mm RST3 1.67 3 2.68 3.4 2 1.9 2.06 1.68 2.8 1.55 2.88 1.68 mm PRW1 0.03 0.046 0.097 0.129 0.23 0.346 -0.36 0.377 0.266 0.124 0.07 0.028 PRW2 0.393 0.324 0.333 0.473 0.497 0.613 0.592 0.549 0.527 0.482 0.098 0.407 DAYP 1.5 1.8 3.9 5.9 9.7 14.2 14.6 14.10 . 10.8 6 2.2 1.4 d El Vigia, Tlanepantla, Morelos. (17066) OBMX 22 23.5 25.6 27.7 25.4 21.9 21.4 21.8 21 23.1 23.1 22.6 °c OBMN 7.7 8.2 9.4 12 11.7 11.8 10.3 10.7 10.8 10.1 9 8.3 °c SDTMX 2 2.2 2.5 2.3 6.1 6 5.2 5.6 6 1.4 1.9 1.5 °c SDTMN 2.1 1.8 2.3 2 3.1 2.8 3.4 2.9 3 1.8 1.8 1.8 °c SMY 11.1 12.7 10.6 24.9 87.5 249.5 220.8 202.5 195.5 71 23.1 I 0.8 mm RST2 12.7 4.7 7 7.4 8.6 11.8 12.3 12.7 13.4 12.8 11.3 0.8 mm RST3 1.43 1.48 3.13 2.81 1.93 0.50 1.93 1.91 1.45 2.79 1.41 1 mm PRW1 0.019 0.055 0.045 0.072 0.257 0.5 0.43 0.371 0.373 0.171 0.057 0.019 PRW2 0.2 0.375 0.4 0.567 0.493 0.761 0.718 0.742 0.648 0.472 0.353 0 DAYP 0.7 2.3 2.1 4.3 10.4 20.3 18.7 18.3 15.4 7.6 2.4 0.6 d Huecauasco E-7, Ocuituco, Morelos (17045) OBMX 23.7 24.5 26.6 27.8 27.7 25.2 22.1 22.6 22.3 22.4 23.7 22.9 °c OBMN 7.6 6.6 8.3 10.3 12.9 12.5 11.5 11.3 12.1 10.7 9.4 7.4 °c SDTMX 2.6 3.4 3 3.6 4.2 3.8 3.1 3 2.6 2.9 2.5 3.3 °c SDTMN 1.8 2.2 2.6 1.9 1.5 1.1 1.2 1.1 0.8 1.4 1.2 2 °c SMY 7.7 18 4 24.4 58.2 197.6 198.1 193.3 1196.1 84.6 33.4 2.6 mm RST2 13.6 6.5 2.7 7.7 7.7 13.4 9.5 12.1 9.8 7.9 11.3 2 mm RST3 -0.24 -0.04 0.79 2.92 1.56 1.04 1.49 1.95 1.88 0.98 0.86 -0.37 mm PRW1 0.016 0.057 0.025 0.087 0.177 0.348 0.381 0.398 0.442 0.209 0.041 0.025 PRW2 0 0.2 0.143 0.438 0.393 0.552 0.72 0.638 0.71 0.479 0.609 0 DAYP 0.5 1.9 0.9 4 7 13.1 17.9 16.3 18.1 8.9 2.9 0.8 d 176
B1. Climatic data for the meteorological stations around the Texcoco District — Continued. Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Sn. Juan Tlacotenco, Morelos (17039) OBMX 20 21.7 23.8 24.9 24.1 20.6 19.4 19.6 18.9 19.7 20.3 20.1 °c OBMN 7.6 8.4 9.6 11.1 12.1 12 11.2 11.3 11.3 10.5 9.2 8.6 °c SDTMX 2.4 2.5 2.7 3 3.4 3 1.9 1.9 2 2.2 1.9 1.8 °c SDTMN 1.6 1.8 1.8 1.5 1.4 1.1 0.9 0.9 1.1 1.5 1.6 1.6 °c SMY 19.3 10.6 19.7 39.3 85.8 283.5 392.5 306.7 254.1 107.5 15.1 6.9 mm RST2 15.5 3.9 8.1 6.5 12 15.2 17 14.8 13.8 12.8 7.7 3.8 mm RST3 1.89 1.69 1.86 2.71 4.7 2.28 1.48 2.01 1.49 2.12 1.22 1.11 mm PRW1 0.026 0.06 0.052 0.149 0.259 0.44 0.529 0.614 0.393 0.172 0.049 0.038 PRW2 0.556 0.265 0.314 0.456 0.512 0.782 0.8 0.746 0.768 0.614 0.353 0.28 DAYP 1.7 2.1 2.2 6.4 10.8 20.1 22.5 21.9 18.9 9.6 2.1 1.6 d Tlacualera, Tlacualera,Morelos. (17021) OBMX 16.4 16.8 20.7 21.9 I 21.90 19.9 18.5 17.8 17.9 18 17.5 17.1 °c OBMN 5.3 5.6 7.8 9.6 I 10.70 10.4 9.5 9.5 9.4 8.5 7 5.8 °c SDTMX 4.6 5.3 4.1 5 5.3 4.5 4.9 4.2 4.5 4.6 4.1 4 °c SDTMN 2.1 2.2 2.8 3 3 2.7 2.6 2.2 2.4 2.1 2.4 2.2 °c SMY 20.8 9.3 9 15.7 59.5 174.8 177.7 174.3 165.1 61.9 12.5 7.3 mm RST2 17.5 4.8 6.1 7.6 9.7 14 11.1 10.1 10.3 11.1 6.5 9.3 mm RST3 1.44 1.08 1.43 1.67 1.94 2.25 2.29 1.69 1.64 1.57 1.3 2.04 mm PRW1 0.025 0.04 0.033 0.051 0.152 0.261 0.433 0.443 0.341 0.128 0.047 0.021 PRW2 0.444 0.256 0.381 0.356 5 0.448 0.637 0.613 0.6 0.634 0.417 0.294 0.346 DAYP 1.3 1.4 1.6 2.2 6.7 12.6 16.4 16.3 14.5 5.6 1.9 1 d Sn. Juan Totolapán, Tepetlaoxtóc, Méx. (17051) OBMX 24.8 27.3 27.3 30.7 30.4 29.9 25.4 26.4 26.1 26.5 25.5 25.1 °c OBMN 7.3 7.3 8.9 9.8 12 12.2 11.4 11 11.3 10 9.6 8.7 °c SDTMX 2.7 3.6 4.1 4.5 5.6 4.6 3.8 4.3 3.8 3.6 3.5 3.3 °c SDTMN 1.4 1.9 2.6 1.8 1.9 1.4 1.2 1.1 1.3 1.1 1.4 1.7 °c SMY 2.3 16 2.8 25.5 69.2 145.4 190.5 147.4 153.6 78.1 21.9 1.3 mm RST2 0.9 3.4 2.2 15.7 6 12.1 14.8 10 11.2 8.8 7.3 1.5 mm RST3 1.93 -0.34 0.19 2.03 1.4 0.37 2.49 2.25 1.48 0.86 0.64 0 mm PRW1 0.016 0.053 0.016 0.054 0.153 0.229 0.367 0.288 0.298 0.155 0.045 0.012 PRW2 0 0.353 0.2 0.368 0.5 0.385 0.608 0.537 0.576 0.444 0.375 0 DAYP 0.5 2.1 0.6 2.4 7.3 8.1 15 11.9 12.4 6.8 2 0.4 d E.T.A. 118 Yecapixtla, Morelos. (17043) OBMX 25.1 26.1 29.1 30.8 30.3 27.6 25.7 25.9 25.2 25.8 25.7 25.4 °c OBMN 8.4 9.9 11.7 14 15.7 16.6 15.1 15.1 15.50 114.2 11.6 10.3 °c SDTMX 1.9 2.2 2.6 1.7 2.2 2.9 1.7 1.6 1.6 1.6 1.7 1.5 °c SDTMN 2 2.2 2.6 2.2 2.2 1.9 1.6 1.7 1.4 1.9 2.4 2.2 °c SMY 19 10.1 2.9 11.3 74 197 196.6 197.4 170.2 84.1 17.7 4.1 mm RST2 10.2 5 4.1 2.9 7.6 12.5 11.2 12.4 10.5 12.2 6.7 3.5 mm RST3 1.01 0.9 1.35 0.61 1.49 1.85 2.74 3.38 1.95 3.33 0.79 0.56 mm PRW1 0.03 0.047 0.021 0.074 0.202 0.402 0.511 0.442 0.404 0.205 0.05 0.021 PRW2 0.462 0.333 0.167 0.304 0.486 0.677 0.719 0.725 0.74 0.5 0.389 0.167 DAYP 1.6 1.9 0.8 2.9 8.8 16.6 20 19.1 18.3 9 2.3 0.8 d Sn. Pedro B. Juárez E-1, Puebla. (21193) OBMX 19.7 20.5 21.7 24 24.5 23.7 21.7 21.6 22.1 20.5 21.5 21.7 °c OBMN 6.9 7 7.8 10.4 11.5 11.5 10.1 10 10.2 9.1 6.7 6.4 °c SDTMX 4.3 2.3 3 3 3.4 3.3 2.7 2.8 2.5 2.9 2.7 2.6 °c SDTMN 1.9 1.7 2.4 1.8 1.7 1.3 1.3 1.6 1.4 1.7 2.6 2.3 °c SMY 6.1 8 7.6 20.5 88.1 177.3 180.2 152.9 173.5 43.9 8 0.7 mm RST2 6.7 5.3 3.6 5 10.1 12.9 13 12.3 11.5 5.8 4.7 0 mm RST3 -0.33 1.57 0.91 1.62 2.95 2.03 3.02 3.31 1.62 1.8 2.78 0 mm PRW1 0.015 0.042 0.034 0.085 0.233 0.362 0.467 0.359 0.402 0.171 0.031 0.004 PRW2 0.2 0.286 0.438 0.444 0.511 0.664 0.563 0.628 0.643 0.413 0.467 0 DAYP 0.6 1.6 1.8 4 10 15.6 16 15.2 15.9 7 1.7 0.1 d 177
B1. Climatic data for the meteorological stations around the Texcoco District — Continued. Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Sta. Rita Tlahuapán, Puebla (21096) OBMX 20.1 21.6 23.6 24.5 23.9 22.5 21.7 21.8 21.5 21.5 20.8 19.8 °c OBMN 3.3 3.6 5.1 6.4 7.6 7.7 7.6 7.4 7.5 6.3 5.3 3.7 °c SDTMX 3.1 2.5 2.8 3 3.1 3.1 2.6 2.6 2.5 2.6 2.7 2.5 °c SDTMN 2.2 2 2 1.8 1.7 1.9 1.7 1.6 1.7 2 2.3 2.5 °c SMY 11.7 8.2 14 37.6 100.2 159.9 155.5 136.2 139.3 68 17.8 6.8 mm RST2 9.9 4.3 7.5 5.7 8.2 11.2 8.7 7.5 9.1 9.2 5.7 2.9 mm RST3 4.69 0.64 2.46 2.5 2.54 2.61 2.07 1.64 2.66 2.42 2.53 1.06 mm PRW1 0.043 0.036 0.056 0.172 0.26 0.395 0.448 0.375 0.447 0.184 0.072 0.039 PRW2 0.326 0.333 0.333 0.431 0.647 0.641 0.659 0.647 0.546 0.47 0.422 0.275 DAYP 1.8 1.4 2.4 7 13.2 15.7 17.6 16 14.9 8 3.3 1.6 d Tochimilco D-1, Puebla (21196) OBMX 23.1 23.5 24.9 27.8 26.7 24.4 24.5 25.3 24.7 24.8 24.2 23.8 °c OBMN 8 8.6 9.3 11.7 12.4 13.3 12.5 12.3 12.8 11.2 9.8 9.1 °c SDTMX 2 2.4 2.5 3.2 3.4 2.8 2.9 2.7 2.4 2.8 2.2 2.1 °c SDTMN 1.7 1.8 2.5 1.8 1.5 1.2 1.3 1.1 1.2 1.8 1.9 2.1 °c SMY 10.5 17.7 5.6 37.4 86.5 203.3 200.7 115.8 168 53.1 9.1 2.7 mm RST2 9.2 6 2.5 8.2 7.7 11.1 12.3 8.7 9.3 7.4 6 6.5 mm RST3 0.36 2.28 0.95 1.48 1.82 1.17 2.16 2.51 2.11 1.67 1.35 0.49 mm PRW1 0.018 0.079 0.042 0.108 0.301 0.541 0.442 0.511 0.444 0.15 0.043 0.015 PRW2 0.286 0.333 0.313 0.5 0.509 0.634 0.699 0.507 0.66 0.441 0.267 0 DAYP 0.8 3 1.8 5.3 11.8 17.9 18.4 15.8 I 17.00 6.6 1.7 0.4 d Amoxac de Guerrero, Tlaxcala. (29042) OBMX 23.1 24.1 25.4 26 27.2 25.1 25.4 25.8 23.5 24.9 24.3 23.3 °c OBMN 2.5 3.1 4.5 5 6.6 8.8 7.2 6.3 6.9 5.5 4.5 4.2 °c SDTMX 2.7 3.3 3.4 4 4.6 6.2 5.7 5.8 4 4.2 3.6 3.1 °c SDTMN 2.2 2.2 2.7 1.5 2.2 6.6 2.9 2.8 2.6 2.3 2.5 3.5 °c SMY 4.3 3.6 10.1 22.5 50 99.6 134.8 87.7 90.6 26 9.2 0.9 mm RST2 1.2 1.3 6.8 3.2 9.2 7.7 6.6 7.5 10.2 4.1 3.2 0 mm RST3 0.69 0.41 1.65 1.05 2.48 1.67 1 2.19 2.69 2.34 0.62 -2.01 mm PRW1 0.008 0.059 0.038 0.104 0.138 0.276 0.351 0.286 0.217 0.137 0.042 0.01 PRW2 0.789 0 0.143 0.447 0.481 0.579 0.657 0.532 0.654 0.364 0.345 0 DAYP 1.2 1.6 1.3 4.8 6.5 11.9 15.7 11.8 11.6 5.5 1.8 0.3 d Cuautla, Calpulalpán, Tlaxcala. (29006) OBMX 19 20.1 22.7 23.6 23.2 21.1 20.1 20.5 19.8 19.9 19.6 18.9 °c OBMN 2.9 3.5 5.4 7 8.2 8.7 8 7.9 8.2 6.6 4.6 3.5 °c SDTMX 3.1 3 3.2 3.1 2.7 3 2.3 2.2 2.7 2.8 2.6 2.6 °c SDTMN 2 2 2.2 2 1.6 1.6 1.5 1.5 1.8 2.2 2 2.2 °c SMY 11.2 11.1 24.4 37.6 72.2 122.5 117.2 105.8 85.30 48.7 8.7 5.4 mm RST2 6.3 5.1 7.1 5 6.6 8.1 6.9 7.6 7.1 6.9 4.6 2.8 mm RST3 2.35 2.7 2.64 1.92 2.98 2.22 1.91 2.54 2.02 2.2 3.42 1.77 mm PRW1 0.047 0.073 0.114 0.175 0.276 0.377 0.445 0.462 0.362 0.173 0.075 0.047 PRW2 0.375 0.339 0.347 0.573 0.604 0.72 0.701 0.64 0.596 0.538 0.274 0.318 DAYP 2.2 2.8 4.6 8.7 12.7 17.2 18.5 17.4 14.2 8.5 2.8 2 d Esc. Agrop. Nanacamilpa, Tlaxcala (29039) OBMX 18.4 19.4 22.3 22.6 22.6 20.8 19.3 19.60 119.4 20 19.2 18.8 °c OBMN 1 1.6 3.7 5.7 7.4 7.7 7.1 6.8 6.5 5.2 2.8 2 °c SDTMX 3.3 2.9 3 3.3 2.9 3.3 2 1.9 2.6 3.2 2.5 2.5 °c SDTMN 2 2.4 2.2 1.9 1.7 2.2 1.8 1.9 2.6 2.4 2.4 2.5 °c SMY 8.5 11.9 18.1 37.5 81.6 127 145.9 139.4 112.8 63.5 16.8 6 mm RST2 4.8 2.7 6.1 6.7 8.9 8.9 7.6 8.3 8.9 7.4 5.8 3.5 mm RST3 1.45 0.89 1.44 2.04 3.05 1.75 1.95 2.31 2.46 1.37 1.78 1.42 mm PRW1 0.038 0.076 0.08 0.141 0.259 0.375 0.526 0.472 0.434 0.188 0.07 0.042 PRW2 0.444 0.452 0.286 0.474 0.596 0.619 0.728 0.702 0.603 0.506 0.37 0.214 DAYP 2 3.4 3.1 6.3 12.1 14.9 20.4 19 15.7 8.6 3 1.6 d 178
B1. Climatic data for the meteorological stations around the Texcoco District — Continued. Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec La Venta, Calpulalpán, Tlaxcala. (29013) OBMX 17.7 19 21.6 22.3 21.6 19.7 18.6 18.6 17.9 18.4 18.5 17.8 °c OBMN -1.5 -0.5 1 3.5 5.2 6.5 6 6 6.3 4 0.8 -0.3 °c SDTMX 2.9 2.7 2.9 3 2.6 3.1 2 2.2 2.5 2.7 2.4 2.5 °c SDTMN 2.6 3 2.6 2.2 2.2 2.6 2.2 2.2 2.7 2.8 2.9 3.1 °c SMY 12.6 12.1 18.2 38.1 73.2 133 127.2 110.7 102.3 59.5 11.1 7.3 mm RST2 6 4.8 6.9 5 6.8 8.9 8.5 7.2 7.7 9 3.4 3.5 mm RST3 1.6 3.71 2.7 2.54 1.92 3.12 2.85 2.62 2.13 3.75 1.81 2.23 mm PRW1 0.059 0.075 0.094 0.181 0.289 0.35 0.508 0.521 0.415 0.196 0.091 0.054 PRW2 0.333 0.333 0.258 0.56 0.556 0.7 0.646 0.58 0.614 0.52 0.281 0.383 DAYP 2.5 2.8 3.5 8.7 12.2 16.2 18.3 17.2 15.5 9 3.4 2.5 d Límites Calpulalpán-Tlaxcala (29014) OBMX 17.1 18.1 20.4 21.1 20.5 18.4 17.3 17.2 16.9 17.1 17.6 17 °c OBMN 2.3 2.7 4.5 6.1 7 8 7.3 7.3 7.2 6 4 3 °c SDTMX 2.9 2.6 2.7 3.1 2.6 3.3 2 2 1.8 2.6 2.7 2.7 °c SDTMN 1.9 2.1 2.2 2.1 1.7 1.6 1.5 1.5 1.7 1.9 2 2.1 °c SMY 5.6 6.8 13.2 41.5 74.4 138 128.5 114 113.1 58.3 11.9 8.7 mm RST2 2.3 2 4 4.4 6.8 8.9 7.6 7 9.8 6.2 3.5 3.8 mm RST3 1.16 0.78 1.76 1.65 2.41 2.1 2.24 1.99 3.22 1.53 1.6 2.08 mm PRW1 0.045 0.078 0.103 0.196 0.304 0.279 0.478 0.489 0.403 0.213 0.103 0.046 PRW2 0.35 0.2 0.263 0.583 0.556 0.789 0.718 0.642 0.696 0.573 0.308 0.48 DAYP 2 2.5 3.8 9.6 12.6 17.1 19.5 17.9 17.1 10.3 3.9 2.5 d Sn. Antonio Calpulalpán, Tlaxcala (29019) OBMX 21.2 22.6 25.3 26.4 I 27.00 24.7 23.3 23.9 22.8 22.8 21.7 20.9 °c OBMN 2 2.7 4.8 6.9 8.7 9.5 8.6 8.3 8.5 6.3 4.1 2.9 °c SDTMX 3.5 3.3 4.7 4.6 4.7 4.3 3.5 3.8 3.8 3.9 4 2.9 °c SDTMN 2.1 2.2 2.4 1.9 1.9 2.2 1.9 1.8 2.5 2.6 2.2 2.4 °c SMY 8.1 8.4 15.7 39.1 75.1 135.6 131.5 123.2 79.8 48.1 11.4 6.8 mm RST2 4.5 2.7 6 5.2 7.6 9.8 8 7.9 7 7.2 5.3 4.2 mm RST3 1.43 1.2 2.18 1.76 2.67 2.24 2.42 1.74 2.11 1.87 2.4 0.89 mm PRW1 0.034 0.057 0.075 0.15 0.243 0.387 0.418 0.39 0.287 0.167 0.065 0.03 PRW2 0.367 0.404 0.286 0.552 0.589 0.642 0.679 0.629 0.57 0.429 0.292 0.32 DAYP 1.6 2.5 2.9 7.5 11.5 15.6 17.5 15.9 12 7 2.5 1.3 d Sombrerito, Calpulalpán, Tlaxcala (29028) OBMX 17.8 18.9 21.4 22 22.1 19.6 18.5 18.6 18.6 18 18 17.4 °c OBMN 2.2 2.5 4.9 6.2 7.2 7.3 6.5 6.5 6.6 5.4 3.8 2.7 °c SDTMX 2.8 2.7 2.7 2.8 2.6 3.1 2.3 2.2 2.1 2.6 2.9 2.5 °c SDTMN 1.6 2 1.8 2.1 1.5 1.4 1.3 1.5 1.6 1.8 1.9 1.9 °c SMY 11 6.2 15.6 37.8 77.4 160.6 136.3 144.2 124.9 69.5 13.1 8.1 mm RST2 8.5 2.8 4.3 4.5 7.2 9.1 6.8 9.6 8.1 7.4 3 3.2 mm RST3 2.73 1.54 2.08 1.35 2.51 1.67 1.65 2.83 2.01 2.27 2.16 0.86 mm PRW1 0.038 0.049 0.096 0.147 0.254 0.354 0.496 0.475 0.372 0.209 0.091 0.045 PRW2 0.389 0.278 0.333 0.56 0.581 0.729 0.683 0.614 0.652 0.526 0.314 0.381 DAYP 1.8 1.8 3.9 7.5 11.7 17 18.9 17.1 15.5 9.5 3.5 2.1 d 179
B2. Average monthly wind velocity (WVL[1-12]) for the meteorological stations around the Texcoco District (m/s). Place Key Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Col. Agricola Oriental, D. F. 9009 0.76 0.88 0.59 0.68 0.79 0.74 0.72 0.84 0.70 0.70 0.65 0.71 Col. Del Valle (SMN), D. F. 9011 1.52 1.83 1.81 1.84 1.81 1.76 1.78 1.73 1.80 1.69 1.56 1.49 Col. Escandon, D. F. 9012 0.98 1.27 1.31 1.34 1.12 1.02 1.07 1.01 1.04 0.99 1.03 1.05 Col. Moctezuma (SMN), D. F. 9013 3.00 3.63 3.99 3.42 3.40 3.14 2.96 3.10 3.02 2.96 2.69 2.31 Col. Santa Ursula Coapa, Coyoacan, D. F. 9014 1.77 1.49 1.13 1.34 1.26 1.46 1.45 1.77 1.81 1.61 1.58 1.78 Cuautepec Barrio Bajo, D. F. 9017 3.55 2.84 3.69 3.15 3.07 2.89 2.60 2.60 2.40 2.45 2.50 2.64 Mor. 77, Sn. Pablo Barrio, Ixtap., D. F. 9026 1.19 1.12 1.10 1.18 1.30 1.23 1.19 1.26 1.33 1.20 1.17 1.10 Km. 6+250 Gran Canal, D. F. 9029 1.07 0.87 0.86 0.99 1.03 1.21 0.99 0.99 0.93 1.03 0.93 1.42 Milpa Alta, Milpa Alta, D. F. 9032 1.66 1.74 1.81 1.48 1.20 1.33 1.48 1.67 1.57 1.66 1.84 1.53 Moyoguarda, Xochimilco, D. F. 9034 1.18 1.80 1.47 1.45 1.04 1.00 1.03 1.29 1.29 1.14 1.03 1.08 Sn Gregorio Atlapulco, Xochimilco, D. F. 9042 2.20 2.44 2.57 2.29 2.43 2.44 2.42 2.39 2.23 2.23 2.14 2.16 Col. San Juan De Aragon, D. F. 9043 1.25 1.29 1.11 1.17 1.23 1.76 1.57 1.52 1.45 1.52 1.43 1.30 Santa Ana Tlacotenco, Milpa Alta, D. F. 9045 1.60 1.66 1.68 1.55 1.46 1.46 1.46 1.46 1.47 1.52 1.47 1.61 Tlahuac (Xochimilco), D. F. 9051 2.41 2.34 2.46 2.45 2.04 1.89 1.79 1.88 1.96 2.00 2.00 2.27 Vertedor, Milpa Alta, D. F. 9058 2.14 2.12 2.08 1.78 1.64 1.78 1.70 1.76 1.86 1.67 1.83 1.70 Puente de la Llave, Pantitlan, D. F. 9068 2.09 1.50 1.70 1.36 1.25 1.36 1.75 1.65 1.95 1.61 1.80 1.48 Amecameca de Juarez, Amecameca, Mex. 15007 2.34 2.12 2.22 2.31 2.05 1.97 2.07 2.04 2.05 2.14 2.14 2.19 Atenco (DGE), Atenco, Mex. 15008 1.39 1.74 1.62 1.43 1.39 1.78 1.62 1.58 1.44 1.62 1.46 1.61 Coatepec de los Olivos, Ixtapaluca, Mex. 15017 1.45 1.55 1.57 1.51 1.55 1.55 1.60 1.46 1.65 1.60 1.45 1.55 Col. Avila Camacho, Ixtapaluca, Mex. 15018 1.52 1.50 1.63 1.44 1.38 1.39 1.41 1.51 1.40 1.47 1.45 1.63 Chalco, Chalco, Mex. 15020 1.83 1.96 1.99 1.93 2.00 1.97 1.68 1.75 1.74 1.74 1.78 1.86 Chiconautla, Ecatepec, Mex. 15022 1.63 1.36 1.48 1.63 1.55 1.48 1.57 1.54 1.44 1.56 1.60 1.84 Juchitepec, Juchitepec, Mex. 15039 1.72 1.61 1.42 1.10 1.20 1.45 1.36 1.28 1.26 1.28 1.38 1.43 DKm. 2+120 (Bombas), Ecatepec, Mex. 15040 2.23 2.04 2.31 2.47 1.90 2.94 2.57 3.05 2.60 2.83 2.37 1.85 Km 27+250 Gran Canal, Ecatepec, Mex. 15041 1.70 2.03 1.70 1.83 1.70 1.76 1.81 2.05 1.87 1.76 1.88 1.76 La Grande, Atenco, Mex. 15044 2.40 2.24 1.72 1.53 1.68 2.12 2.21 1.89 2.23 2.20 2.08 1.99 Los Reyes, La Paz, Mex. 15050 1.73 1.52 1.81 1.64 1.64 1.64 1.85 1.69 1.75 1.81 1.70 1.93 Nepantla,Tepetixtla (SMN), Mex. 15060 2.12 2.14 2.16 2.12 2.30 2.10 2.02 2.23 2.03 1.78 2.06 2.06 Otumba, Otumba, Mex. 15065 1.21 1.01 1.20 1.22 1.22 1.38 1.54 1.37 1.08 1.33 1.18 1.19 Rio Frio, Ixtapaluca, Mex. 15082 2.53 2.65 2.32 2.29 2.28 2.14 2.34 2.51 2.28 2.20 2.04 1.99 San Andres Riva Palacio, Texcoco, Mex. 15083 1.66 1.86 1.76 1.66 1.76 2.11 1.81 1.72 1.82 1.72 1.88 1.77 Sn. Jeronimo Xonacahuacan, Tecamac, Mex. 15090 1.23 1.47 1.42 1.42 1.34 1.23 1.10 1.13 1.16 1.30 1.26 1.38 San Jose de las Presas, Otumba, Mex. 15091 1.66 2.17 2.17 2.46 2.38 2.82 1.78 2.38 2.66 3.01 1.90 1.92 San Juan Ixhuatepec, Tlalnepantla, Mex. 15092 1.54 1.54 1.46 1.40 1.57 1.53 1.53 1.55 1.55 1.49 1.43 1.50 San Luis Ameca, Tenango del Aire, Mex. 15094 2.61 3.54 3.43 3.14 2.55 3.11 2.97 2.51 2.53 2.70 2.64 3.00 San M. de las Piramides, S. M. Pira.,Mex. 15097 2.22 2.42 2.46 2.74 2.69 2.38 2.46 2.48 2.30 2.19 2.46 2.41 San Rafael, Tlalmanalco, Mex. 15106 0.98 1.09 0.94 0.88 0.77 0.82 0.94 0.92 1.01 1.06 0.90 1.09 Tepexpan, Acolman, Mex. 15124 1.40 1.55 1.33 1.28 1.20 1.14 1.14 1.08 1.22 1.16 1.19 1.43 Texcoco, Texcoco (DGE), Mex. 15125 2.66 2.66 2.66 3.05 2.78 2.10 1.97 2.02 2.03 1.86 2.98 2.48 Tultepec, Tultepec, Mex. 15129 1.70 1.64 1.64 1.70 1.72 1.65 1.64 1.65 1.70 1.59 1.70 1.70 Xochihuacan, Otumba, Mex. 15135 2.46 2.41 2.56 2.71 2.44 2.15 1.90 1.81 2.20 2.68 2.41 2.17 Atenco (CFE), Atenco, Mex. 15138 3.53 3.46 3.11 3.36 3.37 3.20 3.08 3.25 2.79 2.85 3.06 3.97 E.T.A. 32, Tlalpitzahuac, Ixtapaluca, Mex. 15141 4.82 4.44 4.82 4.82 3.68 3.30 3.30 3.30 3.30 4.44 3.68 4.06 Plan Lago de Tex. (Campamento), Mex. 15145 1.82 1.95 2.09 1.82 1.95 2.19 2.32 2.18 3.10 2.16 1.70 1.81 El Tejocote, Texcoco, Mex. 15167 1.85 2.24 2.27 2.11 2.28 2.19 2.18 1.91 2.58 2.27 1.78 2.34 Chapingo, Texcoco, Mex. 15170 2.04 1.96 1.96 2.08 2.00 2.26 1.72 2.16 1.85 1.77 1.80 1.87 San Juan Totolapan, Tepetlaoxtoc, Mex. 15210 2.24 1.85 1.89 1.42 1.18 1.52 1.82 1.26 1.10 1.60 1.56 2.04 Atlautla E-9, Atlautla, Mex. 15252 2.18 3.68 2.18 3.50 1.86 2.18 3.30 3.68 3.30 3.30 3.30 3.30 Tlalmananco, Tlalmananco, Mex. 15280 3.78 3.30 3.30 3.30 3.30 2.37 3.30 2.37 2.60 3.30 3.30 3.30 Ecatzingo E-8, Ecatzingo, Mex. 15288 2.67 2.57 2.46 2.67 2.43 2.01 2.62 1.77 1.95 3.18 2.66 2.95 Tlacualera, Tlacualera, Mor. 17021 2.03 2.40 2.80 2.42 1.86 1.71 2.19 2.32 2.21 2.03 1.95 2.07 E.T.A. 118, Yecapixtla, Mor. 17043 2.22 2.10 1.91 2.12 2.00 2.32 2.14 2.08 2.47 2.10 2.08 2.08 Huecauaxco E-7, Ocuituco, Mor. 17045 2.10 2.11 2.42 2.34 2.17 2.19 2.30 2.38 2.61 2.74 2.67 2.32 Totolapan E-10,Totolapan, Mor. 17051 0.99 1.00 0.77 1.12 0.84 0.56 0.88 0.90 0.92 0.81 0.81 0.63 Santa Rita Tlahuapan, Pue. 21096 2.86 3.66 3.50 3.34 2.94 2.97 3.06 2.88 2.95 2.83 3.02 2.76 Cuaula, Calpulalpan, Tlax. 29006 1.56 1.57 1.72 1.70 1.63 1.70 1.63 1.63 1.81 1.82 1.64 1.50 La Venta, Calpulalpan, Tlax. 29013 1.65 1.86 1.99 2.07 1.89 1.58 1.65 1.49 1.59 1.68 1.78 1.57 Limites, Calpulalpan, Tlax. 29014 1.58 1.86 2.02 2.06 1.70 1.88 1.86 2.06 2.02 2.02 1.70 1.74 San Antonio Calpulalpan, Tlax. 29019 1.55 1.62 1.50 1.97 1.58 1.48 1.76 1.56 1.42 1.55 1.56 2.42 Esc. Agrop. Nanacamilpa, Tlax. 29039 1.58 1.46 1.58 1.70 1.58 1.99 1.70 1.70 1.48 1.70 1.58 1.85 B3. Profile of Physical and Chemical Parameters by Soil Type.
SALB = Soil albedo PH = Soil pH K = Water erosion soil erodibility factor SMB = Sum of bases (cmol/kg) FI = Wind erosion soil erodibility factor CBN = Organic carbon (%) Z = Depth from the surface to the bottom of the soil layer CAC = Calcium carbonate (%) (m) CEC = Cation exchange capacity (cmol/kg) BD = Bulk density of the soil layer (t/m3) ROK = Coarse fragment content (%) U = Wilting point (1500 kPa) (m/m) WNO3= Initial Nitrate concentration (g/t) FC = Field capacity1 (33kPa) (m/m) AP = Labile P concentration (g/t) SAN = Sand content (%) RSD = Crop residue1 (t/ha) SIL = Silt content (%) BDD = Bulk density (oven dry) (t/m3). WN= Organic N concentration (g/t)
Soil Layer Z BD U FC SAN SIL WN pH SMB CBN CAC CEC ROK WNO3 AP RSD BDD SC WP SALB Type m t/m3 m/m m/m % % g/t cmol/kg % % cmol/kg % g/m3 g/t t/ha t/m3 mm/h g/m3 BE-D 1 0.30 1.37 0.160 0.280 41.9 31.1 207 7.0 8.0 0.96 2.0 16.7 2.9 6.4 18.7 0.34 1.46 4.30 12 0.13 2 0.35 1.36 0.164 0.291 40.5 30.6 135 7.2 11.0 0.21 2.1 14.0 4.0 6.2 11.2 0.31 1.52 3.72 10 BE-L 1 0.30 1.38 0.150 0.270 46.4 27.7 228 6.9 6.0 1.29 2.5 15.0 30.0 6.7 8.0 0.42 1.47 4.40 10 0.13 2 0.35 1.39 0.158 0.277 45.9 26.8 128 6.6 16.2 0.23 2.6 18.4 30.0 6.5 0.0 0.38 1.49 3.92 10 BE-P 1 0.30 1.44 0.120 0.240 52.5 29.2 216 5.6 3.8 0.87 0.0 13.2 6.0 5.0 42.2 0.34 1.54 9.30 10 0.13 2 0.35 1.43 0.129 0.244 51.5 28.3 169 6.4 15.8 0.62 0.0 13.3 0.0 5.0 8.9 0.25 1.60 7.54 10 BE-SF 1 0.30 1.31 0.200 0.330 34.3 29.1 210 8.6 29.9 1.07 0.0 34.8 10.0 5.0 9.8 0.64 1.40 2.40 10 0.13 2 0.65 1.30 0.220 0.352 33.0 27.3 130 7.9 9.0 0.25 0.0 12.3 10.0 5.0 0.6 0.38 1.57 2.06 10 3 0.85 1.28 0.246 0.377 30.7 25.1 118 8.0 9.5 0.11 0.0 12.8 13.0 5.0 0.0 0.10 1.56 1.79 10 4 1.00 1.27 0.255 0.387 29.5 24.9 108 8.1 10.0 0.00 0.0 13.3 13.0 5.0 0.0 0.01 1.56 1.75 10 BH-G 1 0.30 1.48 0.110 0.220 55.0 30.7 178 6.1 3.4 0.67 0.0 12.8 3.7 5.0 14.3 0.33 1.58 14.90 10 0.13 2 0.65 1.45 0.124 0.238 53.5 27.4 157 6.0 6.0 0.51 0.0 12.3 1.9 5.0 5.6 0.22 1.56 8.41 10 BH-L 1 0.30 1.38 0.150 0.270 44.2 30.7 253 6.9 5.8 1.46 0.0 14.8 8.2 6.5 22.5 0.39 1.47 4.90 15 0.13 2 0.35 1.38 0.155 0.279 43.3 29.9 288 7.0 7.5 1.78 0.0 16.2 12.5 6.2 30.2 0.34 1.48 4.26 15 BK-D 1 0.30 1.47 0.120 0.220 64.1 18.2 176 7.5 7.1 0.62 0.2 15.9 2.7 6.3 17.2 0.36 1.57 9.30 10 0.13 2 0.65 1.50 0.123 0.221 63.7 18.0 314 6.6 4.1 2.32 0.0 13.4 4.2 10.0 2.3 0.43 1.50 8.70 10 GM-SF 1 0.30 1.40 0.130 0.260 41.7 36.5 482 8.1 24.5 4.07 0.2 30.3 0.4 5.0 20.0 0.35 1.49 7.10 10 0.12 2 0.65 1.36 0.145 0.278 39.5 35.6 242 7.4 33.4 1.46 0.0 42.1 0.5 5.0 7.7 0.31 1.51 5.37 10 3 1.00 1.35 0.163 0.295 38.1 33.3 149 7.2 36.7 0.47 0.0 44.8 0.7 5.0 0.0 0.07 1.53 3.93 10 HC-DP 1 0.30 1.41 0.140 0.250 50.6 26.4 206 7.6 13.6 0.93 0.2 21.3 2.7 6.3 21.3 0.36 1.50 5.60 10 0.13 2 0.65 1.41 0.152 0.267 48.8 25.3 220 8.7 45.6 1.20 0.4 47.8 2.5 6.1 7.7 0.32 1.42 4.31 10 HC-L 1 0.30 1.55 0.090 0.190 69.1 19.5 213 8.4 47.9 0.89 0.5 49.7 0.0 6.6 35.1 0.36 1.65 22.06 10 0.13 2 0.35 1.53 0.108 0.206 66.7 18.7 149 7.2 36.7 0.47 0.0 44.8 0.7 5.0 0.0 0.07 1.53 13.89 10 180 B3. Profile of Physical and Chemical Parameters by Soil Type — Continued. Soil Layer Z BD U FC SAN SIL WN pH SMB CBN CAC CEC ROK WNO3 AP RSD BDD SC WP SALB Type m t/m3 m/m m/m % % g/t cmol/kg % % cmol/kg % g/m3 g/t t/ha t/m3 mm/h g/m3 HC-SF 1 0.30 1.30 0.210 0.340 35.1 27.2 253 8.3 28.1 1.39 0.0 33.3 0.0 5.0 29.9 0.52 1.39 2.20 10 0.13 2 0.65 1.30 0.228 0.356 33.9 24.9 182 8.4 36.8 0.80 0.0 27.3 0.0 5.0 5.0 0.40 1.38 1.89 10 3 0.85 1.28 0.251 0.381 31.1 23.9 155 8.5 39.0 0.52 0.0 29.2 0.0 5.0 2.2 0.17 1.39 1.72 10 4 1.00 1.28 0.266 0.393 30.8 21.6 138 8.5 40.4 0.34 6.0 30.4 0.0 5.0 0.5 0.04 1.39 1.61 10 HH-D 1 0.30 1.42 0.130 0.250 49.5 29.8 204 7.9 18.4 0.97 0.0 25.3 0.0 6.5 14.0 0.34 1.52 7.30 10 0.13 2 0.35 1.40 0.155 0.271 47.6 25.8 144 7.7 21.5 0.39 1.3 18.7 3.6 5.8 1.9 0.23 1.51 4.09 10 HH-DP 1 0.30 1.38 0.150 0.270 46.9 27.3 197 7.3 9.5 0.88 0.0 17.8 3.3 6.5 14.9 0.38 1.47 4.40 10 0.13 2 0.48 1.39 0.163 0.280 46.3 25.4 156 7.5 18.6 0.52 0.5 17.9 3.5 6.1 2.8 0.29 1.50 3.57 10 3 0.65 1.39 0.171 0.288 45.5 24.3 134 7.9 23.8 0.29 2.0 19.4 3.7 5.6 1.1 0.18 1.51 3.09 10 HH-G 1 0.30 1.47 0.110 0.230 54.7 30.0 194 6.2 2.7 0.83 0.0 12.3 0.0 5.0 16.4 0.52 1.57 13.30 12 0.13 2 0.65 1.43 0.130 0.246 50.9 28.5 144 7.3 11.3 0.41 0.0 14.5 0.0 5.0 0.0 0.47 1.52 7.28 10 HH-L 1 0.30 1.37 0.160 0.280 45.7 26.4 210 7.4 11.6 1.01 0.0 19.7 3.3 6.5 17.0 0.38 1.46 3.70 10 0.13 2 0.35 1.36 0.194 0.312 42.2 23.0 240 6.7 23.1 1.44 0.0 27.0 14.0 5.9 6.1 0.25 1.43 2.32 10 HH-LP 1 0.30 1.43 0.130 0.240 53.1 26.2 213 6.9 7.1 1.04 0.0 15.9 8.6 6.5 18.4 0.39 1.53 7.00 10 0.13 2 0.35 1.43 0.145 0.257 51.2 24.6 278 6.6 21.5 1.80 0.0 25.8 11.7 6.2 14.0 0.31 1.43 4.99 10 3 0.65 1.42 0.157 0.268 50.1 23.0 240 6.7 23.1 1.44 0.0 27.0 14.0 5.9 6.1 0.25 1.43 3.88 10 HH-SF 1 0.30 1.36 0.160 0.290 40.7 30.6 224 7.8 12.8 1.06 0.0 20.6 0.0 5.0 29.7 0.62 1.45 3.70 10 0.13 2 0.65 1.34 0.199 0.322 38.9 25.3 148 7.6 22.8 0.43 0.0 19.0 0.0 5.0 2.5 0.45 1.43 2.30 10 3 0.85 1.33 0.211 0.333 37.8 24.2 135 7.7 25.2 0.31 0.0 19.9 0.0 5.0 0.0 0.45 1.42 2.06 10 4 1.00 1.32 0.219 0.343 37.0 23.3 127 7.8 27.0 0.22 0.0 20.6 0.0 5.0 0.0 0.07 1.41 1.91 10 JD-G 1 0.30 1.52 0.100 0.210 61.3 26.2 203 6.0 2.1 0.72 0.0 11.7 0.0 10.0 42.0 0.43 1.62 18.90 10 0.13 2 0.65 1.49 0.104 0.213 60.6 25.7 163 7.9 14.8 0.41 0.0 14.8 0.0 5.0 25.0 0.25 1.61 15.90 13 JD-LP 1 0.30 1.52 0.100 0.210 61.7 26.3 195 6.6 2.5 0.71 0.0 12.1 0.0 5.0 32.5 0.35 1.62 20.30 19 0.13 2 0.45 1.48 0.121 0.227 59.1 22.8 173 8.0 14.8 0.52 1.0 14.8 0.0 5.0 25.0 0.11 1.60 9.14 13 3 0.65 1.47 0.134 0.238 58.4 20.3 154 8.4 8.7 0.31 19.0 8.7 0.0 5.0 25.0 0.01 1.57 6.30 13 JE-LP 1 0.30 1.52 0.100 0.210 60.3 21.3 195 6.6 2.5 0.71 0.0 12.1 0.0 5.0 32.5 0.52 1.62 20.30 10 0.13 2 0.65 1.47 0.133 0.236 58.9 20.2 253 6.1 4.2 1.56 0.0 13.5 9.0 5.0 9.9 0.37 1.52 6.55 10 JE-P 1 0.30 1.45 0.120 0.240 48.8 34.1 261 6.1 4.2 1.56 0.0 13.5 10.0 5.0 19.8 0.35 1.55 11.00 10 0.13 2 0.35 1.40 0.135 0.259 46.0 32.0 140 7.4 7.1 0.30 19.0 11.3 10.0 5.0 6.8 0.25 1.62 6.54 10 JE-SF 1 0.30 1.42 0.130 0.250 48.2 31.8 219 7.0 10.5 1.05 0.0 18.7 0.0 5.0 25.0 0.52 1.52 7.90 10 0.13 2 0.65 1.41 0.136 0.256 47.8 30.1 253 6.1 4.2 1.56 0.0 13.5 9.0 5.0 9.9 0.37 1.52 6.34 10 3 1.00 1.39 0.154 0.274 45.3 28.3 117 7.7 8.5 0.10 2.0 13.4 27.5 5.0 0.0 0.03 1.62 4.31 10 RC-L 1 0.30 1.36 0.170 0.280 46.3 24.7 205 7.6 11.3 0.92 0.2 19.4 2.7 6.3 21.3 0.36 1.45 3.40 10 0.13 2 0.35 1.37 0.195 0.311 43.3 21.7 189 6.5 4.3 0.83 0.0 13.6 2.9 6.2 10.6 0.30 1.55 2.24 10 RD-G 1 0.30 1.49 0.110 0.220 57.1 28.4 192 6.1 2.8 0.73 0.0 12.4 0.1 10.0 27.1 0.43 1.59 14.70 10 0.13 2 0.65 1.46 0.121 0.232 55.3 26.6 139 5.5 8.5 0.26 0.0 10.1 0.0 5.0 10.5 0.23 1.58 9.38 10 RD-LP 1 0.30 1.53 0.090 0.200 63.9 24.7 218 5.8 1.8 0.72 0.0 11.5 0.0 5.0 62.2 0.38 1.63 21.70 16 0.13 2 0.35 1.50 0.112 0.215 62.6 21.6 189 5.8 9.0 0.74 0.0 18.2 0.0 5.0 20.9 0.37 1.60 11.95 10 3 0.65 1.49 0.120 0.222 61.6 20.7 156 5.9 11.8 0.48 0.0 20.8 0.0 5.0 7.6 0.33 1.59 9.49 10 RD-P 1 0.30 1.49 0.110 0.220 57.7 27.9 187 6.1 3.3 0.72 0.0 12.8 0.0 5.0 20.7 0.34 1.59 14.70 10 0.13 2 0.65 1.46 0.125 0.234 56.3 24.6 139 5.5 8.5 0.26 0.0 10.1 0.0 5.0 10.5 0.23 1.58 8.27 10 RE-G 1 0.30 1.46 0.110 0.230 51.5 32.4 250 6.2 3.6 1.45 0.0 13.0 0.0 5.0 19.3 0.37 1.56 12.20 10 0.13 2 0.35 1.43 0.128 0.246 50.3 29.5 244 6.2 3.5 1.46 0.0 12.9 0.0 5.0 9.5 0.40 1.53 7.65 10 RE-L 1 0.30 1.43 0.120 0.240 51.3 29.4 233 7.9 18.5 1.24 0.2 25.4 2.7 6.6 21.8 0.36 1.53 8.40 10 0.13 2 0.65 1.41 0.155 0.270 48.4 25.0 134 8.1 18.8 0.28 0.0 13.2 3.0 5.0 2.2 0.07 1.58 4.06 10 181 B3. Profile of Physical and Chemical Parameters by Soil Type — Continued. Soil Layer Z BD U FC SAN SIL WN pH SMB CBN CAC CEC ROK WNO3 AP RSD BDD SC WP SALB Type m t/m3 m/m m/m % % g/t cmol/kg % % cmol/kg % g/m3 g/t t/ha t/m3 mm/h g/m3 RE-LP 1 0.30 1.42 0.130 0.250 47.8 31.4 194 6.8 6.2 0.88 0.0 15.1 0.0 5.0 11.8 0.40 1.52 7.30 10 0.13 2 0.51 1.39 0.143 0.265 45.5 30.6 147 8.1 17.7 0.40 0.0 12.5 2.0 5.0 4.4 0.19 1.58 5.44 10 3 0.65 1.38 0.164 0.284 44.4 27.0 134 8.1 18.8 0.28 0.0 13.2 3.0 5.0 2.2 0.07 1.58 3.58 10 RE-SF 1 0.30 1.47 0.110 0.230 55.3 28.3 191 7.9 13.2 0.85 0.0 21.0 4.0 10.0 10.8 0.03 1.57 11.60 10 0.13 2 0.65 1.43 0.143 0.254 52.2 24.2 199 7.1 7.7 1.02 0.0 16.4 3.3 8.1 1.5 0.56 1.50 5.17 10 3 0.85 1.42 0.152 0.262 51.4 23.0 134 8.1 18.8 0.28 0.0 13.2 3.0 5.0 2.2 0.07 1.58 4.30 10 4 1.00 1.42 0.159 0.269 50.3 22.4 123 8.2 19.8 0.17 13.0 13.9 5.0 5.0 0.1 0.02 1.57 0.37 10 TH-L 1 0.30 1.37 0.150 0.280 42.7 30.6 212 6.2 4.8 0.97 0.2 14.0 2.7 6.3 25.0 0.36 1.46 4.30 10 0.13 2 0.35 1.36 0.169 0.295 40.4 29.7 160 5.7 23.6 0.55 0.0 40.4 0.0 5.0 5.0 0.80 1.38 3.41 10 TH-LP 1 0.30 1.48 0.110 0.220 55.7 29.6 232 5.8 2.8 1.08 0.0 12.3 10.0 10.0 39.0 0.43 1.58 14.20 10 0.13 2 0.65 1.45 0.122 0.236 53.8 27.6 153 6.0 7.5 0.45 0.0 22.4 10.0 5.0 6.4 0.64 1.59 8.97 10 TH-P 1 0.30 1.48 0.110 0.220 55.7 29.1 228 6.0 3.0 1.13 0.0 12.5 10.0 10.0 26.9 0.43 1.58 13.40 10 0.13 2 0.35 1.44 0.130 0.243 53.0 26.4 230 6.0 4.0 1.29 0.0 13.3 1.9 8.0 11.2 0.60 1.53 7.19 10 TO-L 1 0.30 1.41 0.130 0.250 47.7 31.0 213 6.0 3.7 0.97 0.0 13.1 3.3 6.5 26.2 0.38 1.50 7.00 10 0.13 2 0.35 1.40 0.143 0.264 46.6 29.4 181 5.9 2.9 0.76 0.0 12.4 0.0 5.0 7.9 0.34 1.54 5.32 10 TO-LP 1 0.30 1.37 0.150 0.280 40.0 34.0 201 6.4 4.5 0.99 0.0 13.7 2.0 5.0 8.0 0.32 1.46 4.80 14 0.13 2 0.65 1.35 0.163 0.293 38.8 32.6 153 6.6 10.0 0.46 1.0 33.3 4.0 5.0 5.9 0.09 1.57 3.90 10 VC-SF 1 0.30 1.30 0.210 0.350 33.2 28.2 221 7.9 17.1 1.13 0.0 24.2 0.0 5.0 17.7 0.35 1.39 2.20 10 0.13 2 0.45 1.29 0.227 0.360 31.4 27.8 176 7.6 23.1 0.70 0.0 31.6 0.0 5.0 8.2 0.26 1.37 2.02 10 3 0.70 1.29 0.240 0.371 30.9 26.1 149 7.9 25.3 0.45 0.0 34.2 0.0 5.0 2.6 0.13 1.39 1.86 10 4 1.00 1.28 0.247 0.380 29.9 25.9 134 8.1 26.6 0.29 1.0 35.8 0.0 5.0 0.0 0.03 1.40 1.81 10 VP-D 1 0.30 1.38 0.160 0.270 50.5 22.1 207 7.6 16.4 1.00 0.2 23.6 2.7 6.3 15.0 0.36 1.47 3.70 10 0.13 2 0.35 1.41 0.167 0.278 49.0 21.9 243 7.6 11.5 1.39 1.1 19.6 24.0 6.5 17.1 0.36 1.51 3.22 10 VP-L 1 0.30 1.35 0.170 0.290 43.0 26.8 198 7.3 10.3 0.90 0.0 18.6 3.4 6.3 15.1 0.33 1.44 3.20 10 0.13 2 0.35 1.36 0.186 0.305 42.2 24.5 179 6.8 5.9 0.77 0.0 14.9 20.8 7.7 4.9 0.59 1.40 2.58 10 VP-LP 1 0.30 1.34 0.190 0.300 46.6 19.6 212 7.1 7.8 1.15 1.1 16.5 24.0 6.5 3.6 0.36 1.43 2.30 10 0.13 2 0.65 1.38 0.197 0.308 45.9 18.6 169 7.3 26.3 0.69 4.1 31.6 25.9 5.7 0.0 0.22 1.48 2.08 10 VP-SF 1 0.30 1.35 0.170 0.290 43.3 25.9 215 7.5 13.6 1.02 0.0 21.3 22.0 5.0 21.9 0.48 1.44 3.10 10 0.13 2 0.65 1.35 0.192 0.312 41.1 24.4 168 7.5 31.0 0.67 0.0 30.8 28.5 5.0 1.4 0.20 1.49 2.41 10 3 1.00 1.34 0.207 0.328 39.5 23.1 155 7.7 31.9 0.53 5.0 32.5 30.0 5.0 0.1 0.06 1.50 2.08 10 ZG-SF 1 0.30 1.30 0.210 0.340 33.1 28.6 225 8.7 31.7 1.14 23.0 36.3 9.0 5.0 22.3 0.37 1.39 2.20 10 0.13 2 0.65 1.29 0.234 0.365 31.6 26.3 203 9.0 40.6 1.07 15.0 34.2 10.0 5.0 0.8 0.25 1.44 1.91 10 3 0.85 1.28 0.241 0.373 30.9 25.7 160 9.0 42.6 0.59 12.0 37.1 8.0 5.0 0.0 0.09 1.45 1.83 10 4 1.00 1.27 0.262 0.393 29.1 24.2 123 9.0 44.5 0.17 12.0 39.6 5.5 5.0 0.0 0.01 1.46 0.17 10 ZM-SF 1 0.30 1.35 0.170 0.300 40.5 29.1 309 8.8 38.9 1.88 0.0 42.3 0.0 5.0 47.4 0.62 1.44 3.30 10 0.13 2 0.65 1.34 0.186 0.313 38.4 28.3 224 9.0 42.7 1.29 0.0 32.6 0.0 5.0 3.8 0.45 1.40 2.74 10 3 1.00 1.33 0.195 0.323 37.0 27.8 212 9.0 44.7 1.17 0.0 34.6 0.0 5.0 0.8 0.45 1.39 2.48 10 ZO-SF 1 0.30 1.34 0.180 0.300 41.5 27.3 236 8.8 45.8 1.29 0.0 48.0 0.0 5.0 18.7 0.40 1.43 3.00 10 0.13 2 0.65 1.35 0.183 0.306 40.6 26.7 155 9.0 46.6 0.49 0.0 33.7 0.0 5.0 5.5 0.19 1.48 2.74 10 3 0.75 1.34 0.198 0.322 39.1 25.3 143 9.0 48.7 0.38 0.0 35.0 0.0 5.0 2.4 0.06 1.50 0.23 10 4 1.00 1.34 0.206 0.329 38.6 24.3 120 9.0 53.1 0.13 10.0 37.8 0.0 5.0 0.0 0.00 1.53 2.13 10 182 B4. Hydrological Response Units (HRU) used for Model Validation. HRU Monicipio Soil Water S P CN CN's Hydro. Furrow CN Weather Yield Grain Yield Grain Area Type Suply % USLE Group Condition Station Trad. 2002 Trad. 2003 ha 1 Texcoco VP-SF Irrigation 2.7 1.0 C Good straight 82 15170 3.83 4.32 751.5 2 Atenco ZG-SF Irrigation 0.4 1.0 D Good straight 83 15008 1.71 1.76 187.5 3 Chiautla VP-L Irrigation 10.6 0.6 D Poor contour 85 15008 1.35 1.31 14.4 4 Atenco VC-SF Irrigation 0.6 1.0 D Good straight 83 15008 1.71 1.74 1962.8 5 Atenco-Chicon. BE-SF Irrigation 0.5 1.0 D Good straight 83 15124 2.12 2.26 374.9 6 Texcoco HH-DP Irrigation 4.4 0.5 B Poor contour 83 15170 3.50 4.05 108.2 7 Texcoco HC-SF Irrigation 0.6 1.0 D Good straight 83 15170 4.51 4.88 571.4 8 Texcoco HH-SF Irrigation 1.3 1.0 C Good straight 82 15170 4.49 4.89 585.9 9 Chicoloapán RE-SF Irrigation 2.1 1.0 B Good straight 81 15017 1.74 2.00 477.8 10 Chicoloapán HH-D Irrigation 9.6 0.6 A Poor contour 84 15050 1.58 1.93 112.6 11 Ixtapaluca HH-SF Irrigation 1.5 1.0 C Good straight 82 15020 3.18 3.46 918.6 12 Chalco HH-DP Irrigation 3.4 0.5 C Poor contour 85 15020 2.42 2.78 15.9 13 Chalco JE-SF Irrigation 0.5 1.0 C Good straight 82 15020 2.81 3.06 245.6 14 Coacalco HC-SF Rain fed 0.2 1.0 D Good straight 83 15041 3.58 3.45 64.9 15 Ecatepec ZO-SF Rain fed 1.1 1.0 D Good straight 83 15041 2.54 2.43 335.8 16 Ecatepec VP-SF Rain fed 0.3 1.0 D Good straight 83 15041 2.91 2.70 20.2 17 Coacalco VP-SF Rain fed 0.6 1.0 D Good straight 83 15041 3.59 3.33 102.3 18 Ecatepec ZM-SF Rain fed 0.8 1.0 D Good straight 83 15041 2.64 2.51 50.5 19 Ecatepec HC-SF Rain fed 1.7 1.0 D Good straight 83 15022 3.11 2.99 64.9 20 Ecatepec HC-SF Rain fed 1.6 1.0 D Good straight 83 15041 2.85 2.73 75.0 21 Ecatepec HC-DP Rain fed 6.3 0.6 D Fair contour 84 15022 2.40 2.27 36.0 22 Ecatepec ZM-SF Rain fed 1.2 1.0 D Good straight 83 15022 2.96 2.84 38.9 23 Ecatepec HC-SF Rain fed 0.4 1.0 D Good straight 83 15041 2.85 2.74 109.6 24 Ecatepec ZO-SF Rain fed 0.8 1.0 C Good straight 82 15022 2.55 2.42 581.0 25 Coacalco HH-D Rain fed 13.8 0.6 D Poor contour 85 15041 2.09 1.97 263.8 26 Tepetlaoxtoc HH-L Rain fed 12.5 0.5 C Fair contour 84 15124 0.95 0.98 41.8 27 Tepetlaoxtoc VP-L Rain fed 9.7 0.6 C Fair contour 84 15124 1.11 1.14 14.4 28 Tepetlaoxtoc HH-L Rain fed 9.0 0.6 C Fair contour 84 15124 1.05 1.06 18.7 29 Tepetlaoxtoc HH-L Rain fed 10.8 0.6 C Fair contour 84 15124 0.95 0.97 15.9 30 Coacalco HH-L Rain fed 22.9 0.6 D Poor contour 85 15041 1.76 1.76 89.4 31 Tepetlaoxtoc VP-D Rain fed 8.6 0.6 C Fair contour 84 15135 1.17 1.14 400.8 32 Ecatepec HH-L Rain fed 24.9 0.6 D Poor contour 85 15041 1.45 1.45 109.6 33 Tepetlaoxtoc HH-LP Rain fed 8.7 0.5 C Fair contour 84 15101 1.43 1.41 161.5 34 Coacalco BE-D Rain fed 15.0 0.6 D Fair contour 84 15041 1.76 1.76 23.1 35 Texcoco VP-SF Rain fed 3.3 0.6 C Good contour 82 15101 3.46 3.47 1724.9 36 Tepetlaoxtoc RE-L Rain fed 9.6 0.6 C Poor contour 85 15135 1.39 1.46 31.7 37 Ecatepec HC-SF Rain fed 0.5 1.0 D Good straight 83 15022 3.11 3.01 34.6 38 Tepetlaoxtoc HH-L Rain fed 14.7 0.6 C Fair contour 84 15210 1.01 1.02 559.5 39 Chiautla BK-D Rain fed 3.5 0.6 B Fair contour 82 15124 1.84 1.40 46.1
40 Tepetlaoxtoc HH-LP Rain fed 7.8 0.5 C Fair contour 84 15124 1.52 1.22 30.3 183 B4. Hydrological Response Units (HRU) used for Model Validation — Continued. HRU Monicipio Soil Water S P CN CN's Hydro. Furrow CN Weather Yield Grain Yield Grain Area Type Suply % USLE Group Condition Station Trad. 2002 Trad. 2003 ha 41 Tepetlaoxtoc RE-L Rain fed 9.2 0.6 C Poor contour 85 15135 1.49 1.46 37.5 42 Ecatepec ZG-SF Rain fed 0.6 1.0 D Good straight 83 15008 2.58 2.54 2587.8 43 Tepetlaoxtoc VP-L Rain fed 15.4 0.6 C Fair contour 84 15008 0.99 0.99 115.3 44 Tepetlaoxtoc HH-L Rain fed 13.6 0.5 B Fair contour 82 15008 0.90 0.90 113.9 45 Tepetlaoxtoc BE-D Rain fed 9.3 0.6 C Good contour 82 15210 1.12 1.15 11.5 46 Atenco ZO-SF Rain fed 0.5 1.0 D Good straight 83 15124 1.70 1.48 75.0 47 Tepetlaoxtoc HH-LP Rain fed 8.4 0.6 B Fair contour 82 15101 1.40 1.38 477.3 48 Tepetlaoxtoc VP-LP Rain fed 7.5 0.6 C Fair contour 84 15135 1.43 1.41 51.9 49 Atenco VC-SF Rain fed 1.2 1.0 D Good straight 83 15008 1.71 1.68 2185.0 50 Tepetlaoxtoc BE-L Rain fed 10.2 0.6 C Fair contour 84 15135 0.99 0.97 11.5 51 Atenco BE-SF Rain fed 0.5 1.0 D Good straight 83 15124 1.99 1.54 560.9 52 Ecatepec HC-L Rain fed 28.7 0.6 C Poor contour 85 15041 1.43 1.43 41.8 53 Chiautla RE-LP Rain fed 5.8 0.6 B Good contour 81 15124 1.56 1.29 27.4 54 Chiautla VP-L Rain fed 20.8 0.6 B Fair contour 82 15008 1.02 1.05 51.9 55 Chiautla-Chicon HH-L Rain fed 32.5 0.6 C Fair contour 84 15008 1.32 1.34 207.6 56 Tepetlaoxtoc RE-L Rain fed 13.8 0.6 C Fair contour 84 15210 1.39 1.37 11.5 57 Tepetlaoxtoc HH-DP Rain fed 7.7 0.6 C Fair contour 84 15101 1.48 1.45 644.6 58 Tepetlaoxtoc VP-D Rain fed 8.0 0.6 C Fair contour 84 15210 1.18 1.20 148.5 59 Ecatepec ZM-SF Rain fed 1.1 1.0 A Good straight 80 15022 2.48 2.35 108.2 60 Tepetlaoxtoc VP-LP Rain fed 4.0 0.6 C Fair contour 84 15210 1.55 1.58 27.4 61 Tepetlaoxtoc VP-D Rain fed 8.3 0.6 C Fair contour 84 15210 1.18 1.21 196.1 62 Tepetlaoxtoc RE-LP Rain fed 6.9 0.5 B Fair contour 82 15101 1.38 1.35 13.0 63 Tepetlaoxtoc VP-D Rain fed 8.5 0.6 C Fair contour 84 15210 1.20 1.22 186.0 64 Tepetlaoxtoc RE-SF Rain fed 3.2 0.6 B Fair contour 82 15101 1.85 1.81 95.2 65 Tepetlaoxtoc RE-LP Rain fed 7.0 0.5 B Fair contour 82 15101 1.39 1.36 11.5 66 Texcoco RE-LP Rain fed 7.4 0.6 C Fair contour 84 15101 2.96 2.90 445.7 67 Tepetlaoxtoc RE-LP Rain fed 8.1 0.6 C Fair contour 84 15210 1.47 1.51 49.0 68 Tepetlaoxtoc TH-P Rain fed 15.1 0.6 C Good contour 82 15210 1.18 1.20 28.8 69 Tepetlaoxtoc VP-D Rain fed 5.9 0.6 C Fair contour 84 15101 1.04 0.97 167.3 70 Tepetlaoxtoc HH-L Rain fed 14.3 0.6 C Fair contour 84 15210 1.03 1.05 20.2 71 Atenco HH-SF Rain fed 0.4 1.0 D Fair straight 84 15008 2.08 2.06 14.4 72 Tepetlaoxtoc HH-L Rain fed 10.3 0.6 C Fair contour 84 15210 1.03 1.05 328.9 73 Tepetlaoxtoc HH-L Rain fed 10.4 0.6 C Fair contour 84 15210 1.03 1.05 677.9 74 Tepetlaoxtoc VP-L Rain fed 12.7 0.6 C Good contour 82 15210 1.09 1.10 21.6 75 Atenco ZO-SF Rain fed 0.2 1.0 C Good straight 82 15008 1.62 1.60 23.1 76 Tepetlaoxtoc RC-L Rain fed 16.9 0.6 C Fair contour 84 15210 1.04 1.05 18.7 77 Texcoco HH-L Rain fed 16.4 0.5 C Fair contour 84 15101 2.02 1.89 513.5 78 Tepetlaoxtoc HH-L Rain fed 13.1 0.6 C Fair contour 84 15210 1.02 1.03 20.2 79 Tepetlaoxtoc VP-L Rain fed 10.5 0.6 C Good contour 82 15210 1.06 1.07 23.1 80 Atenco ZO-SF Rain fed 0.2 1.0 D Good straight 83 15008 1.74 1.72 24.5 184 B4. Hydrological Response Units (HRU) used for Model Validation — Continued. HRU Monicipio Soil Water S P CN CN's Hydro. Furrow CN Weather Yield Grain Yield Grain Area Type Suply % USLE Group Condition Station Trad. 2002 Trad. 2003 ha 81 Tepetlaoxtoc RE-L Rain fed 10.0 0.6 C Fair contour 84 15101 1.31 1.20 57.7 82 Tepetlaoxtoc RE-L Rain fed 16.9 0.6 C Good contour 82 15210 1.43 1.47 15.9 83 Atenco ZO-SF Rain fed 0.3 1.0 D Good straight 83 15008 1.76 1.74 24.5 84 Tepetlaoxtoc BH-L Rain fed 10.6 0.6 C Poor contour 85 15210 1.10 1.12 69.2 85 Texcoco HH-L Rain fed 14.0 0.5 C Fair contour 84 15101 2.05 1.99 132.7 86 Tepetlaoxtoc RE-L Rain fed 13.0 0.6 C Fair contour 84 15210 1.54 1.57 14.4 87 Texcoco VP-L Rain fed 8.0 0.6 C Fair contour 84 15210 2.32 2.37 63.5 88 Texcoco BH-L Rain fed 19.0 0.6 C Good contour 82 15210 2.01 2.04 242.4 89 Tepetlaoxtoc BH-L Rain fed 13.0 0.6 C Fair contour 84 15210 1.13 1.15 111.1 90 Texcoco HH-DP Rain fed 5.9 0.6 C Fair contour 84 15101 3.08 3.02 369.3 91 Texcoco TH-P Rain fed 13.0 0.6 C Good contour 82 15210 2.16 2.21 95.2 92 Tepetlaoxtoc TH-P Rain fed 7.6 0.6 C Good contour 82 15210 1.36 1.40 62.0 93 Texcoco BE-D Rain fed 9.7 0.5 C Fair contour 84 15101 2.32 2.17 473.2 94 Texcoco HH-DP Rain fed 5.5 0.6 B Fair contour 82 15170 2.79 2.96 1678.2 95 Texcoco VP-L Rain fed 9.8 0.6 D Fair contour 84 15101 2.38 2.23 39.0 96 Texcoco HC-SF Rain fed 1.1 1.0 D Good straight 83 15170 3.89 3.94 194.8 97 Texcoco HH-DP Rain fed 8.9 0.6 D Fair contour 84 15101 3.05 2.83 70.7 98 Texcoco HH-SF Rain fed 1.7 1.0 C Good straight 82 15170 4.02 4.06 1145.8 99 Texcoco HH-DP Rain fed 4.6 0.6 C Fair contour 84 15101 3.08 3.02 278.5 100 Texcoco BE-L Rain fed 22.8 0.6 C Poor contour 85 15018 2.01 2.09 33.2 101 Texcoco BE-D Rain fed 11.3 0.6 C Good contour 82 15101 2.02 1.91 90.9 102 Texcoco HH-D Rain fed 9.1 0.5 C Fair contour 84 15101 2.70 2.44 36.1 103 Texcoco HH-LP Rain fed 7.1 0.6 C Fair contour 84 15101 3.17 3.10 14.4 104 Texcoco HH-DP Rain fed 6.5 0.5 C Fair contour 84 15101 3.08 3.02 73.6 105 Texcoco HH-DP Rain fed 8.8 0.5 C Fair contour 84 15101 3.04 2.80 219.3 106 Texcoco HH-L Rain fed 9.3 0.5 C Fair contour 84 15101 2.33 2.19 11.5 107 Texcoco BE-D Rain fed 9.9 0.5 C Fair contour 84 15101 2.33 2.19 11.5 108 Texcoco HH-DP Rain fed 6.8 0.5 C Fair contour 84 15101 3.08 3.01 20.2 109 Texcoco BE-D Rain fed 9.8 0.6 C Good contour 82 15101 2.33 2.19 14.4 110 Texcoco HH-L Rain fed 10.3 0.5 C Fair contour 84 15017 1.91 2.04 33.2 111 Chicoloapán HC-DP Rain fed 7.3 0.6 C Fair contour 84 15017 2.27 2.28 646.7 112 Texcoco HH-L Rain fed 14.3 0.6 C Fair contour 84 15017 1.91 2.03 151.6 113 Texcoco HC-DP Rain fed 7.1 0.6 B Fair contour 82 15050 2.96 3.00 239.6 114 Texcoco HH-D Rain fed 13.2 0.6 C Good contour 82 15017 2.25 2.39 70.7 115 Chimalhuacán HC-L Rain fed 12.8 0.6 A Fair contour 81 15050 1.34 1.31 174.7 116 Texcoco HH-D Rain fed 9.5 0.5 C Fair contour 84 15017 2.11 2.55 17.3 117 Ixtapaluca HH-L Rain fed 12.1 0.5 C Fair contour 84 15017 1.41 1.52 52.0 118 Paz La RE-SF Rain fed 3.3 0.6 B Good contour 81 15017 2.48 2.45 475.0 119 Chicoloapán HH-L Rain fed 14.6 0.5 C Fair contour 84 15017 1.48 1.58 69.3 120 Chimalhuacán HH-L Rain fed 22.4 0.6 A Fair contour 81 15050 1.17 1.27 67.8 185 B4. Hydrological Response Units (HRU) used for Model Validation — Continued. HRU Monicipio Soil Water S P CN CN's Hydro. Furrow CN Weather Yield Grain Yield Grain Area Type Suply % USLE Group Condition Station Trad. 2002 Trad. 2003 ha 121 Ixtapaluca HH-D Rain fed 9.9 0.5 C Good contour 82 15017 2.03 2.15 92.4 122 Ixtapaluca BH-L Rain fed 15.9 0.5 C Good contour 82 15017 1.38 1.51 20.2 123 Chicoloapán HH-D Rain fed 9.2 0.6 B Fair contour 82 15017 1.85 1.96 752.2 124 Chicoloapán HH-L Rain fed 13.3 0.6 B Fair contour 82 15017 1.40 1.49 60.6 125 Ixtapaluca HH-L Rain fed 11.5 0.5 C Fair contour 84 15017 1.41 1.53 290.2 126 Chicoloapán HH-DP Rain fed 5.7 0.6 C Fair contour 84 15017 2.35 2.31 115.5 127 Ixtapaluca HH-L Rain fed 11.1 0.6 D Fair contour 84 15017 1.55 1.65 27.4 128 Ixtapaluca HH-DP Rain fed 6.4 0.6 C Fair contour 84 15017 2.25 2.33 678.6 129 Ixtapaluca BH-L Rain fed 18.3 0.6 C Poor contour 85 15018 1.46 1.51 36.1 130 Ixtapaluca HH-L Rain fed 13.3 0.6 B Fair contour 82 15017 1.45 1.54 30.3 131 Ixtapaluca HH-D Rain fed 13.5 0.5 C Good contour 82 15017 1.55 1.76 20.2 132 Ixtapaluca TH-P Rain fed 12.4 0.6 C Poor contour 85 15018 1.57 1.64 92.4 133 Ixtapaluca HH-L Rain fed 18.2 0.6 B Fair contour 82 15017 1.48 1.58 130.0 134 Ixtapaluca RE-L Rain fed 12.9 0.6 B Fair contour 82 15050 1.67 1.93 462.1 135 Ixtapaluca HH-D Rain fed 10.9 0.6 B Fair contour 82 15017 1.74 1.85 274.4 136 Ixtapaluca HH-DP Rain fed 8.6 0.6 D Fair contour 84 15017 2.32 2.41 82.3 137 Ixtapaluca HH-D Rain fed 16.2 0.5 C Fair contour 84 15017 1.38 1.60 20.2 138 Chalco HH-SF Rain fed 3.1 0.6 C Good contour 82 15017 2.88 2.87 1569.9 139 Ixtapaluca HH-LP Rain fed 6.9 0.6 C Fair contour 84 15017 2.29 2.42 26.0 140 Ixtapaluca HH-DP Rain fed 8.4 0.6 D Fair contour 84 15017 2.35 2.44 67.9 141 Ixtapaluca HH-D Rain fed 11.3 0.5 C Fair contour 84 15017 1.40 1.60 37.5 142 Ixtapaluca HH-L Rain fed 16.6 0.6 D Fair contour 84 15017 1.54 1.65 17.3 143 Chicoloapán RE-L Rain fed 9.8 0.6 B Fair contour 82 15017 1.98 2.29 15.9 144 Ixtapaluca HH-L Rain fed 15.1 0.5 D Poor contour 85 15018 1.29 1.17 183.4 145 Ixtapaluca HH-D Rain fed 18.0 0.5 C Fair contour 84 15017 1.41 1.63 11.6 146 Ixtapaluca HH-D Rain fed 15.9 0.6 C Poor contour 85 15018 1.64 1.72 67.9 147 Ixtapaluca TH-LP Rain fed 5.4 0.6 C Poor contour 85 15018 2.06 2.15 18.8 148 Paz La RE-G Rain fed 9.9 0.6 B Good contour 81 15050 1.26 1.44 15.9 149 Ixtapaluca RE-L Rain fed 16.1 0.6 B Fair contour 82 15017 2.11 2.20 36.1 150 Ixtapaluca HH-D Rain fed 14.1 0.6 B Fair contour 82 15017 1.77 1.90 30.3 151 Ixtapaluca BE-D Rain fed 13.5 0.5 D Poor contour 85 15018 1.53 1.57 95.3 152 Ixtapaluca HH-DP Rain fed 7.8 0.6 D Fair contour 84 15017 2.33 2.42 206.5 153 Chalco TH-L Rain fed 14.9 0.6 C Poor contour 85 15018 1.51 1.55 98.2 154 Ixtapaluca HH-D Rain fed 12.3 0.6 C Fair contour 84 15020 1.63 1.69 47.7 155 Ixtapaluca BE-D Rain fed 11.5 0.6 C Poor contour 85 15018 1.53 1.57 339.4 156 Chalco ZM-SF Rain fed 0.4 1.0 D Good straight 83 15020 2.72 2.87 514.3 157 Chalco RE-SF Rain fed 1.8 1.0 B Good straight 81 15050 2.91 3.04 11.6 158 Ixtapaluca HH-D Rain fed 13.1 0.5 D Poor contour 85 15018 0.84 0.78 15.9 159 Chalco ZG-SF Rain fed 0.5 1.0 B Good straight 81 15020 2.36 2.45 60.7 160 Ixtapaluca VP-L Rain fed 13.0 0.6 D Fair contour 84 15017 1.67 1.63 20.2 186 B4. Hydrological Response Units (HRU) used for Model Validation — Continued. HRU Monicipio Soil Water S P CN CN's Hydro. Furrow CN Weather Yield Grain Yield Grain Area Type Suply % USLE Group Condition Station Trad. 2002 Trad. 2003 ha 161 Chalco HH-DP Rain fed 7.5 0.5 C Poor contour 85 15018 1.79 1.71 845.1 162 Chalco VP-L Rain fed 14.1 0.5 D Poor contour 85 15018 1.31 1.18 104.0 163 Ixtapaluca TH-P Rain fed 11.7 0.6 C Poor contour 85 15018 1.55 1.63 23.1 164 Ixtapaluca HH-D Rain fed 14.9 0.5 C Poor contour 85 15018 1.69 1.78 13.0 165 Chalco HH-DP Rain fed 6.6 0.5 C Poor contour 85 15018 1.73 1.63 36.1 166 Ixtapaluca RE-LP Rain fed 7.2 0.5 C Fair contour 84 15020 2.01 2.15 47.7 167 Chalco HH-DP Rain fed 5.9 0.5 C Poor contour 85 15018 1.78 1.68 24.6 168 Chalco BE-D Rain fed 18.1 0.6 C Poor contour 85 15018 1.49 1.52 17.3 169 Chalco JE-SF Rain fed 2.3 1.0 C Good straight 82 15020 2.34 2.66 3699.0 170 Chalco BE-D Rain fed 9.6 0.6 C Poor contour 85 15018 1.62 1.66 34.7 171 Chalco HH-DP Rain fed 4.1 0.6 D Good contour 83 15020 2.06 2.24 70.8 172 Chalco HH-DP Rain fed 8.9 0.5 D Poor contour 85 15018 1.04 0.97 15.9 173 Chalco BE-D Rain fed 16.8 0.5 C Poor contour 85 15018 1.45 1.48 20.2 174 Chalco RE-L Rain fed 20.5 0.6 D Good contour 83 15020 2.05 2.17 96.8 175 Chalco HH-DP Rain fed 8.5 0.6 D Fair contour 84 15280 2.26 2.23 177.7 176 Chalco HH-D Rain fed 14.3 0.5 D Poor contour 85 15280 1.75 1.74 30.3 177 Chalco GM-SF Rain fed 1.0 1.0 C Good straight 82 15020 2.83 3.22 755.7 178 Chalco HH-D Rain fed 9.1 0.5 D Poor contour 85 15280 2.00 1.99 59.2 179 Chalco HH-D Rain fed 12.6 0.5 D Poor contour 85 15280 1.79 1.77 26.0 180 Chalco TO-L Rain fed 12.0 0.5 D Good contour 83 15280 1.60 1.59 150.3 181 Chalco HH-D Rain fed 11.3 0.5 D Fair contour 84 15280 1.80 1.77 34.7 182 Chalco RE-G Rain fed 45.1 0.6 B Good contour 81 15020 1.33 1.34 33.2 183 Chalco TO-LP Rain fed 5.5 0.5 C Good contour 82 15280 2.13 2.01 20.2 184 Chalco HH-LP Rain fed 4.7 0.6 C Fair contour 84 15094 1.63 2.18 62.1 185 Chalco RE-LP Rain fed 5.6 0.6 C Good contour 82 15280 2.17 2.00 365.6 186 Chalco HH-LP Rain fed 8.1 0.6 B Fair contour 82 15280 2.22 2.00 18.8 187 Chalco HH-DP Rain fed 3.5 0.6 B Good contour 81 15020 1.93 2.13 153.2 188 Cocotitlán HH-DP Rain fed 6.5 0.6 C Good contour 82 15094 1.97 2.43 30.3 189 Chalco RE-LP Rain fed 6.2 0.6 C Good contour 82 15280 2.26 2.24 11.6 190 Cocotitlán HH-LP Rain fed 6.3 0.6 B Poor contour 83 15094 1.70 2.26 31.8 191 Chalco HH-LP Rain fed 4.9 0.6 A Good contour 80 15020 1.79 1.99 17.3 192 Chalco HH-LP Rain fed 5.7 0.6 A Fair contour 81 15020 1.81 2.00 28.9 193 Chalco JE-G Rain fed 3.6 0.6 B Good contour 81 15094 1.45 1.89 66.5 194 Tlamanalco TH-P Rain fed 18.3 0.6 B Poor contour 83 15106 1.30 1.94 11.6 195 Temamatla JE-G Rain fed 3.9 0.6 B Good contour 81 15094 1.95 2.52 492.8 196 Tlamanalco TO-L Rain fed 11.9 0.6 C Good contour 82 15280 1.82 1.81 173.4 197 Amecameca RE-LP Rain fed 7.6 0.6 C Good contour 82 15094 2.10 2.57 1728.8 198 Amecameca HH-L Rain fed 47.9 0.6 B Poor contour 83 15106 1.21 1.64 40.5 199 Temamatla HH-G Rain fed 4.2 0.6 B Good contour 81 15094 2.12 2.58 11.6 200 Tlamanalco RE-LP Rain fed 6.1 0.6 C Good contour 82 15280 2.60 2.57 33.2 187 B4. Hydrological Response Units (HRU) used for Model Validation — Continued. HRU Monicipio Soil Water S P CN CN's Hydro. Furrow CN Weather Yield Grain Yield Grain Area Type Suply % USLE Group Condition Station Trad. 2002 Trad. 2003 ha 201 Ayapango JD-G Rain fed 5.3 0.6 B Good contour 81 15007 2.56 2.48 2708.2 202 Tlamanalco HH-LP Rain fed 7.3 0.6 B Fair contour 82 15094 2.03 2.74 161.9 203 Juchitepec HH-L Rain fed 11.0 0.6 C Poor contour 85 15039 1.76 1.77 208.2 204 Amecameca TH-P Rain fed 16.9 0.6 B Good contour 81 15007 1.92 1.91 59.3 205 Tlamanalco TH-LP Rain fed 6.1 0.6 C Good contour 82 15280 2.67 2.54 15.9 206 Tlamanalco TH-P Rain fed 15.4 0.6 C Good contour 82 15280 1.97 1.94 15.9 207 Temamatla HH-LP Rain fed 4.8 0.6 B Good contour 81 15094 1.91 2.52 23.1 208 Tlamanalco TH-P Rain fed 8.2 0.6 B Good contour 81 15280 2.20 2.05 27.5 209 Tlamanalco RE-LP Rain fed 5.4 0.6 C Good contour 82 15280 2.60 2.57 65.0 210 Ayapango HH-G Rain fed 4.1 0.6 B Good contour 81 15094 2.11 2.70 806.8 211 Atlautla RD-G Rain fed 5.7 0.6 B Good contour 81 15007 2.67 2.61 3955.7 212 Juchitepec RE-LP Rain fed 7.5 0.6 C Fair contour 84 15039 2.55 2.57 13.0 213 Juchitepec TH-P Rain fed 8.9 0.6 C Fair contour 84 15039 2.20 2.21 40.5 214 Juchitepec RE-G Rain fed 9.4 0.6 B Fair contour 82 15039 2.11 2.11 2969.0 215 Juchitepec RE-L Rain fed 9.6 0.6 C Fair contour 84 15039 2.73 2.75 63.6 216 Tenango del aire HH-LP Rain fed 6.4 0.6 B Fair contour 82 15094 2.31 3.19 20.2 217 Amecameca RE-L Rain fed 29.3 0.6 A Good contour 80 15007 2.26 2.26 11.6 218 Ayapango HH-L Rain fed 9.8 0.6 B Poor contour 83 15039 1.95 1.92 34.7 219 Ayapango HH-G Rain fed 1.4 1.0 B Fair straight 82 15039 2.85 2.73 89.6 220 Tenango del aire HH-LP Rain fed 3.2 0.6 C Poor contour 85 15039 3.25 2.95 31.8 221 Ayapango TH-LP Rain fed 2.9 1.0 B Fair straight 82 15039 2.46 2.24 39.0 222 Amecameca JD-LP Rain fed 5.1 0.6 A Fair contour 81 15007 2.45 2.47 60.7 223 Amecameca JD-LP Rain fed 3.4 0.6 A Fair contour 81 15007 2.45 2.48 276.2 224 Juchitepec TH-P Rain fed 21.9 0.6 B Fair contour 82 15039 1.93 1.93 21.7 225 Juchitepec RE-LP Rain fed 5.0 0.6 B Poor contour 83 15039 2.55 2.57 150.4 226 Juchitepec TH-P Rain fed 23.9 0.6 B Fair contour 82 15039 1.93 1.93 13.0 227 Amecameca RD-G Rain fed 7.8 0.6 B Good contour 81 15007 2.49 2.52 186.6 228 Juchitepec TH-P Rain fed 22.5 0.6 C Fair contour 84 15039 1.93 1.93 11.6 229 Juchitepec TH-P Rain fed 27.9 0.6 B Fair contour 82 15039 1.93 1.92 11.6 230 Juchitepec HH-L Rain fed 19.6 0.6 B Poor contour 83 15039 1.76 1.76 30.4 231 Juchitepec RD-P Rain fed 8.5 0.6 B Fair contour 82 15039 2.61 2.63 14.5 232 Juchitepec RE-LP Rain fed 8.6 0.6 B Fair contour 82 15039 2.54 2.57 475.9 233 Juchitepec HH-LP Rain fed 5.2 0.6 B Poor contour 83 15039 2.72 2.75 422.4 234 Juchitepec BH-G Rain fed 7.4 0.6 B Fair contour 82 15039 2.65 2.50 159.1 235 Juchitepec TH-P Rain fed 10.1 0.6 C Poor contour 85 17021 1.76 1.90 109.9 236 Amecameca RD-P Rain fed 7.4 0.6 B Good contour 81 15007 2.57 2.59 44.8 237 Juchitepec HH-L Rain fed 11.8 0.6 C Poor contour 85 17021 1.71 1.81 44.8 238 Juchitepec TH-P Rain fed 24.0 0.6 C Fair contour 84 15039 1.93 1.93 21.7 239 Atlautla HH-D Rain fed 14.4 0.6 B Fair contour 82 15252 1.91 1.83 27.5 240 Tepetlixpa RD-G Rain fed 5.6 0.6 B Fair contour 82 15252 2.73 2.29 510.7 188 B4. Hydrological Response Units (HRU) used for Model Validation — Continued. HRU Monicipio Soil Water S P CN CN's Hydro. Furrow CN Weather Yield Grain Yield Grain Area Type Suply % USLE Group Condition Station Trad. 2002 Trad. 2003 ha 241 Tepetlixpa TH-P Rain fed 15.8 0.6 B Poor contour 83 17051 1.66 1.66 390.7 242 Tepetlixpa HH-LP Rain fed 7.6 0.6 B Poor contour 83 15252 2.76 2.38 27.5 243 Tepetlixpa TH-LP Rain fed 5.4 0.6 B Fair contour 82 15252 2.65 2.19 195.3 244 Tepetlixpa JE-P Rain fed 9.3 0.6 B Fair contour 82 15252 2.47 2.38 11.6 245 Tepetlixpa TO-L Rain fed 10.4 0.6 B Fair contour 82 15252 1.88 1.44 70.9 246 Tepetlixpa TH-L Rain fed 14.4 0.6 B Poor contour 83 15252 1.93 1.56 13.0 247 Tepetlixpa BE-P Rain fed 9.6 0.6 B Fair contour 82 15252 2.31 1.69 400.9 248 Ozumba TO-L Rain fed 8.6 0.6 B Fair contour 82 15252 1.81 1.32 86.8 249 Ozumba TO-L Rain fed 9.7 0.6 B Poor contour 83 15252 1.85 1.40 18.8 250 Ecatzingo TH-P Rain fed 9.0 0.6 B Good contour 81 17045 1.95 1.46 1448.8 251 Tepetlixpa TH-P Rain fed 23.8 0.6 B Poor contour 83 17051 1.93 2.10 14.5 252 Tepetlixpa TO-L Rain fed 9.9 0.6 B Poor contour 83 17051 1.94 1.86 188.2 253 Atlautla TH-L Rain fed 13.7 0.6 B Fair contour 82 17045 1.74 1.50 13.0 254 Tepetlixpa BE-P Rain fed 13.3 0.6 B Fair contour 82 17051 1.60 1.58 15.9 255 Atlautla TO-L Rain fed 32.8 0.6 B Fair contour 82 17045 1.67 1.49 33.3 62599.4 189 B5. Hydrological balance, corn yield and soil erosion by HRU. HRU Rain- Runoff Plant ET Perco- Subsuper Irriga- Corn Corn Corn Corn Soil Soil Soil fall evap. lation ficial tion yield yield yield yield erosion erosion erosion flow Mng Current Current Current Current Current Current Current Current INIFAP INIFAP Current Current Current Yr 1 1 1 1 1 1 1 100 1 100 1 50 100 Units mm mm mm mm mm mm mm t/ha t/ha t/ha t/ha t/ha t/ha t/ha 1 706.2 102.8 354.3 633.8 342.4 11.7 390 2.89 2.84 2.36 2.30 1.0 1.7 2.3 2 749.1 116.8 376.3 665.5 346.8 2.1 390 2.64 2.61 2.60 2.56 0.7 0.8 0.9 3 880.8 259.4 304.4 619.7 339.3 51.3 390 1.43 1.42 1.16 1.16 111.8 120.7 126.4 4 663.6 98.6 367.1 652.0 292.2 2.4 390 2.74 2.73 2.46 2.45 0.7 0.7 0.7 5 697.2 97.9 367.3 657.6 320.4 2.2 390 3.20 3.16 2.97 2.91 0.4 0.5 0.6 6 781.5 152.8 367.3 649.2 344.7 20.3 390 2.52 2.50 2.00 1.99 12.0 13.0 13.6 7 683.6 96.5 373.9 675.8 291.1 2.3 390 3.41 3.40 3.33 3.34 0.6 0.6 0.6 8 682.2 93.5 374.5 671.0 295.2 5.2 390 3.33 3.31 2.92 2.89 1.0 1.0 1.0 9 706.1 92.8 368.8 656.4 332.4 9.5 390 3.08 3.07 2.85 2.83 1.4 1.5 1.6 10 805.5 203.3 307.3 626.5 319.8 44.3 390 1.72 1.71 1.29 1.29 79.6 79.8 79.8 11 708.6 99.2 372.1 668.0 317.6 6.2 390 3.14 3.12 3.05 3.02 1.4 1.4 1.4 12 872.1 185.7 364.1 643.3 413.6 18.1 390 2.00 1.99 1.50 1.50 11.1 12.0 12.5 13 810.2 104.4 386.4 665.2 423.1 2.4 390 2.64 2.64 2.39 2.38 0.8 0.8 0.8 14 807.6 81.4 274.7 593.6 125.3 0.3 0 2.16 2.08 3.68 3.64 1.5 1.6 1.6 15 687.0 54.6 299.9 584.7 43.3 1.4 0 2.18 2.10 3.60 3.56 1.8 1.8 1.8 16 864.7 94.2 196.1 524.0 245.8 0.8 0 1.83 1.75 2.50 2.45 0.9 1.4 1.9 17 789.2 77.1 266.8 555.5 153.4 1.4 0 2.06 1.96 3.50 3.38 0.8 1.3 1.8 18 830.5 88.1 261.0 598.0 139.1 1.8 0 2.23 2.14 3.34 3.30 2.6 2.6 2.6 19 795.4 81.8 266.3 584.7 121.5 3.2 0 2.16 2.09 3.61 3.57 4.4 4.5 4.5 20 802.2 93.0 280.9 598.1 101.7 2.9 0 2.17 2.09 3.74 3.69 4.9 5.1 5.1 21 824.6 158.9 207.6 533.8 116.2 12.5 0 1.62 1.56 3.14 3.10 56.7 61.4 63.7 22 874.7 89.8 234.5 583.5 196.6 3.1 0 2.12 2.04 3.12 3.10 3.3 3.4 3.4 23 785.3 73.8 286.0 592.0 112.9 0.8 0 2.25 2.17 3.90 3.85 1.6 1.6 1.6 24 715.1 49.2 294.1 584.3 75.0 1.3 0 2.18 2.11 3.54 3.51 1.4 1.4 1.4 25 721.7 167.9 169.6 508.3 28.2 16.8 0 1.23 1.17 3.29 3.26 252.4 256.5 256.5 26 831.5 191.9 193.1 531.5 82.9 23.3 0 1.30 1.25 2.21 2.17 148.6 163.3 171.0 27 886.1 197.8 242.3 563.0 101.7 21.6 0 1.62 1.59 2.16 2.12 104.7 114.6 120.1 28 855.8 165.2 246.4 562.1 106.6 20.5 0 1.72 1.70 1.73 1.69 57.3 62.4 65.3 29 867.7 200.1 196.0 534.4 108.6 23.0 0 1.33 1.29 2.02 1.99 123.5 135.0 141.3 30 799.9 204.4 175.1 524.1 38.2 31.7 0 1.31 1.27 2.75 2.71 441.7 483.6 506.4 31 699.2 107.9 199.1 516.8 59.2 14.0 0 1.66 1.64 2.87 2.78 32.2 34.6 35.9 32 871.2 235.1 192.7 551.8 44.7 38.2 0 1.27 1.24 2.13 2.12 643.8 705.0 738.1 33 746.6 104.6 227.3 531.7 89.4 15.4 0 1.84 1.77 3.46 3.36 32.1 40.1 45.1 34 843.4 214.9 195.7 541.6 59.9 24.9 0 1.30 1.26 2.42 2.40 263.2 287.3 299.2 35 657.6 50.2 247.5 536.8 64.7 4.3 0 1.85 1.75 3.96 3.85 2.5 4.3 5.8 190 B5. Hydrological balance, corn yield and soil erosion by HRU — Continued. HRU Rain- Runoff Plant ET Perco- Subsuper Irriga- Corn Corn Corn Corn Soil Soil Soil fall evap. lation ficial tion yield yield yield yield erosion erosion erosion flow Mng Current Current Current Current Current Current Current Current INIFAP INIFAP Current Current Current Yr 1 1 1 1 1 1 1 100 1 100 1 50 100 Units mm mm mm mm mm mm mm t/ha t/ha t/ha t/ha t/ha t/ha t/ha 36 838.5 142.4 263.5 575.2 98.7 19.0 0 1.77 1.69 2.83 2.79 72.7 78.9 81.9 37 861.2 94.6 243.1 588.9 174.7 1.3 0 2.13 2.06 3.22 3.20 2.5 2.5 2.5 38 693.9 118.4 164.6 509.4 42.8 20.5 0 1.28 1.26 2.75 2.67 92.9 102.0 106.9 39 798.0 109.4 223.7 534.0 146.3 8.0 0 1.62 1.54 3.25 3.17 11.8 12.7 13.2 40 852.3 154.5 212.1 545.6 134.1 16.9 0 1.59 1.53 2.97 2.91 55.0 67.6 76.1 41 854.6 143.7 239.7 576.2 114.3 19.2 0 1.84 1.78 2.85 2.81 47.5 51.0 52.9 42 651.9 37.9 295.5 575.5 36.3 0.7 0 2.14 2.07 3.56 3.53 0.7 0.9 1.0 43 774.7 175.4 164.1 521.8 51.9 22.9 0 1.22 1.17 2.83 2.78 195.2 216.7 227.2 44 771.4 167.1 174.4 522.8 58.0 21.4 0 1.27 1.23 2.72 2.66 144.4 159.7 167.4 45 886.1 154.6 237.7 560.9 145.9 24.7 0 1.74 1.72 1.70 1.68 59.8 64.1 66.6 46 850.3 84.1 267.5 596.9 165.9 1.2 0 2.16 2.08 3.37 3.35 2.1 2.1 2.1 47 691.7 94.4 231.2 529.3 55.0 11.9 0 1.72 1.64 3.65 3.57 36.5 45.7 51.6 48 856.8 132.2 266.9 557.3 147.3 18.3 0 1.82 1.75 2.04 1.89 11.1 19.5 26.9 49 659.2 45.4 290.4 571.6 37.4 1.6 0 2.13 2.05 3.54 3.51 1.8 1.8 1.8 50 906.7 210.8 206.0 518.8 149.2 26.4 0 1.36 1.29 1.18 1.01 27.8 55.4 83.3 51 703.2 50.3 289.1 578.7 70.6 0.8 0 2.20 2.12 3.93 3.86 0.9 1.2 1.3 52 858.4 212.2 190.0 541.2 56.3 47.9 0 1.27 1.23 1.98 1.96 776.3 786.6 786.6 53 878.6 126.4 181.1 543.5 191.7 15.4 0 1.54 1.51 2.72 2.72 45.6 45.9 45.9 54 850.7 215.8 153.3 534.1 65.8 33.6 0 1.10 1.06 2.68 2.66 614.0 672.6 705.2 55 758.9 161.8 172.7 535.6 19.0 39.1 0 1.29 1.25 2.82 2.79 710.8 782.6 820.5 56 892.9 173.3 189.1 545.9 142.0 30.8 0 1.41 1.35 2.37 2.33 161.3 173.6 180.0 57 698.2 96.3 220.1 529.3 56.7 10.9 0 1.75 1.68 3.58 3.52 33.8 37.2 39.0 58 803.7 142.8 217.0 541.5 102.2 17.1 0 1.70 1.68 2.33 2.27 38.7 41.3 42.8 59 771.9 42.5 279.9 595.1 127.5 2.3 0 2.24 2.16 3.49 3.45 1.4 1.4 1.4 60 830.8 107.1 244.3 533.6 179.2 10.7 0 1.81 1.74 2.33 2.13 3.3 5.7 7.9 61 722.7 108.4 209.0 527.5 71.7 14.8 0 1.66 1.64 2.65 2.57 27.4 29.3 30.4 62 873.2 143.7 253.7 574.7 135.5 15.9 0 1.83 1.77 2.92 2.90 52.7 53.7 53.7 63 695.9 97.9 191.2 517.4 65.7 14.2 0 1.66 1.64 2.73 2.69 30.2 32.4 33.6 64 755.8 80.9 212.1 542.6 121.3 6.1 0 1.83 1.71 3.48 3.39 9.5 10.7 11.3 65 894.8 178.7 162.7 522.4 175.2 17.1 0 1.44 1.40 2.46 2.45 96.7 97.6 97.6 66 748.8 110.8 228.5 548.7 71.1 11.9 0 1.79 1.73 3.54 3.52 45.0 46.2 46.2 67 902.4 146.3 237.7 565.8 168.4 21.0 0 1.84 1.79 2.52 2.50 49.4 49.8 49.8 68 880.4 175.6 186.5 534.2 133.8 36.4 0 1.37 1.32 1.61 1.54 106.6 135.1 154.9 69 770.5 153.0 196.2 508.2 96.2 11.7 0 1.37 1.33 2.77 2.72 45.2 48.8 50.7 70 841.8 181.5 201.0 546.1 84.3 27.9 0 1.35 1.32 1.69 1.67 151.2 165.7 173.5 191 B5. Hydrological balance, corn yield and soil erosion by HRU — Continued. HRU Rain- Runoff Plant ET Perco- Subsuper Irriga- Corn Corn Corn Corn Soil Soil Soil fall evap. lation ficial flow tion yield yield yield yield erosion erosion erosion Mng Current Current Current Current Current Current Current Current INIFAP INIFAP Current Current Current Yr 1 1 1 1 1 1 1 100 1 100 1 50 100 Units mm mm mm mm mm mm mm t/ha t/ha t/ha t/ha t/ha t/ha t/ha 71 884.2 112.4 221.9 571.2 198.6 1.0 0 2.05 1.97 2.92 2.91 3.1 3.1 3.1 72 711.1 125.3 175.9 507.3 59.7 16.2 0 1.29 1.26 2.45 2.38 54.8 60.2 63.0 73 697.3 121.4 169.6 502.8 54.9 15.7 0 1.29 1.26 2.70 2.63 55.4 60.8 63.7 74 842.1 156.2 202.6 546.3 110.6 27.9 0 1.36 1.32 1.71 1.67 94.5 103.5 108.4 75 851.2 80.5 215.0 561.1 207.4 0.6 0 2.02 1.95 2.91 2.91 1.7 1.8 1.8 76 865.7 204.1 187.8 540.6 87.0 32.8 0 1.32 1.29 1.91 1.88 214.7 231.3 240.1 77 705.9 125.5 169.9 513.0 42.5 23.0 0 1.28 1.26 2.65 2.56 107.6 118.4 124.1 78 871.2 187.9 190.5 544.3 110.3 28.3 0 1.33 1.29 1.65 1.62 119.4 129.8 135.8 79 861.0 157.8 189.8 540.6 136.7 25.3 0 1.37 1.34 1.63 1.60 71.0 77.3 81.0 80 853.1 88.3 222.7 566.5 196.6 0.4 0 2.07 2.00 3.02 3.01 1.9 1.9 1.9 81 783.0 119.7 194.9 541.5 100.3 18.4 0 1.48 1.41 2.93 2.86 48.4 52.5 54.4 82 879.2 132.4 199.4 567.7 138.8 38.5 0 1.51 1.43 2.61 2.58 124.9 134.7 139.7 83 867.6 91.6 247.6 581.8 191.3 0.9 0 2.11 2.04 3.24 3.22 2.1 2.1 2.1 84 816.3 177.8 192.8 530.6 86.5 20.4 0 1.39 1.36 1.95 1.87 65.1 79.4 88.8 85 771.9 178.3 176.5 524.0 46.2 20.4 0 1.28 1.24 2.79 2.73 170.3 188.1 196.9 86 868.3 153.2 197.9 551.3 134.2 28.5 0 1.50 1.42 2.48 2.44 94.6 101.8 105.6 87 772.6 131.2 225.5 550.5 74.3 15.2 0 1.66 1.64 2.22 2.15 37.1 40.6 42.5 88 690.6 103.2 174.8 513.0 45.4 27.5 0 1.35 1.33 2.51 2.39 99.1 121.3 136.1 89 766.9 146.4 175.9 527.3 69.6 22.3 0 1.36 1.33 2.45 2.32 64.8 79.5 89.2 90 684.6 84.9 220.0 527.9 59.4 8.6 0 1.78 1.72 3.69 3.62 18.7 20.6 21.6 91 741.7 118.6 177.2 509.6 87.9 24.7 0 1.38 1.34 2.23 2.07 55.3 70.6 81.2 92 855.9 133.0 231.2 552.6 149.9 20.3 0 1.74 1.70 1.61 1.50 24.5 31.0 35.6 93 676.2 98.4 197.4 518.4 43.1 14.3 0 1.61 1.60 2.96 2.88 40.7 44.1 45.9 94 661.2 69.9 215.8 524.2 55.1 7.6 0 1.77 1.71 3.75 3.67 13.3 14.7 15.4 95 818.0 168.2 228.8 557.8 72.1 18.5 0 1.67 1.65 2.10 2.07 71.5 78.1 81.8 96 720.5 65.0 257.6 574.5 74.9 1.6 0 2.13 2.05 3.59 3.55 2.4 2.4 2.4 97 856.1 148.9 240.0 576.8 110.1 18.2 0 1.77 1.71 2.96 2.92 50.4 55.2 57.7 98 671.8 36.7 290.0 584.8 45.9 2.1 0 2.27 2.18 4.05 3.96 2.0 2.0 2.0 99 746.7 95.2 234.2 543.3 94.7 8.5 0 1.82 1.76 3.48 3.41 14.7 16.2 16.9 100 873.1 216.6 185.7 523.6 87.9 45.0 0 1.32 1.25 1.53 1.35 111.2 222.6 334.8 101 843.5 161.0 185.2 551.5 106.6 23.9 0 1.35 1.32 2.14 2.12 90.3 97.1 101.1 102 832.6 160.7 229.7 561.5 90.0 18.8 0 1.72 1.68 2.12 2.09 67.1 67.9 67.9 103 869.1 146.6 256.7 569.1 132.9 16.2 0 1.83 1.76 2.85 2.79 34.1 42.4 47.7 104 757.9 98.8 260.5 564.5 79.1 11.3 0 1.77 1.70 2.92 2.88 26.5 29.4 30.9 105 736.4 97.0 224.3 552.6 67.4 14.0 0 1.76 1.71 3.51 3.45 33.9 37.5 39.3 192 B5. Hydrological balance, corn yield and soil erosion by HRU — Continued. HRU Rain- Runoff Plant ET Perco- Subsuper Irriga- Corn Corn Corn Corn Soil Soil Soil fall evap. lation ficial flow tion yield yield yield yield erosion erosion erosion Mng Current Current Current Current Current Current Current Current INIFAP INIFAP Current Current Current Yr 1 1 1 1 1 1 1 100 1 100 1 50 100 Units mm mm mm mm mm mm mm t/ha t/ha t/ha t/ha t/ha t/ha t/ha 106 868.4 179.8 240.2 561.4 105.5 21.0 0 1.69 1.67 1.70 1.66 65.8 71.4 74.8 107 856.6 178.6 240.1 560.4 95.1 21.3 0 1.71 1.69 1.86 1.84 85.2 91.7 95.4 108 876.3 149.2 262.8 578.7 129.5 15.5 0 1.79 1.71 2.81 2.77 43.2 47.6 49.8 109 885.3 170.4 232.4 565.7 124.6 23.9 0 1.67 1.65 1.79 1.77 74.7 80.3 83.5 110 858.9 207.6 189.6 543.6 86.4 19.6 0 1.30 1.26 2.35 2.33 116.2 127.3 133.3 111 731.6 105.4 224.1 536.5 76.6 11.8 0 1.66 1.59 3.53 3.48 45.4 49.3 51.2 112 768.6 161.5 192.4 521.4 62.2 23.5 0 1.31 1.27 2.26 2.20 160.7 175.3 183.6 113 717.1 87.6 227.4 540.7 76.7 11.5 0 1.68 1.60 3.60 3.55 34.2 37.2 38.6 114 856.0 175.2 194.6 548.9 102.6 27.8 0 1.30 1.25 2.08 2.06 184.2 187.0 187.0 115 801.8 180.4 158.2 505.9 90.2 23.8 0 1.13 1.09 2.73 2.70 222.3 225.5 225.5 116 882.6 185.8 239.8 564.1 110.6 22.0 0 1.71 1.67 1.74 1.73 84.9 85.5 85.5 117 838.8 184.6 184.7 536.6 93.0 23.8 0 1.34 1.31 2.09 2.05 113.7 123.7 129.5 118 728.6 50.0 229.7 551.4 118.5 6.1 0 1.89 1.76 3.84 3.74 5.4 6.1 6.4 119 799.3 182.3 183.7 533.8 57.1 23.3 0 1.27 1.22 2.58 2.54 189.6 210.0 219.8 120 798.8 165.4 191.2 541.0 54.2 36.1 0 1.25 1.20 2.14 2.09 380.0 417.4 437.2 121 773.6 128.7 246.2 555.8 69.6 18.6 0 1.67 1.62 2.32 2.27 84.1 85.0 85.0 122 837.7 157.0 192.6 548.6 97.9 33.0 0 1.39 1.35 1.86 1.78 112.3 137.2 153.7 123 691.6 101.7 214.4 524.5 50.8 14.4 0 1.69 1.63 2.91 2.84 56.8 57.3 57.3 124 788.0 156.0 193.4 529.5 77.1 24.1 0 1.29 1.25 2.33 2.29 145.5 159.2 166.7 125 727.6 134.6 180.9 515.5 56.8 18.0 0 1.29 1.26 2.31 2.24 72.2 79.3 83.0 126 748.4 99.9 213.1 543.2 93.2 10.0 0 1.63 1.56 3.39 3.35 28.8 31.6 33.1 127 820.3 193.6 190.2 529.5 76.1 19.9 0 1.23 1.19 1.97 1.93 137.3 150.3 157.4 128 713.8 87.7 250.2 548.7 64.9 9.9 0 1.80 1.73 3.25 3.19 22.0 24.2 25.4 129 809.2 172.3 184.1 544.9 60.2 30.5 0 1.36 1.32 2.11 2.05 159.8 195.1 218.3 130 843.3 189.1 194.0 544.7 82.2 25.1 0 1.30 1.26 2.11 2.07 167.2 183.7 192.3 131 837.0 154.0 188.7 549.8 103.8 28.4 0 1.39 1.33 1.98 1.95 121.2 122.7 122.7 132 780.9 159.4 182.0 511.6 85.3 23.3 0 1.39 1.35 1.96 1.82 69.8 88.8 101.8 133 762.7 153.1 189.7 532.9 46.1 27.4 0 1.28 1.24 2.44 2.40 243.7 268.7 281.5 134 713.3 100.2 197.1 529.7 60.7 18.4 0 1.46 1.38 3.21 3.15 81.0 88.5 92.0 135 757.2 144.1 191.5 526.4 66.0 18.4 0 1.32 1.27 2.47 2.41 118.7 120.9 120.9 136 790.7 119.9 255.0 564.8 86.3 15.6 0 1.74 1.68 2.72 2.67 51.2 56.6 59.2 137 846.3 180.2 185.0 554.7 80.0 30.4 0 1.36 1.31 2.16 2.14 206.1 208.9 208.9 138 709.6 51.3 210.9 550.6 97.2 4.9 0 1.83 1.73 3.60 3.55 6.8 7.0 7.0 139 825.3 130.7 259.5 561.7 114.4 14.5 0 1.81 1.74 2.78 2.71 29.9 37.3 42.0 140 788.5 122.2 260.7 568.3 78.6 14.7 0 1.77 1.70 2.86 2.83 51.2 56.8 59.5 193 B5. Hydrological balance, corn yield and soil erosion by HRU — Continued. HRU Rain- Runoff Plant ET Perco- Subsuper Irriga- Corn Corn Corn Corn Soil Soil Soil fall evap. lation ficial tion yield yield yield yield erosion erosion erosion flow Mng Current Current Current Current Current Current Current Current INIFAP INIFAP Current Current Current Yr 1 1 1 1 1 1 1 100 1 100 1 50 100 Units mm mm mm mm mm mm mm t/ha t/ha t/ha t/ha t/ha t/ha t/ha 141 836.3 169.3 188.4 549.7 93.8 22.5 0 1.39 1.34 1.99 1.96 104.9 106.3 106.3 142 840.7 210.8 192.8 536.8 63.7 28.0 0 1.27 1.24 2.09 2.05 277.7 303.1 317.2 143 862.3 119.8 270.5 578.1 137.8 23.6 0 1.89 1.83 2.28 2.24 45.5 49.1 50.9 144 741.3 161.0 185.8 518.3 39.7 20.8 0 1.25 1.21 2.20 2.15 192.1 210.4 220.5 145 871.7 185.3 204.3 550.1 97.3 37.8 0 1.38 1.33 1.61 1.59 255.1 258.5 258.5 146 782.2 171.5 182.7 534.0 50.1 24.3 0 1.34 1.29 2.27 2.23 202.5 206.4 206.4 147 837.6 116.7 273.9 562.2 143.2 13.0 0 1.89 1.83 2.25 2.17 12.9 16.3 18.7 148 853.4 160.5 221.1 540.4 127.9 23.6 0 1.55 1.52 1.67 1.65 104.6 105.1 105.1 149 818.2 145.2 204.5 558.8 83.8 27.6 0 1.46 1.38 2.71 2.67 173.6 190.0 197.2 150 844.3 193.9 184.7 534.8 87.3 26.8 0 1.29 1.24 1.96 1.93 241.8 245.0 245.0 151 775.1 169.9 184.0 528.0 53.9 21.1 0 1.30 1.27 2.36 2.31 138.5 150.4 156.5 152 761.1 110.7 259.2 567.1 68.4 12.8 0 1.73 1.66 2.76 2.71 38.8 42.7 44.7 153 791.4 180.4 196.3 532.7 52.4 23.6 0 1.35 1.31 2.11 2.07 174.6 188.7 195.9 154 802.6 204.5 126.2 497.8 79.8 20.0 0 1.08 1.03 2.31 2.29 293.4 296.8 296.8 155 733.5 137.8 184.6 516.9 57.8 18.3 0 1.33 1.30 2.31 2.25 84.3 91.8 95.6 156 740.9 55.4 229.3 559.8 120.6 0.8 0 2.17 2.08 3.16 3.14 1.2 1.2 1.2 157 867.9 76.5 247.3 582.4 203.1 4.9 0 2.13 2.03 3.21 3.17 4.0 4.4 4.6 158 847.1 243.9 142.4 514.0 67.6 20.5 0 1.05 1.01 2.45 2.44 356.0 360.8 360.8 159 806.7 67.2 212.1 555.6 180.4 1.2 0 2.05 1.97 2.87 2.86 1.3 1.6 1.8 160 849.0 234.5 125.7 501.5 89.2 22.3 0 1.00 0.97 2.18 2.15 295.5 322.5 337.7 161 727.1 105.4 254.5 550.4 56.8 11.2 0 1.75 1.69 2.94 2.90 35.5 39.0 40.8 162 784.1 185.4 190.3 529.7 46.3 21.0 0 1.24 1.19 2.13 2.10 197.7 217.7 228.4 163 850.1 185.2 200.0 529.0 109.5 25.4 0 1.41 1.37 1.40 1.32 75.2 95.6 109.6 164 865.2 213.1 193.8 543.8 78.8 27.7 0 1.36 1.31 1.78 1.77 226.1 229.6 229.6 165 839.5 150.1 228.8 550.5 122.7 13.9 0 1.72 1.66 2.35 2.33 41.8 45.8 47.9 166 821.6 161.1 150.5 517.8 126.7 14.0 0 1.44 1.39 2.40 2.40 97.4 98.6 98.6 167 881.3 158.4 238.3 554.9 151.9 14.3 0 1.68 1.61 2.36 2.32 38.0 41.6 43.5 168 850.1 201.7 191.5 545.7 69.3 32.1 0 1.31 1.27 2.02 2.00 269.9 292.0 303.8 169 731.5 54.6 191.4 530.9 139.6 4.6 0 1.82 1.72 3.18 3.15 5.1 5.2 5.2 170 833.7 174.5 238.7 553.6 83.8 19.4 0 1.69 1.66 1.81 1.78 74.2 80.4 83.6 171 803.7 115.1 185.7 526.9 149.0 9.2 0 1.57 1.51 2.85 2.82 20.9 22.9 23.9 172 854.4 191.9 207.0 557.3 88.1 15.3 0 1.58 1.52 3.13 3.09 119.4 130.6 136.7 173 863.3 208.4 198.1 549.8 72.3 30.6 0 1.34 1.31 1.82 1.79 245.9 267.2 278.3 174 790.5 153.0 142.9 522.4 82.5 31.7 0 1.29 1.22 2.81 2.78 371.1 401.8 416.7 175 761.9 113.4 241.1 548.4 82.9 14.6 0 1.67 1.61 2.61 2.57 48.4 53.1 55.6 194 B5. Hydrological balance, corn yield and soil erosion by HRU — Continued. HRU Rain- Runoff Plant ET Perco- Subsuper Irriga- Corn Corn Corn Corn Soil Soil Soil fall evap. lation ficial tion yield yield yield yield erosion erosion erosion flow Mng Current Current Current Current Current Current Current Current INIFAP INIFAP Current Current Current Yr 1 1 1 1 1 1 1 100 1 100 1 50 100 Units mm mm mm mm mm mm mm t/ha t/ha t/ha t/ha t/ha t/ha t/ha 176 860.6 232.3 156.1 515.0 86.4 25.7 0 1.16 1.13 1.66 1.64 293.8 297.0 297.0 177 746.9 51.3 223.7 553.3 137.4 1.9 0 2.28 2.21 3.11 3.11 1.6 1.7 1.7 178 814.8 181.4 246.7 552.8 61.5 16.5 0 1.65 1.60 2.25 2.21 105.0 106.9 106.9 179 865.8 231.1 183.9 529.7 80.5 22.9 0 1.24 1.19 1.96 1.94 247.6 251.5 251.5 180 784.8 160.1 193.7 525.8 75.6 21.5 0 1.30 1.27 2.18 2.12 125.1 136.8 143.3 181 836.3 205.6 189.8 530.3 78.4 20.4 0 1.29 1.23 2.05 2.01 170.7 173.8 173.8 182 839.3 186.2 130.3 547.0 41.9 62.6 0 1.10 1.07 2.29 2.28 2483.4 2512.2 2512.2 183 855.1 140.7 181.7 541.9 158.2 12.9 0 1.55 1.51 2.76 2.75 47.2 49.7 51.1 184 840.7 139.3 172.3 510.7 178.7 11.6 0 1.51 1.45 2.58 2.54 22.5 27.6 31.0 185 759.4 97.8 181.4 519.4 129.5 11.3 0 1.58 1.53 2.85 2.83 35.8 36.3 36.3 186 863.6 163.2 164.4 514.6 165.6 19.2 0 1.45 1.40 2.46 2.42 68.6 83.9 94.3 187 781.4 79.5 193.7 535.5 156.3 8.1 0 1.60 1.55 2.94 2.92 10.5 11.4 12.0 188 834.8 128.7 198.4 549.6 140.2 14.4 0 1.59 1.54 2.96 2.94 48.2 52.5 54.9 189 879.5 128.1 248.7 569.6 165.4 16.0 0 1.76 1.71 2.45 2.43 39.6 40.0 40.0 190 834.2 143.4 198.4 536.9 138.2 13.7 0 1.57 1.52 2.89 2.85 38.8 47.7 53.6 191 868.0 111.5 210.2 542.7 199.1 13.7 0 1.58 1.53 3.18 3.13 18.7 22.9 25.7 192 859.6 137.1 175.3 516.0 190.4 14.8 0 1.45 1.39 2.54 2.49 31.7 38.9 43.6 193 807.8 97.9 191.2 528.8 170.6 8.7 0 1.57 1.52 2.87 2.85 14.9 15.0 15.0 194 887.0 211.8 211.8 547.7 88.5 37.3 0 1.39 1.34 1.40 1.33 203.5 259.5 298.0 195 759.1 78.6 178.5 508.7 159.1 9.0 0 1.55 1.50 2.76 2.74 13.7 13.9 13.9 196 780.9 159.3 186.7 520.2 77.6 21.2 0 1.26 1.22 2.11 2.06 129.2 142.2 148.8 197 747.3 80.0 254.3 557.7 90.7 14.1 0 1.76 1.71 2.84 2.82 34.7 35.3 35.3 198 847.9 174.8 185.7 572.1 29.9 69.0 0 1.30 1.26 2.02 1.99 1276.1 1402.4 1468.3 199 880.3 114.8 152.0 519.5 232.0 12.4 0 1.45 1.42 2.39 2.39 25.4 25.5 25.5 200 846.9 123.0 224.1 554.4 154.0 14.4 0 1.71 1.66 2.21 2.20 39.3 39.7 39.7 201 750.0 65.6 257.7 558.0 112.0 11.0 0 1.85 1.79 2.85 2.83 13.9 14.2 14.2 202 792.1 104.8 250.7 553.0 116.2 15.2 0 1.71 1.64 2.45 2.39 25.3 31.5 35.4 203 784.4 147.8 196.6 531.7 82.0 20.7 0 1.33 1.30 1.77 1.73 77.6 85.3 89.4 204 826.0 145.1 198.6 537.8 105.6 36.5 0 1.38 1.33 1.49 1.41 121.4 155.4 178.8 205 873.0 110.1 264.0 562.1 182.5 17.1 0 1.93 1.86 2.13 2.06 14.7 18.6 21.3 206 867.9 198.1 183.4 520.6 114.5 33.7 0 1.28 1.23 1.78 1.71 177.0 224.9 257.9 207 836.9 116.1 190.5 524.1 183.2 12.5 0 1.50 1.45 2.69 2.64 20.6 25.3 28.4 208 849.9 138.3 248.8 550.4 138.2 21.3 0 1.77 1.73 1.32 1.25 29.5 37.6 43.1 209 823.6 102.2 257.7 572.1 134.0 12.4 0 1.78 1.73 2.64 2.63 26.5 26.9 26.9 210 751.3 62.6 257.0 563.2 113.5 8.4 0 1.82 1.76 2.86 2.84 8.9 9.1 9.1 195 B5. Hydrological balance, corn yield and soil erosion by HRU — Continued. HRU Rain- Runoff Plant ET Perco- Subsuper Irriga- Corn Corn Corn Corn Soil Soil Soil fall evap. lation ficial tion yield yield yield yield erosion erosion erosion flow Mng Current Current Current Current Current Current Current Current INIFAP INIFAP Current Current Current Yr 1 1 1 1 1 1 1 100 1 100 1 50 100 Units mm mm mm mm mm mm mm t/ha t/ha t/ha t/ha t/ha t/ha t/ha 211 752.6 73.2 256.3 554.1 109.8 11.5 0 1.81 1.75 2.93 2.90 20.0 20.5 20.6 212 877.6 118.8 282.9 593.5 143.7 18.5 0 1.82 1.77 2.13 2.11 37.5 38.1 38.1 213 836.5 147.1 238.5 552.7 115.6 20.9 0 1.75 1.71 1.56 1.46 36.9 46.9 53.8 214 750.6 112.5 222.9 546.0 72.5 17.5 0 1.77 1.75 2.10 2.08 46.5 47.0 47.0 215 844.0 118.0 243.2 572.8 128.7 21.5 0 1.85 1.79 2.77 2.72 42.2 45.5 47.3 216 850.8 119.8 252.5 555.5 158.8 16.0 0 1.75 1.69 2.35 2.29 24.2 29.8 33.5 217 874.7 124.2 198.2 584.1 106.1 59.1 0 1.52 1.44 2.59 2.55 353.5 380.3 394.6 218 838.6 172.5 236.9 555.6 88.9 20.0 0 1.55 1.51 1.85 1.81 92.5 101.0 105.8 219 812.8 70.1 307.9 586.9 150.3 3.5 0 2.16 2.11 2.47 2.45 2.7 2.7 2.7 220 838.8 114.2 254.4 545.3 170.3 8.3 0 1.77 1.71 2.34 2.27 7.7 9.5 10.7 221 853.3 103.0 259.3 551.0 190.1 8.1 0 1.85 1.79 2.45 2.36 5.9 7.5 8.6 222 827.8 79.5 246.6 568.1 165.8 13.2 0 1.89 1.84 2.49 2.47 13.1 13.2 13.2 223 777.6 66.8 244.3 548.7 150.7 8.2 0 1.87 1.82 2.53 2.50 5.9 6.0 6.0 224 853.4 173.6 198.8 544.4 90.4 44.7 0 1.33 1.29 1.42 1.34 226.3 288.6 332.4 225 786.4 93.5 236.8 559.4 119.6 10.5 0 1.79 1.73 2.80 2.77 17.0 17.3 17.3 226 872.1 178.1 202.7 547.6 95.2 50.5 0 1.41 1.36 1.43 1.36 270.1 344.4 396.1 227 780.4 74.5 243.3 563.1 122.0 16.9 0 1.86 1.80 2.65 2.61 27.9 28.4 28.4 228 867.4 191.1 182.5 535.0 94.2 46.0 0 1.36 1.31 1.74 1.67 248.7 316.4 362.9 229 862.9 181.1 195.2 544.9 81.8 54.9 0 1.41 1.36 1.59 1.50 373.6 476.0 547.5 230 846.0 186.3 174.0 543.5 79.1 36.0 0 1.26 1.23 2.02 1.99 262.7 286.2 299.8 231 874.3 116.1 247.6 575.2 160.8 21.8 0 1.86 1.80 2.44 2.42 45.0 45.4 45.4 232 771.5 96.1 238.0 560.6 94.6 16.1 0 1.75 1.70 2.71 2.69 43.3 44.2 44.2 233 776.0 92.1 236.7 544.3 126.0 11.3 0 1.84 1.78 2.71 2.62 10.2 12.6 14.2 234 789.4 100.7 249.6 554.4 115.4 15.5 0 1.82 1.76 2.97 2.93 40.5 45.1 47.5 235 794.4 164.5 174.2 511.6 98.4 19.8 0 1.38 1.33 2.13 2.04 52.2 66.1 75.7 236 836.0 88.2 239.0 562.4 165.3 18.9 0 1.83 1.78 2.65 2.62 25.6 25.9 25.9 237 846.3 192.0 187.9 535.7 93.9 23.3 0 1.36 1.33 1.75 1.71 111.3 121.5 127.2 238 859.1 182.2 186.3 542.7 86.0 47.3 0 1.39 1.35 1.58 1.50 272.9 347.8 399.3 239 888.2 176.2 193.8 549.1 128.0 34.0 0 1.42 1.37 1.60 1.57 166.9 168.5 168.5 240 775.1 95.3 198.8 530.3 135.6 12.1 0 1.65 1.59 3.03 3.00 30.8 31.2 31.2 241 784.3 193.2 155.9 500.6 65.2 25.0 0 1.14 1.09 2.66 2.61 241.6 307.8 353.6 242 851.2 152.9 195.2 532.8 147.0 17.1 0 1.54 1.48 2.90 2.85 52.2 64.4 72.6 243 789.8 106.4 217.9 533.7 135.5 11.8 0 1.64 1.58 3.27 3.20 20.6 26.3 30.2 244 871.1 175.2 237.3 550.4 122.0 22.3 0 1.60 1.56 1.69 1.62 64.7 82.6 95.0 245 825.3 184.4 133.3 502.4 116.9 21.1 0 1.03 1.01 2.25 2.23 173.5 188.7 197.6 196 B5. Hydrological balance, corn yield and soil erosion by HRU — Continued. HRU Rain- Runoff Plant ET Perco- Subsuper Irriga- Corn Corn Corn Corn Soil Soil Soil fall evap. lation ficial tion yield yield yield yield erosion erosion erosion flow Mng Current Current Current Current Current Current Current Current INIFAP INIFAP Current Current Current Yr 1 1 1 1 1 1 1 100 1 100 1 50 100 Units mm mm mm mm mm mm mm t/ha t/ha t/ha t/ha t/ha t/ha t/ha 246 866.6 229.5 131.6 513.6 96.0 26.2 0 1.05 1.02 2.27 2.24 385.1 413.4 429.0 247 792.1 164.0 193.8 530.3 79.7 17.5 0 1.39 1.36 2.65 2.61 114.0 131.9 143.5 248 819.6 169.0 180.2 522.1 109.5 17.9 0 1.35 1.32 2.38 2.36 109.9 119.4 125.0 249 863.8 206.1 160.8 518.0 118.4 20.5 0 1.29 1.28 2.18 2.17 162.0 175.5 183.7 250 815.2 158.5 182.4 513.7 122.2 20.0 0 1.28 1.24 2.49 2.43 77.1 97.8 112.3 251 859.9 202.6 186.8 540.2 72.6 43.4 0 1.29 1.23 1.73 1.66 397.3 507.3 583.8 252 804.9 175.7 184.7 525.1 85.6 18.3 0 1.34 1.31 2.54 2.51 145.9 158.4 165.8 253 860.6 218.2 154.0 527.4 89.4 25.1 0 1.14 1.11 2.79 2.78 307.7 330.0 342.6 254 867.7 214.9 119.4 496.9 126.8 28.0 0 0.99 0.96 2.09 2.06 264.5 305.5 331.6 255 873.7 230.9 137.9 529.8 59.9 51.3 0 1.07 1.04 2.38 2.36 1585.0 1730.3 1810.6 197 198
B6. Land owners in the Texcoco district with soil erosion over 20 t/ha. Erosion Erosion No. (t/ha) Land Owner No. (t/ha) Land Owner 62 373.6 Ej. Juchitepec, Ozumba 174 203.5 Ej. Tlalmanalco, Amecameca 63 270.1 Pueblo Juchitepec, Juchitepec 175 129.2 Caserío Fábrica, Tlalmanalco 64 30.8 Ej. Ozumba, Atlautla 179 129.2 Ej. Tlalmanalco, Tlalmanalco 65 30.8 Hacienda Actopan, Ozumba 180 121.4 Santo Tomás Atzingo, Tlalmanalco 66 30.8 Pueblo Ozumba, Atlautla 182 129.2 Ej. San Juan Atzacoaloya, Tlalmanalco 75 25.6 Pueblo San Antonio Zayatzingo, Amecameca 183 129.2 Rancho El Socorro, Tlalmanalco 78 27.9 Pueblo San Pedro Nexapa, Amecameca 184 34.7 Pueblo San Juan Atzacoazoya, Tlalmanalco 83 40.5 Ej. Cuijingo, Juchitepec 185 34.7 Rancho Santa Cruz, Tlalmanalco 84 262.7 Pueblo Cuijingo, Juchitepec 186 34.7 Santo Tomás Atzingo, Tlalmanalco 85 40.5 Ampliación Cuijingo, Juchitepec 188 177 Pueblo Tlalmanalco, Tlalmanalco 86 40.5 J. Pérez, Juchitepec 189 177 Rancho La Mesa 43, Tlalmanalco 88 262.7 Hacienda Tlaxomulco, Juchitepec 190 34.7 Rancho Solís, Tlalmanalco 100 92.5 Ej. Juchitepec, Ayapango 191 34.7 Rancho San Luis, Tlalmanalco 101 46.5 Ej. San Juan Evangelista Tlamapa, Tenango del A. 192 34.7 41, Tlalmanalco 102 46.5 Rancho Tezoncal, Tenango del A. 193 34.7 12, Tlalmanalco 103 46.5 Ej. Santiago Tepopula, Ayapango 194 177 Terrenos Ayto. Tlalmanalco, Tlalmanalco 104 46.5 Rancho Tequimilco, Tenango del A. 195 34.7 Varios propietarios, Tlalmanalco 105 272.9 Ampliación Cuijingo, Juchitepec 196 34.7 Pedro Cardoso, Tlalmanalco 106 272.9 Ampliación Totolapan Morelos, Juchitepec 197 177 Rancho Tomatla, Tlalmanalco 107 52.2 Ej. San Nicolás del Vigía, Juchitepec 198 34.7 Cascos Hacienda Chiconquindios, Tlalmanalco 108 77.6 Zona fraccionada Juchitepec, Juchitepec 199 34.7 Tenango del Aire Agostaderos, Tlalmanalco 110 77.6 Ej. Ayotzingo, Chalco 200 177 Ej. Tenango del Aire (Temporal), Tlalmanalco 113 46.5 Ampliación San Mateo Huitzilzingo, Tenango del A. 201 34.7 Hacienda Chiconquindios, Tlalmanalco 114 46.5 Ampliación San Mateo Tepopulco, Tenango del A. 202 35.8 Ej. San Mateo Tezoquiapan, Chalco 115 46.5 Ampliación San Mateo Tepopulco, Tenango del A. 203 177 Rancho Los Tordos, Tlalmanalco 116 46.5 Ej. San Mateo Tepopulco, Tenango del A. 204 34.7 Agostadero Rocha, Tlalmanalco 117 77.6 Hacienda Atenpilla, Tenango del A. 205 34.7 Rancho El Chemo, Tlalmanalco 118 77.6 Santiago y San Mateo, Tenango del A. 207 39.3 Rancho El Soldado, Tlalmanalco 119 34.7 Terrenos Tenango del Aire, Tenango del A. 208 129.2 Ej. San Luis Tlalmimilolpan, Tlalmanalco 120 34.7 Hacienda Atempilla, Tenango del A. 209 129.2 Pueblo San Martín Tezoquiapan, Chalco 121 92.5 Costocan, Ayapango 210 129.2 Pueblo San Lorenzo Tlalmimilolpan, Tlalmanalco 122 34.7 Pueblo San Juan Costocan, Tenango del A. 211 129.2 Ej. Tlalmimilolpan, Tlalmanalco 123 34.7 San Juan Evangelista, Ayapango 212 129.2 Ej. San Mateo Tezoquiapan, Chalco 124 34.7 Ej. Costocan, Ayapango 213 129.2 Hacienda Miraflores, Chalco 125 34.7 Ej. Ayapango, Tenango del A. 218 125.1 Ej. Zavaleta, Chalco 126 34.7 Ej. San Juan, Ayapango 219 293.8 Ej. Santa María Huexoculco, Chalco 128 34.7 Ej. Zentlalpan, Ayapango 220 247.6 Pueblo Santa María Huexoculco, Chalco 129 34.7 Terrenos Comunales Poxtla y Pueblo, Ayapango 221 125.1 Ej. San Andrés Metla, Chalco 131 34.7 Ej. San Francisco Zentlalpan, Amecameca 224 35.8 Ej. Cocotitlán, Chalco 133 34.7 Alfredo Ramos, Amecameca 226 68.6 Ej. Candelaria Tlapala, Chalco 135 34.7 Pueblo Zentlalpan, Amecameca 228 68.6 Pueblo San Andrés Metla, Chalco 136 34.7 Terrenos Zentlalpan, Amecameca 229 38.8 Ej. Cocotitlán, Cocotitlan 137 34.7 Terrenos Ayto. Tlalmanalco, Amecameca 230 35.8 Ej. Temamatla, Tlalmanalco 138 34.7 Ej. San Mateo Tepopula, Tlalmanalco 231 77.6 Pueblo Temamatla, Tenango del A. 143 26.5 Santo Tomás Atzingo, Tlalmanalco 233 77.6 Ampliación Temamatla, Temamatla 144 121.4 Rancho Espinoza, Tlalmanalco 234 77.6 Hacienda Asunción del Monte y Ejido de Stgo., Temama 145 121.4 Pueblo San Antonio Rinconada, Amecameca 236 31.7 P. p. de Atlazalapa, Temamatla 146 121.4 Pueblo Santa Isabel Chalma, Amecameca 237 77.6 Ej. San Pablo Atlazalapa, Temamatla 148 121.4 Ej. Santiago Cuautenco, Amecameca 238 2483.4 P. p. de Cocotitlán, Chalco 149 121.4 Pueblo Santiago Cuautenco, Amecameca 246 31.7 Hacienda Axalco, Chalco 150 121.4 Rancho Tlalchichicuautla, Amecameca 247 31.7 San Pablo Atlazalpa, Chalco 151 27.9 Ej. San Pedro Nexapa, Atlautla 263 371.1 Lorenzo Chimalpa, Chalco 153 166.9 Ej. San Juan Tehuistitlán, Atlautla 267 371.1 Ejido, Chalco 154 34.7 Rancho San Luis, Tenango del A. 269 371.1 Ej. Tlahuac, Chalco 155 77.6 Rancho Aculco, Tenango del A. 271 371.1 Ej. Estación Xico, Chalco 156 397.3 Ej. Cuecuecuautitla, Tepetlixpa 273 371.1 Ej. Chalco, Chalco 157 385.1 Pueblo Tepetlixpa, Tepetlixpa 276 2483.4 Hacienda Atoyac, Chalco 158 241.6 Pueblo Cuecuecuautitla, Tepetlixpa 283 48.4 Casco Hacienda González, Chalco 159 241.6 Ej. San Miguel Netlan, Tepetlixpa 286 197.7 Dotación Complementaria San Martín Cuautlalpan,Chalc 160 145.9 Pueblo San Miguel Nepantla Placa, Tepetlixpa 287 48.4 Barrio Atlanote, Chalco 161 307.7 Rancho San Nicolás Metepec, Atlautla 288 97.4 Hacienda Venta Nueva, Ixtapaluca 162 77.1 Rancho Caltecoyan, Ozumba 289 35.5 Ej. San Marcos, Chalco 163 114 Rancho San José Chichintla, Ozumba 290 35.5 Pueblo Huixtoco, Chalco 164 1585 Pueblo San Andrés Tlacamac, Ecatzingo 291 356 Ampliación Chalco, Ixtapaluca 165 77.1 Pueblo Tlacotitlán, Atlautla 292 35.5 Agostadero Amp. San Marcos Huixtoco, Ixtapaluca 166 109.9 Pueblo Mamalhuazuca, Ozumba 293 295.5 Ej. Zoquiapan, Ixtapaluca 167 385.1 Pueblo Chimalhuacán, Atlautla 294 295.5 P. p. Buenavista , Ixtapaluca 168 166.9 Pueblo Tepeoculco y S. Miguel Atlautla, Ecatzingo 295 35.5 Ampliación San Marcos Huixtoco, Ixtapaluca 169 1276.1 Montes Comunales de Amecameca, Ayapango 298 192.1 Ej. Ixtapaluca, Ixtapaluca 170 203.5 Montes de Santiago Cuautenco, Amecameca 304 72.2 Ampliación Ayotla, Chalco 199
B6. Land owners in the Texcoco district with soil erosion over 20 t/ha — Continued. Erosion Erosion No. (t/ha) Land Owner No. (t/ha) Land Owner 305 81 Propiedad de Tlalpizahuac, Los Reyes 428 107.6 Terrenos comunales San Jerónimo Amanalco, Texcoco 307 81 Zona Urbanizada, Ixtapaluca 429 107.6 Pueblo Santa María Tecuanulco, Texcoco 308 81 Rancho Guadalupe, Ixtapaluca 430 99.1 Ej. San Juan Totolapan, Texcoco 309 81 Pueblo Ayotla, Ixtapaluca 431 99.1 Ej. Santo Tomás Apipilhuasco, Texcoco 310 81 Ampliación Ayotla, Ixtapaluca 433 34.1 Ej. Tequexquinahuac, Texcoco 311 81 Ampliación Tlapacoyac, Ixtapaluca 434 40.7 Ampliación Santa María Nativitas, Texcoco 313 173.6 Hacienda Acozac, Ixtapaluca 435 40.7 Ej. San Miguel, Texcoco 314 81 Magdalena Atipac, Ixtapaluca 436 170.3 Ej. San Dieguito, Texcoco 316 293.4 Ixtapaluca, Ixtapaluca 437 40.7 Ej. def. de Santa María Nativitas, Texcoco 320 293.4 Ampliación Ixtapaluca, Ixtapaluca 439 170.3 Pueblo San Dieguito, Texcoco 321 173.6 Ampliación Tlapacoyac, Ixtapaluca 442 170.3 Pueblo San Miguel Tlaixpan, Texcoco 323 243.7 Fracc. de Jesús María, Ixtapaluca 484 776.3 Pueblos, Ejidos y Terrenos comunales diversos sAtenc 324 118.7 Rancho Santa Bárbara, Ixtapaluca 494 170.3 Pueblo Purificación, Texcoco 325 118.7 Rancho La Cotera, Ixtapaluca 496 170.3 Pueblo San Bernardo, Texcoco 326 118.7 Ej. San Francisco Acuautla, Ixtapaluca 497 107.6 Ej. Tezontla, Texcoco 328 51.2 San Francisco Acuautla, Ixtapaluca 498 107.6 Pueblo de San Bernardo y San Andrés, Tepetlaoxtoc 329 192.1 Ej. San Francisco Acuautla, Ixtapaluca 499 107.6 Pueblo de Tezontla, Texcoco 330 243.7 Ampliación San Gregorio Cuatzingo, Ixtapaluca 500 214.7 Pueblo de Santo Tomás Apipilhuasco, Tepetlaoxtoc 331 118.7 Ej. Atipac, Chicoloapan 501 99.1 Terrenos fraccionados entre vecinos del P. AlpiTepet 332 356 Segunda Ampliación Ixtapaluca, Ixtapaluca 502 92.9 P. p., Tepetlaoxtoc 333 119.4 Rancho Venta de Córdoba, Chalco 503 94.5 Lote Sofia Blancas, Tepetlaoxtoc 334 295.5 Hacienda Venta de Córdoba Pastal, Ixtapaluca 504 119.4 Ej. Santo Tomás Apipilhuasco lote 3 Juan BlancaTepet 335 269.9 Ej. Santa María Huexoculco, Chalco 505 106.6 Hacienda Adela Sánchez V. Blancas, Tepetlaoxtoc 336 255.1 Rancho San Andrés, Ixtapaluca 506 106.6 Ej. Cuatzingo, Tepetlaoxtoc 337 277.7 Segunda Ampliación San Francisco Acuautla, Ixtapaluc 507 92.9 Rancho Buenavista , Tepetlaoxtoc 339 81 Ampliación Tlalpizahuac, Los Reyes 508 92.9 Hacienda Montecillo, Tepetlaoxtoc 340 81 Hacienda San Isidro, Los Reyes 509 151.2 Señorita Hermila Blancas, Tepetlaoxtoc 341 81 Ej. Los Reyes, Los Reyes 510 92.9 Ej. Cuatzingo, Tepetlaoxtoc 342 56.8 La Magdalena Atipac, Ixtapaluca 511 92.9 Pueblo de Cuatzingo, Tepetlaoxtoc 343 81 Pueblo La Magdalena Atipac, Chicoloapan 512 33.8 Rancho de Tlacaluca, Tepetlaoxtoc 344 380 Pueblo San Sebastián Chimalpa, Chimalhuacan 513 54.8 Ej. Santa Inés, Tepetlaoxtoc 345 81 Ej. San Sebastián Chimalpa, Chicoloapan 518 45.2 Basilio Rojas, Texcoco 346 56.8 Terreno Invadido por San Bernardino, Chicoloapan 520 45.2 Ma Para.., Texcoco 347 243.7 Hacienda San Francisco Acuautla, Ixtapaluca 532 144.4 Pueblo de Tepetlaoxtoc, Texcoco 348 118.7 Ej. La Magdalena, Ixtapaluca 533 111.8 San Francisco Joloapan, Chiautla 349 118.7 Ej. Atipac, Ixtapaluca 534 710.8 Pueblo San Pablo Joloapan, Chiautla 351 243.7 Ampliación Coatepec, Ixtapaluca 535 710.8 Ej. San Pablo Joloapan, Chiautla 360 72.2 Rancho El Olivar, Ixtapaluca 536 710.8 P. p., Tepetlaoxtoc 361 48.4 Barrio Santa María, Chalco 537 148.6 Ej. la Concepción Joloapan, Tepetlaoxtoc 362 293.8 Ej. San Martín Cuautlalpan, Chalco 538 144.4 Los Reyes Nopala, Tepetlaoxtoc 363 203.5 Monte Alto Hacienda de Ixtlahuaca, Ixtapaluca 539 32.1 Barrio la Trinidad, Tepetlaoxtoc 364 197.7 Ej. La Compañía, Chalco 540 148.6 Rancho de Cuegro, Tepetlaoxtoc 365 269.9 Hacienda Venta Nueva, Chalco 541 92.9 La Trinidad, Tepetlaoxtoc 366 202.5 Ej. Zoquiapan, Ixtapaluca 542 161.3 Ej. Cuatzingo, Tepetlaoxtoc 367 84.3 Hacienda de Zoquiapan, Ixtapaluca 543 161.3 Rancho Altila, Tepetlaoxtoc 368 69.8 Ampliación de Río Frío, Chalco 544 59.8 Ej. San Miguel Xoloc, Tepetlaoxtoc 369 159.8 Hacienda Río Frío, Ixtapaluca 545 72.7 Ej. Belén, Tepetlaoxtoc 370 206.1 Ej. Coatepec, Ixtapaluca 546 92.9 Ej. San Francisco Tlaltica, Tepetlaoxtoc 371 206.1 Pueblo Coatlinchán, Ixtapaluca 547 92.9 Hacienda San Vicente, Tepetlaoxtoc 372 206.1 Ampliación Coatepec, Ixtapaluca 558 710.8 Pueblo de Tepatitlán, Chiautla 373 116.2 Rancho Tecoac, Texcoco 561 710.8 Pueblo Ejido Tlaltecahuacan, Chiautla 374 160.7 Ej. def. de Coatlinchán, Texcoco 562 45.6 Pueblo San Lucas Huitzilucan, Chiautla 375 243.7 Pueblo Coatepec, Ixtapaluca 564 614 Ej. Huitzilucan, Chiautla 376 243.7 Ampliación San Vicente Chicoloapan, Ixtapaluca 566 148.6 Santiago Tepatitlán, Tepetlaoxtoc 378 184.2 Ampliación San Miguel Coatlinchán, Texcoco 567 148.6 Pueblo de Belén, Tepetlaoxtoc 379 184.2 Santiago Cuautlalpan, Chicoloapan 568 72.7 Ej. San Miguel Xoloc, Tepetlaoxtoc 388 45.4 Hacienda Coxtitlán, Chicoloapan 569 32.2 Pueblo Santa Bárbara, Tepetlaoxtoc 390 243.7 Ej. Chimalhuacán, Chicoloapan 570 72.7 P. p., Tepetlaoxtoc 391 34.2 Pueblo San Vicente Chicoloapan, Chicoloapan 571 32.2 Hacienda de Santa Bárbara, Tepetlaoxtoc 406 380 Barrio San Agustín Atlapulco, Chimalhuacan 572 92.9 Hacienda San Telmo , Tepetlaoxtoc 409 380 Pueblo Chimalhuacán, Hda. Aragón y Hda. ChapingChima 573 92.9 Ej. Santa Barbara, Tepetlaoxtoc 413 74.7 Hacienda Chapingo, Texcoco 575 32.2 Ej. Oxtotipicac, Tepetlaoxtoc 414 74.7 Ampliación Santiago Suchinango, Texcoco 576 195.2 Hacienda de Buenavista, Tepetlaoxtoc 415 85.2 Ampliación San Miguel Tlaixpan, Texcoco 577 614 Ej. Ocopulco, Chiautla 418 111.2 Hacienda Guadalupe, Ixtapaluca 578 195.2 P. p., Tepetlaoxtoc 422 90.3 Ej. San Pablo Ixayoc, Texcoco 579 195.2 Las huertas, Tepetlaoxtoc 423 67.1 Pueblo San Pablo Ixayoc, Texcoco 580 123.5 El moral, Tepetlaoxtoc 424 90.3 Ej. Santa Catarina del Monte, Texcoco 582 56.7 Ampliación Totolcingo, Ecatepec 425 99.1 Pueblo Santa Catarina del Monte, Texcoco 584 56.7 Ej. def. Tepexpan, Ecatepec 426 107.6 Tecuanulco, Texcoco 585 56.7 Ej. San Miguel Totolcingo, Ecatepec 427 107.6 Ejido y pueblo San Jerónimo Amanalco, Texcoco 586 56.7 Pueblo de San Miguel Totolcingo, Ecatepec 200
APPENDIX C: BEST MANAGEMENT PRACTICES EVALUATION C1. BMP evaluation for conventional till (CT). BMP BMP Soil HRU Rain- EI ET Plant Perco- Subsuper Runoff CN C Soil Corn ficial fall evap. lation flow erosion yield mm mm mm mm mm mm t/ha t/ha CT, straight row 9 VP-D 31 699.2 294.8 516.8 199.1 59.2 14.0 107.9 86.7 0.19 49.20 1.66 CT, straight row 9 HH-LP 47 691.7 264.8 529.3 231.2 55.0 11.9 94.4 83.6 0.23 40.28 1.72 CT, straight row 9 HH-L 77 705.9 267.8 513.0 169.9 42.5 23.0 125.5 89.2 0.19 151.43 1.28 CT, straight row 9 HC-DP 111 731.6 311.9 536.5 224.1 76.6 11.8 105.4 86.6 0.29 59.01 1.66 CT, straight row 9 RE-L 134 713.3 266.9 529.7 197.1 60.7 18.4 100.2 87.8 0.24 106.53 1.46 CT, straight row 9 HH-D 135 757.2 350.0 526.4 191.5 66.0 18.4 144.1 89.5 0.27 149.88 1.32 CT, straight row 9 BE-D 155 733.5 297.1 516.9 184.6 57.8 18.3 137.8 90.4 0.19 120.55 1.33 CT, straight row 9 HH-DP 161 727.1 271.7 550.4 254.5 56.8 11.2 105.4 87.4 0.23 44.72 1.75 CT, straight row 9 TO-L 196 780.9 318.4 520.2 186.7 77.6 21.2 159.3 90.1 0.25 183.40 1.26 CT, straight row 9 RE-LP 197 747.3 308.8 557.7 254.3 90.7 14.1 80.0 85.5 0.26 67.37 1.76 CT, straight row 9 RD-G 211 752.6 283.6 554.1 256.3 109.8 11.5 73.2 84.7 0.27 32.17 1.81 CT, straight row 9 RE-G 214 750.6 278.1 545.9 222.9 72.5 17.5 112.5 88.2 0.22 70.93 1.77 CT, straight row 9 TH-P 250 815.2 353.5 513.7 182.4 122.2 20.0 158.5 88.6 0.23 104.19 1.28 CT, Contouring, beef manure 2 VP-D 31 699.2 294.8 523.4 219.3 44.1 13.1 117.2 87.1 0.18 33.96 4.43 CT, Contouring, beef manure 2 HH-LP 47 691.7 264.8 537.3 245.4 48.6 11.8 92.9 82.8 0.19 30.15 4.59 CT, Contouring, beef manure 2 HH-L 77 705.9 267.8 520.4 190.0 28.1 21.5 134.3 89.4 0.18 110.48 3.96 CT, Contouring, beef manure 2 HC-DP 111 731.6 311.9 540.3 232.7 60.8 10.9 118.7 87.6 0.26 43.82 4.02 CT, Contouring, beef manure 2 RE-L 134 713.3 266.9 532.9 204.4 52.4 17.5 106.8 88.3 0.22 78.92 4.17 CT, Contouring, beef manure 2 HH-D 135 757.2 350.0 532.1 204.6 55.6 17.6 149.9 89.6 0.24 111.13 3.95 CT, Contouring, beef manure 2 BE-D 155 733.5 297.1 523.1 199.6 43.8 17.2 146.7 90.7 0.19 85.96 4.00 CT, Contouring, beef manure 2 HH-DP 161 727.1 271.7 555.4 263.8 53.3 11.1 104.2 87.2 0.21 31.09 4.70 CT, Contouring, beef manure 2 TO-L 196 780.9 318.4 517.8 189.5 55.6 18.7 186.2 91.3 0.23 137.40 3.30 CT, Contouring, beef manure 2 RE-LP 197 747.3 308.8 555.6 256.0 58.4 11.7 116.6 88.5 0.24 46.24 4.66 CT, Contouring, beef manure 2 RD-G 211 752.6 283.6 555.6 262.2 88.3 10.4 94.8 86.8 0.25 22.81 4.76 CT, Contouring, beef manure 2 RE-G 214 750.6 278.1 550.4 237.5 57.9 16.5 123.5 88.8 0.22 49.09 4.46 CT, Contouring, beef manure 2 TH-P 250 815.2 353.5 517.8 196.8 89.9 17.6 189.2 90.0 0.21 84.04 2.49 CT, Contouring, terrace 3 VP-D 31 699.2 294.8 529.8 221.6 64.1 14.9 89.0 84.6 0.18 14.92 4.45 CT, Contouring, terrace 3 HH-LP 47 691.7 264.8 539.5 245.7 69.1 13.5 68.6 80.1 0.20 12.61 4.55 CT, Contouring, terrace 3 HH-L 77 705.9 267.8 528.4 191.9 46.1 24.9 104.7 87.5 0.18 46.83 3.97 CT, Contouring, terrace 3 HC-DP 111 731.6 311.9 543.7 233.3 90.1 13.0 83.9 84.8 0.26 18.89 4.01 CT, Contouring, terrace 3 RE-L 134 713.3 266.9 535.9 204.5 73.0 20.3 80.4 86.2 0.22 33.25 4.18 CT, Contouring, terrace 3 HH-D 135 757.2 350.0 536.4 205.1 78.4 20.2 120.2 87.9 0.25 51.57 3.89 CT, Contouring, terrace 3 BE-D 155 733.5 297.1 529.1 200.4 69.0 20.2 112.4 88.8 0.19 38.01 4.00 CT, Contouring, terrace 3 HH-DP 161 727.1 271.7 557.9 264.0 79.6 13.1 73.4 84.4 0.21 13.01 4.70 CT, Contouring, terrace 3 TO-L 196 780.9 318.4 524.2 190.1 86.9 22.6 144.6 89.4 0.23 61.10 3.25 CT, Contouring, terrace 3 RE-LP 197 747.3 308.8 558.3 256.1 87.3 13.9 82.7 85.8 0.24 19.69 4.66 CT, Contouring, terrace 3 RD-G 211 752.6 283.6 557.5 262.2 111.3 11.7 68.6 84.3 0.25 15.96 4.74 CT, Contouring, terrace 3 RE-G 214 750.6 278.1 554.7 238.7 77.4 18.5 97.8 87.0 0.22 22.31 4.48 201 CT, Contouring, terrace 3 TH-P 250 815.2 353.5 522.8 196.6 123.1 20.6 148.0 88.1 0.21 38.15 2.37 C1. BMP evaluation for convencional till (CT) — Continued. BMP BMP Soil HRU Rain- EI ET Plant Perco- Subsuper Runoff CN C Soil Corn fall evap. lation ficial flow erosion yield mm mm mm mm mm mm t/ha t/ha CT, Furrow dike: 1.25m 4 VP-D 31 699.2 294.8 524.3 209.9 60.3 14.4 98.9 86.6 0.18 30.44 1.85 CT, Furrow dike: 1.25m 4 HH-LP 47 691.7 264.8 534.6 237.3 69.1 13.3 73.7 82.4 0.24 31.72 1.87 CT, Furrow dike: 1.25m 4 HH-L 77 705.9 267.8 518.3 178.1 39.7 23.0 123.1 89.1 0.19 105.69 1.49 CT, Furrow dike: 1.25m 4 HC-DP 111 731.6 311.9 540.4 228.3 85.2 12.6 92.2 87.1 0.30 43.30 1.81 CT, Furrow dike: 1.25m 4 RE-L 134 713.3 266.9 532.0 199.0 68.8 19.5 88.9 87.1 0.24 74.84 1.69 CT, Furrow dike: 1.25m 4 HH-D 135 757.2 350.0 531.2 196.0 75.8 19.7 128.1 89.0 0.27 109.54 1.53 CT, Furrow dike: 1.25m 4 BE-D 155 733.5 297.1 523.2 189.9 60.8 18.9 127.9 90.2 0.19 80.02 1.53 CT, Furrow dike: 1.25m 4 HH-DP 161 727.1 271.7 554.9 258.4 76.7 12.8 79.5 86.5 0.23 28.32 1.95 CT, Furrow dike: 1.25m 4 TO-L 196 780.9 318.4 518.6 184.0 72.6 20.6 166.5 90.8 0.25 138.61 1.39 CT, Furrow dike: 1.25m 4 RE-LP 197 747.3 308.8 556.1 251.2 80.0 13.2 92.7 87.7 0.27 43.66 1.97 CT, Furrow dike: 1.25m 4 RD-G 211 752.6 283.6 554.8 256.2 119.7 12.1 62.3 85.4 0.28 20.55 2.03 CT, Furrow dike: 1.25m 4 RE-G 214 750.6 278.1 551.9 230.6 78.8 18.4 99.4 87.8 0.23 43.46 1.98 CT, Furrow dike: 1.25m 4 TH-P 250 815.2 353.5 516.6 187.0 117.2 19.8 160.9 89.4 0.23 84.31 1.32 CT, Manure poultry 8 VP-D 31 699.2 294.8 523.1 219.3 43.8 13.0 117.8 87.2 0.18 33.29 4.43 CT, Manure poultry 8 HH-LP 47 691.7 264.8 537.7 245.4 51.7 12.0 89.2 82.6 0.19 28.00 4.59 CT, Manure poultry 8 HH-L 77 705.9 267.8 519.9 190.0 28.1 21.4 134.9 89.5 0.18 108.63 3.96 CT, Manure poultry 8 HC-DP 111 731.6 311.9 540.2 232.7 60.7 10.9 118.9 87.7 0.26 42.78 4.02 CT, Manure poultry 8 RE-L 134 713.3 266.9 533.3 204.4 54.4 17.8 104.1 88.1 0.22 73.91 4.17 CT, Manure poultry 8 HH-D 135 757.2 350.0 532.6 204.6 57.1 17.8 147.7 89.5 0.24 106.09 3.97 CT, Manure poultry 8 BE-D 155 733.5 297.1 522.7 199.6 43.7 17.2 147.1 90.7 0.19 84.16 4.00 CT, Manure poultry 8 HH-DP 161 727.1 271.7 555.3 263.8 53.3 11.1 104.3 87.3 0.21 30.33 4.70 CT, Manure poultry 8 TO-L 196 780.9 318.4 517.6 189.5 55.6 18.6 186.5 91.3 0.23 134.60 3.30 CT, Manure poultry 8 RE-LP 197 747.3 308.8 555.5 256.0 58.3 11.7 116.8 88.6 0.24 45.14 4.66 CT, Manure poultry 8 RD-G 211 752.6 283.6 556.0 262.2 90.6 10.5 91.9 86.7 0.25 21.12 4.77 CT, Manure poultry 8 RE-G 214 750.6 278.1 551.0 237.7 58.7 16.6 122.0 88.8 0.22 47.17 4.48 CT, Manure poultry 8 TH-P 250 815.2 353.5 518.8 196.8 93.5 17.9 184.3 89.9 0.21 79.26 2.49 202 C2. BMP evaluation for minimum till (MT). BMP BMP Soil HRU Rain- EI ET Plant Perco- Subsuper Runoff CN C Soil Corn fall evap. lation ficial flow erosion yield mm mm mm mm mm mm t/ha t/ha MT, beef manure 11 VP-D 31 699.2 294.8 514.2 212.2 20.9 10.8 151.4 89.5 0.18 45.55 2.22 MT, beef manure 11 HH-LP 47 691.7 264.8 533.7 241.0 41.3 11.0 104.6 84.2 0.23 37.30 2.24 MT, beef manure 11 HH-L 77 705.9 267.8 510.2 182.7 12.1 18.1 164.0 91.0 0.18 140.21 1.88 MT, beef manure 11 HC-DP 111 731.6 311.9 536.9 230.0 43.7 9.6 140.6 88.6 0.29 54.64 2.16 MT, beef manure 11 RE-L 134 713.3 266.9 529.7 202.4 34.0 15.0 131.2 89.7 0.24 98.64 2.05 MT, beef manure 11 HH-D 135 757.2 350.0 526.5 200.1 36.0 15.2 177.9 90.6 0.26 138.78 1.85 MT, beef manure 11 BE-D 155 733.5 297.1 514.1 194.6 19.9 14.0 183.0 92.1 0.19 111.62 1.92 MT, beef manure 11 HH-DP 161 727.1 271.7 551.8 261.3 29.5 9.3 133.6 88.7 0.23 41.41 2.34 MT, beef manure 11 TO-L 196 780.9 318.4 511.4 186.0 32.7 15.6 218.8 92.1 0.25 169.81 1.69 MT, beef manure 11 RE-LP 197 747.3 308.8 551.7 253.5 32.3 9.6 149.7 90.0 0.26 62.38 2.34 MT, beef manure 11 RD-G 211 752.6 283.6 553.6 260.1 66.2 9.1 120.2 88.7 0.27 29.79 2.42 MT, beef manure 11 RE-G 214 750.6 278.1 545.0 233.0 31.2 13.8 158.5 90.7 0.22 65.67 2.35 MT, beef manure 11 TH-P 250 815.2 353.5 514.4 191.4 74.1 16.0 210.0 91.0 0.23 96.47 1.54 MT, Contouring 12 VP-D 31 699.2 294.8 517.0 207.5 31.8 11.7 137.1 88.6 0.18 42.09 1.84 MT, Contouring 12 HH-LP 47 691.7 264.8 532.0 236.3 49.3 11.6 97.7 83.7 0.24 36.15 1.87 MT, Contouring 12 HH-L 77 705.9 267.8 511.1 176.5 19.2 19.2 154.8 90.6 0.19 135.39 1.48 MT, Contouring 12 HC-DP 111 731.6 311.9 537.7 227.5 62.2 10.9 119.9 87.6 0.29 48.47 1.82 MT, Contouring 12 RE-L 134 713.3 266.9 529.0 198.7 42.1 16.0 122.5 89.2 0.24 94.68 1.68 MT, Contouring 12 HH-D 135 757.2 350.0 525.2 194.4 43.4 15.9 170.9 90.3 0.27 138.19 1.51 MT, Contouring 12 BE-D 155 733.5 297.1 515.3 188.2 31.3 15.2 168.8 91.5 0.19 105.68 1.52 MT, Contouring 12 HH-DP 161 727.1 271.7 552.0 258.0 46.6 10.5 114.8 87.7 0.23 36.26 1.94 MT, Contouring 12 TO-L 196 780.9 318.4 513.7 182.9 46.7 17.3 200.6 91.5 0.25 161.40 1.39 MT, Contouring 12 RE-LP 197 747.3 308.8 552.9 250.8 50.3 10.9 128.5 89.0 0.27 55.12 1.95 MT, Contouring 12 RD-G 211 752.6 283.6 552.2 255.9 76.9 9.7 110.2 88.0 0.28 28.37 2.02 MT, Contouring 12 RE-G 214 750.6 278.1 545.1 228.2 38.7 14.4 150.2 90.3 0.23 63.55 1.96 MT, Contouring 12 TH-P 250 815.2 353.5 513.8 186.5 85.6 16.9 198.2 90.5 0.23 94.04 1.33 MT, Contouring, mulching 13 VP-D 31 699.2 294.8 522.6 209.6 48.6 13.3 113.2 86.8 0.18 28.17 1.85 MT, Contouring, mulching 13 HH-LP 47 691.7 264.8 534.0 236.6 66.6 13.1 76.9 81.6 0.24 22.78 1.86 MT, Contouring, mulching 13 HH-L 77 705.9 267.8 518.1 178.4 32.9 21.8 131.4 89.3 0.19 92.82 1.49 MT, Contouring, mulching 13 HC-DP 111 731.6 311.9 540.1 227.9 85.4 12.6 92.6 85.8 0.29 29.82 1.80 MT, Contouring, mulching 13 RE-L 134 713.3 266.9 532.0 199.0 61.9 18.6 97.0 87.5 0.24 58.07 1.69 MT, Contouring, mulching 13 HH-D 135 757.2 350.0 529.3 194.7 64.7 18.4 142.8 89.1 0.27 90.83 1.51 MT, Contouring, mulching 13 BE-D 155 733.5 297.1 521.6 189.5 50.8 17.7 140.7 90.3 0.19 69.75 1.53 MT, Contouring, mulching 13 HH-DP 161 727.1 271.7 554.2 258.4 68.0 12.1 89.5 85.9 0.23 22.07 1.95 MT, Contouring, mulching 13 TO-L 196 780.9 318.4 518.9 183.0 70.6 20.3 168.5 90.4 0.25 108.47 1.37 MT, Contouring, mulching 13 RE-LP 197 747.3 308.8 555.3 251.1 74.1 12.8 100.0 87.2 0.27 33.90 1.97 MT, Contouring, mulching 13 RD-G 211 752.6 283.6 553.9 256.0 100.4 11.0 83.6 86.0 0.28 17.22 2.03 MT, Contouring, mulching 13 RE-G 214 750.6 278.1 549.6 229.7 59.8 16.5 122.4 88.8 0.23 41.42 1.97
MT, Contouring, mulching 13 TH-P 250 815.2 353.5 515.4 183.1 117.3 19.7 162.0 89.2 0.24 61.58 1.26 203 C2. BMP evaluation for minimum till (MT) — Continued. BMP BMP Soil HRU Rain- EI ET Plant Perco- Subsuper Runoff CN C Soil Corn fall evap. lation ficial flow erosion yield mm mm mm mm mm mm t/ha t/ha MT, Mulching 14 VP-D 31 699.2 294.8 514.5 206.5 26.8 11.3 145.0 89.1 0.18 36.49 1.83 MT, Mulching 14 HH-LP 47 691.7 264.8 532.9 236.5 55.1 12.1 90.6 83.2 0.24 27.15 1.87 MT, Mulching 14 HH-L 77 705.9 267.8 508.8 175.6 15.8 18.5 161.1 90.9 0.19 116.48 1.48 MT, Mulching 14 HC-DP 111 731.6 311.9 536.6 227.3 53.9 10.3 130.0 88.0 0.29 42.22 1.82 MT, Mulching 14 RE-L 134 713.3 266.9 530.1 198.8 48.9 16.9 113.8 88.7 0.24 69.45 1.69 MT, Mulching 14 HH-D 135 757.2 350.0 526.6 194.5 50.1 16.7 161.9 90.1 0.27 103.46 1.51 MT, Mulching 14 BE-D 155 733.5 297.1 513.0 187.6 26.0 14.5 177.3 91.9 0.19 89.19 1.51 MT, Mulching 14 HH-DP 161 727.1 271.7 551.0 257.7 38.7 9.9 124.4 88.2 0.23 31.80 1.94 MT, Mulching 14 TO-L 196 780.9 318.4 511.8 182.6 39.9 16.4 210.3 91.8 0.25 136.16 1.39 MT, Mulching 14 RE-LP 197 747.3 308.8 551.6 250.4 41.8 10.3 139.2 89.4 0.27 48.03 1.95 MT, Mulching 14 RD-G 211 752.6 283.6 552.9 255.9 85.1 10.1 100.7 87.5 0.28 20.95 2.02 MT, Mulching 14 RE-G 214 750.6 278.1 546.6 228.8 45.8 15.2 140.7 89.9 0.23 48.14 1.96 MT, Mulching 14 TH-P 250 815.2 353.5 514.4 185.5 95.6 17.8 186.6 90.2 0.23 70.96 1.31 MT, mulching, beef manure 15 VP-D 31 699.2 294.8 522.2 215.7 36.1 12.3 127.0 87.8 0.18 31.39 2.25 MT, mulching, beef manure 15 HH-LP 47 691.7 264.8 536.7 242.3 59.1 12.6 82.2 82.1 0.23 23.74 2.22 MT, mulching, beef manure 15 HH-L 77 705.9 267.8 518.2 186.0 21.8 20.2 144.1 89.9 0.18 101.27 1.90 MT, mulching, beef manure 15 HC-DP 111 731.6 311.9 540.5 230.9 69.5 11.5 109.2 86.9 0.29 34.34 2.15 MT, mulching, beef manure 15 RE-L 134 713.3 266.9 533.2 202.9 54.3 17.7 104.4 88.1 0.24 61.91 2.07 MT, mulching, beef manure 15 HH-D 135 757.2 350.0 531.3 201.0 56.0 17.6 150.5 89.5 0.26 93.76 1.84 MT, mulching, beef manure 15 BE-D 155 733.5 297.1 521.5 196.7 35.6 16.1 157.6 91.0 0.19 77.67 1.93 MT, mulching, beef manure 15 HH-DP 161 727.1 271.7 554.9 262.0 53.0 11.1 105.0 87.1 0.23 25.83 2.35 MT, mulching, beef manure 15 TO-L 196 780.9 318.4 517.7 187.3 53.2 18.3 189.3 91.1 0.25 119.44 1.68 MT, mulching, beef manure 15 RE-LP 197 747.3 308.8 555.7 254.4 57.6 11.6 117.6 88.4 0.26 39.57 2.36 MT, mulching, beef manure 15 RD-G 211 752.6 283.6 555.8 260.4 91.3 10.6 91.4 86.7 0.27 18.49 2.43 MT, mulching, beef manure 15 RE-G 214 750.6 278.1 550.4 235.1 51.6 15.9 130.4 89.3 0.22 43.74 2.36 MT, mulching, beef manure 15 TH-P 250 815.2 353.5 517.9 190.0 104.6 18.7 173.2 89.7 0.23 64.40 1.47 204 C3. BMP evaluation for non till (NT). BMP BMP Soil HRU Rain- EI ET Plant Perco- Subsuper Runoff CN C Soil Corn fall evap. lation ficial flow erosion yield mm mm mm mm mm mm t/ha t/ha NT, contouring 17 HC-DP 111 731.6 311.9 539.3 226.8 80.4 12.2 98.8 86.1 0.20 27.94 1.79 NT, contouring 17 RE-L 134 713.3 266.9 530.1 196.9 53.3 17.4 108.7 88.1 0.19 69.77 1.64 NT, contouring 17 HH-D 135 757.2 350.0 527.3 192.7 57.9 17.5 152.5 89.5 0.22 103.27 1.48 NT, contouring 17 BE-D 155 733.5 297.1 516.7 186.0 38.0 16.0 160.0 91.2 0.18 98.03 1.48 NT, contouring 17 HH-DP 161 727.1 271.7 553.2 256.7 61.8 11.6 97.3 86.4 0.19 25.70 1.91 NT, contouring 17 TO-L 196 780.9 318.4 515.7 182.0 54.6 18.3 189.8 91.0 0.20 124.49 1.36 NT, contouring 17 RE-LP 197 747.3 308.8 553.9 249.5 67.1 12.2 109.0 87.7 0.21 38.94 1.92 NT, contouring 17 RD-G 211 752.6 283.6 553.1 254.0 105.8 11.3 78.6 85.6 0.21 15.21 1.99 NT, contouring 17 RE-G 214 750.6 278.1 547.7 226.9 55.8 16.1 128.8 89.2 0.20 49.62 1.91 NT, contouring 17 TH-P 250 815.2 353.5 517.3 184.8 113.5 19.4 164.2 89.2 0.16 53.12 1.31 NT, contouring, terraces 18 VP-D 31 699.2 294.8 523.5 207.2 57.4 14.1 102.9 86.0 0.18 20.56 1.80 NT, contouring, terraces 18 HH-LP 47 691.7 264.8 534.7 234.9 79.6 14.1 62.3 79.8 0.16 10.35 1.83 NT, contouring, terraces 18 HH-L 77 705.9 267.8 517.1 175.3 33.5 21.8 131.9 89.3 0.18 66.92 1.45 NT, contouring, terraces 18 HC-DP 111 731.6 311.9 540.7 226.9 99.6 13.6 76.8 84.1 0.20 14.64 1.78 NT, contouring, terraces 18 RE-L 134 713.3 266.9 532.2 197.2 70.0 19.6 87.5 86.6 0.19 34.20 1.65 NT, contouring, terraces 18 HH-D 135 757.2 350.0 530.4 193.0 77.3 19.7 127.6 88.2 0.22 54.72 1.49 NT, contouring, terraces 18 BE-D 155 733.5 297.1 521.9 187.2 56.2 18.3 134.4 89.9 0.18 51.62 1.50 NT, contouring, terraces 18 HH-DP 161 727.1 271.7 554.6 256.9 80.3 13.0 75.9 84.4 0.19 13.24 1.91 NT, contouring, terraces 18 TO-L 196 780.9 318.4 520.2 182.4 76.9 21.1 160.2 89.8 0.20 64.91 1.36 NT, contouring, terraces 18 RE-LP 197 747.3 308.8 555.5 249.7 87.7 13.8 85.1 85.8 0.21 20.33 1.93 NT, contouring, terraces 18 RD-G 211 752.6 283.6 553.7 253.9 122.9 12.2 59.9 83.5 0.21 8.29 1.99 NT, contouring, terraces 18 RE-G 214 750.6 278.1 550.8 228.0 74.0 17.9 105.8 87.7 0.20 25.85 1.92 NT, contouring, terraces 18 TH-P 250 815.2 353.5 517.8 181.9 139.1 21.6 135.9 87.8 0.16 28.20 1.26 NT, contouring, mulching 19 VP-D 31 699.2 294.8 524.5 207.6 62.4 14.5 96.5 85.4 0.18 23.56 1.80 NT, contouring, mulching 19 HH-LP 47 691.7 264.8 534.8 234.8 83.8 14.4 57.6 79.1 0.16 11.81 1.83 NT, contouring, mulching 19 HH-L 77 705.9 267.8 518.6 175.7 38.1 22.6 124.8 88.9 0.18 85.80 1.45 NT, contouring, mulching 19 HC-DP 111 731.6 311.9 541.0 226.8 105.2 14.0 70.5 83.4 0.20 15.39 1.77 NT, contouring, mulching 19 RE-L 134 713.3 266.9 532.8 197.3 75.1 20.3 81.2 86.0 0.19 39.31 1.65 NT, contouring, mulching 19 HH-D 135 757.2 350.0 531.1 193.0 83.4 20.4 120.0 87.8 0.22 61.89 1.48 NT, contouring, mulching 19 BE-D 155 733.5 297.1 523.0 187.4 62.2 19.0 126.6 89.5 0.18 60.04 1.50 NT, contouring, mulching 19 HH-DP 161 727.1 271.7 555.0 257.0 85.8 13.4 69.6 83.7 0.19 14.18 1.91 NT, contouring, mulching 19 TO-L 196 780.9 318.4 521.2 182.3 84.4 22.0 150.7 89.4 0.20 77.18 1.36 NT, contouring, mulching 19 RE-LP 197 747.3 308.8 555.8 249.8 93.8 14.2 78.2 85.2 0.21 21.32 1.93 NT, contouring, mulching 19 RD-G 211 752.6 283.6 553.9 253.9 127.8 12.5 54.6 82.7 0.21 8.25 1.99 NT, contouring, mulching 19 RE-G 214 750.6 278.1 551.5 228.2 79.6 18.4 98.9 87.2 0.20 29.73 1.92 NT, contouring, mulching 19 TH-P 250 815.2 353.5 517.9 181.0 147.2 22.3 127.0 87.3 0.16 32.32 1.24 205 206
REFERENCES
Adame-M., S., Oropeza-M., J. L., Martinez-M., M. R., Santoyo-C., V., and Ramirez-G., M. E., 2000. Economic evaluation of the soil conservation project in the Texcoco river basin. Agrociencia, 34 (4): 509-521.
Alvarado-B., A., 1975. Influencia de algunos factores ambientales en la respuesta de rendimiento de grano de maíz de temporal a diferentes niveles de grano de maíz de temporal a diferentes niveles de nitrógeno, fósforo y densidad de población, en la zona oriental del Valle de Mexico. Tesis, Doctorado en Suelos. Colegio de Postgraduados, México.
Antezana-T., O. J., 1978. Influencia de la captación in situ de agua de lluvia, cobertura de rastrojo y fechas de siembra en la producción de maíz de temporal. Tesis, Maestría en Suelos. Colegio de Postgraduados, México.
Arnold, J. G. and Williams, J. R., 1989. Stochastic generation of internal storm structure. Transactions of the ASAE (American Society of Agricultural Engineers), 32 (1): 161-166.
Badan, A., 2003. The effects of El Niño in Mexico: A survey. Geofísica Internacional, 42 (3): 567-57.
Baird, A. J., 1999. Modelling. In: Baird,A.J. and Wilby,R.L. (Editors), Eco-hydrology: plants and water en terrestrial and aquatic environments. Routledge physical environment series. London: Routledge, 301-345 pp.
Barbier, B. and Bergeron, G., 1999. Impact of policy interventions on land management in Honduras: results of a bioeconomic model. Agricultural Systems, 60 (1): 1-16.
Baumer, O. W., 1990. Prediction of soil hydraulic parameters. In: WEEP Data Files Indiana. SCS National Soil Survey Laboratory. Lincoln, NE.
Beasley, D. B., Huggins, L. F., and Monke, E. J., 1980. ANSWERS: a model for watershed planning. Transactions of the American Society of Agricultural Engineers, 23: 938-944.
Becerra-M., A., 6-19-1998. Soil conservation and sustainable development, utopia or possibility in Mexico? TERRA, 16 (2): 181-187.
Becker, H., 2003. WEPP: Spiling the secrets of water erosion. Agricultural Research, 45 (4): 4-9.
Bhardwaj, S. P., Khybri, M. L., Ram, S., and Prasad, S. N., 1985. Crop geometry - a nonmonetary input for reducing erosion in corn on four percent slope. El-Swaify, S. A., 207
Moldenhauer, W. C., and Lo, A. (eds). Soil erosion and conservation. Soil Conservation Society of America. Ankeny, IA. 644-648 pp.
Bhuyan, S. J., Kalita, P. K., Janssen, K. A., and Barnes, P. L., 2002. Soil loss predictions with three erosion simulation models. Environmental Modelling & Software, 17 (2): 137- 146.
Bonsu, M., 1985. Organic residues for less erosion and more grain in Ghana. El-Swaify, S. A., Moldenhauer, W. C., and Lo, A. (eds). Soil erosion and conservation. Soil Conservation Society of America. Ankeny, IA. 615-620 pp.
Bower, B., 1993. Study erodes image of pre-Columbian farmers. Science News, 143 (10): 149.
Brown, R. A., Rosenberg, N. J., Hays, C. J., Easterling, W. E., and Mearns, L. O., 2000. Potential production and environmental effects of switchgrass and traditional crops under current and greenhouse-altered climate in the central United States, a simulation study. Agriculture, Ecosystems and Environment, 78 (1): 31-47.
Brown, R. A. and Rosenberg, N. J., 1999. Climate change impacts on the potential productivity of corn and winter wheat in their primary United States growing regions. Climatic Change, 41 (1): 73-107.
Burrough, P. A. and McDonnell, R. A., 1998. Principles of Geographical Information Systems (Spatial Information Systems). 2 edition. Oxford University Press, New York.
Campos de Jesús, S., 1982. Efecto de la captación de lluvia, estiércol y rastrojo sobre la humedad del suelo y producción de la asociación maíz-fríjol. Tesis, Maestría en Suelos. Colegio de Postgraduados, México.
Castelan-O., O. A., Fawcett, R. H., Arriaga-J., C. M., and Smith, A. J., 2000. Evaluation of the CERES-Maize model in simulating campesino farmer yields in the highlands of central Mexico. Experimental Agriculture, 36 (4): 479-500.
Cortes-T., H. G., Figueroa-S., B., Ortiz-S., C. A., and Gonzalez-C., F. V., 1992. Caracterización de la erosividad de la lluvia en México utilizando métodos multivariados. I. Descripción de intensidades máximas e índices de erosividad de la lluvia. Agrociencia, 3 (2): 115-138.
Davis, B., 2000. Introduction to Agricultural Statistics. Delmar, Albany N.Y., USA.
Denevan, W. M., 1992. The Pristine Myth: The Landscape of the Americas in 1492. Annals of the Association of American Geographers, 82 (3): 369-385. 208
Dent, J. B. and Thornton, P. K., 1988. The Role of Biological Simulation Models in Farming Systems Research. Agricultural Administration and Extension, 29 (2): 111-122.
Duley, E. L. and Russel, J. C., 1943. Effect of stubble mulching on soil erosion and runoff. Soil Science Society of America Journal, 7: 77-81.
Easterling, D. R., Meehl, G. A., Parmesan, C., Karl, T. R., and Mearns, L. O., 2003. Climate extremes: Observations, modeling, and impacts. Science, 289 (5487): 2068-2074.
El-Swaify, S. A., Lo, A., Joy, R., Shinshiro, L., and Yost, R. S., 1988. Achieving conservation-effectiveness in the tropics using legume intercrops. Soil Technology, 1: 1- 12.
Elwell, H. A. and Stocking, M. A., 1984. Estimating soil life-span for conservation planning. Tropical Agriculture, 61 (2): 148-150.
Estrada-Berg Wolf, Juan W., 1988. Obtención del factor K de erodabilidad del suelo a partir de información de estudios de suelos en el oriente del Valle de México. Investigación. Universidad Autónoma Chapingo.
Estrella-C., N., 1973. Relaciones empíricas entre el rendimiento del maíz de temporal y algunos factores ambientales en la región de Chalco-Amecámeca, Estado de México. Tesis, Maestría en Suelos. Colegio de Postgraduados, México.
Evans, R., 1980. Mechanics of water erosion and their spatial and temporal controls: an empirical viewpoint. Kirkhy, M. J. and Morgan, R. P. C. (eds). Soil erosion. Wiley. Chichester, UK. 109-128 pp.
Ezcurra, E., Mazari-Hiriart, M., Pisanty, I., and Aguilar, A. G., 1999. The Basin of Mexico: Critical Environmental Issues and Sustainability. United Nations University Press, Tokyo, Japan.
FAO-UNESCO, 1988. Soil map of the world revised legend. 60.World Resources, Food and Agriculture Organization (FAO) and United Nations Educational, Scientific and Cultural Organization (UNESCO), Rome.
Favis-Mortlock, D. T. and Savabi, M. R., 1996. Shifts in rates and spatial distributions of soil erosion and deposition under climate change. In: Anderson,M.G. and Brooks,S.M. (Editors), Advances in Hillslope Processes. John Wiley & Sons; Chichester; UK, 529-560 pp.
Fernández-R., D. S., Oropeza-M., J. L., Martínez-M., M. R., Figueroa-S., B., and Palacios-V., O. L., 1999. Application of the hydrology model SWRRB to the watershed "El Tejocote", Atlacomulco, State of Mexico. Agrociencia, 33 (1): 1-9. 209
Figueroa-S., B., Amante-O., A., Cortes-T., H. G., Pimentel-L., J., Osuna-C., E. S., Rodríguez-O., J. M., and Morales, F. J., 1991. Manual de predicción de pérdidas de suelo por erosión. SARH-Colegio de Postgraduados, Mexico, DF.
Figueroa-S., B., 1975. Pérdidas de suelo y nutrimentos y su relación con el uso del suelo en la cuenca del Río Texcoco. Tesis, Maestría en Suelos. Colegio de Postgraduados, México.
FIRA, 1998. Oportunidades de desarrollo del maíz mexicano: Alternativas de competitividad. Boletín Informativo 30[309]. México, Fideicomisos Instituidos en Relación con la Agricultura (FIRA) del Banco de México.
Florescano, E., 1980. Análisis histórico de las sequías en México.Documentación de la Comisión del Plan Nacional Hidráulico, Secretaría de Recursos Hidráulicos, Mexico D.F.
García, E., 1973. Modificaciones al sistema de clasificación climática de Köppen. Universidad Nacional Autónoma de México, México D.F., México.
Goeck, J. and Geisler, G., 1989. Erosion control in maize fields in Schleswig-Holstein (F.R.G.). Schwertmann, U., Rickson, R. J., and Auerswald, K. (eds). Soil erosion protection measures in Europe. Soil Technology Series. 1: 83-92 pp.
González-G., J. and Zúñiga-P., R., 1992. Efecto del desespigue, acolchado, estercolado y fertilización potásica del maíz (Zea mays) de riego en Chapingo, México. Tesis, Agronomía en Fitotecnia. Universidad Autónoma Chapingo, México.
Grageda-G., J., 1988. Efecto de la labranza y nitrógeno sobre algunas condiciones físicas del suelo y abatimiento de costos de producción de forraje en maíz y sorgo. Tesis, Maestría en Edafología. Colegio de Postgraduados, México.
Harmel, R. D., Richardson, C. W., Hanson, C. L., and Johnson, G. L., 2002. Evaluating the Adequacy of Simulating Maximum and Minimum Daily Air Temperature with the Normal Distribution. Journal of Applied Meteorology, 41 (7): 744-10p.
Hassig, R., 1993. Trade, Tribute, and Transportation: The Sixteenth-Century Political Economy of the Valley of Mexico.Civilization of the American Indian Series, Volume 171. University of Oklahoma Press, Norman, Oklahoma.
Hawkins, R. H., 1993. Asymptotic Determination of Curve Numbers from Data. Journal of Irrigation and Drainage Engineering, 119 (2): 334-345.
Hawkins, Richard H., Lopes, Vicente L., Parker, Ronald A., and Weltz, Mark A., 1991. Efects of global climate change on erosion stability in arid environments using WEPP. Proceedings of the United States-People's Republic of China. U.S. Geological Survey. 244: 85-91. 210
Hudson, N. W., 1995. Soil conservation. 3rd edn. Batsford, London.
ICAMEX, 1994. Paquetes tecnológicos: coordinación regional III Texcoco. Instituto de Investigación y Capacitación Agropecuaria, Acuícola y Forestal del Estado de México (ICAMEX), Toluca, México.
INIA, 1977. El cultivo del maíz en el valle de México. Serie: Circular CIAMEC: 97. Instituto Nacional de Investigaciones Agrícolas (INIA), Campo Agrícola Experimental Valle de México (CIAMEC), México, D. F..
Izaurralde, R. C., Rosenberg, N. J., Brown, R. A., Legler, D. M., Lopez, M. T., and Srinivasan, R., 1999. Modeled effects of moderate and strong 'Los Ninos' on crop productivity in North America. Agricultural and Forest Meteorology, 94 (3-4): 259-268.
Izaurralde, R. C., Rosenberg, N. J., Brown, R. A., and Thomson, A. M., 2003. Integrated assessment of Hadley Center (HadCM2) climate-change impacts on agricultural productivity and irrigation water supply in the conterminous United States: Part II. Regional agricultural production in 2030 and 2095. Agricultural & Forest Meteorology, 117 (1/2): 97-123.
Jensen, J. R., 1996. Introductory Digital Image Processing: A Remote Sensing Perspective. 2nd Edition. Prentice Hall, Upper Saddle River, NJ.
Jones, C. A., Dyke, P. T., Williams, J. R., Kiniry, J. R., Benson, V. W., and Griggs, R. H., 1991. EPIC: an operational model for evaluation of agricultural sustainability. Agricultural Systems, 37 (4): 341-350.
Jones, C. A. and Kiniry, J. R., 1986. CERES-Maize: A Simulation Model of Maize Growth and Development. Texas A&M University Press, College Station, Texas, 194 pp.
Jones, J. W., Tsuji, G. Y., Hoogenboom, G., Hunt, L. A., Thornton, P. K., Wilkens, P. W., Imamure, D. T., Bowen, W. T., and Singh, U., 1998. Decision support system for agrotechnology transfer: DSSAT v3. In: Tsuji,G.Y., Hoogenboom,G., and Thornton,P.K. (Editors), Understanding Options for Agricultural Production: System Approaches for Sustainable Agricultural Development. Kluwer Academic Publishers, Norwell. MA, USA 157-177 pp.
Jones, M. J., 1971. The maintenance of soil organic matter under continuous cultivation at Samaru, Nigeria. Journal of Agricultural Science, 77: 473-482.
Khybri, M. L., 1989. Mulch effects on soil and water loss in maize in India. Moldenhauer, W. C., Hudson, N. W., Sheng, T. C., and Lee, S. W. (eds). Development of conservation farming on hillslopes. Soil and Water Conservation Society. Ankeny IA. 195-198 pp. 211
Knisel, W. G., 1980. CREAMS: a field scale model for chemicals, runoff and erosion from agricultural management systems. USDA Conservation Research Report 26.
Kong, Z. h., Zhang, X. s., and Zhou, G. S., 2002. Agricultural sustainability in a sensitive environment: A case analysis of Loess Plateau in China. Journal of Environmental Sciences China, 14 (3): 357-366.
Landa, R., Meave, J., and Carabias, J., 1997. Environmental deterioration in rural Mexico: An examination of the concept. Ecological Applications, 7 (1): 316-329.
Licona-S., J., Martínez-M., M. R., Mendoza-O., L. E., Figueroa-S., B., and Fernández-R., D. S., 2006. Uso del modelo EPIC para estimar rendimientos de maíz con base en variables fisiotécnicas en el oriente del Estado de México. Terra Latinoamericana, 24 (2): 283-290.
López-M., R., 1996. Estimación del índice de erosividad a partir de la lámina diaria de lluvia.Tesis profesional, Universidad Autónoma Chapingo. Chapingo, México.
Loza-P., A., 1985. Rentabilidad del maíz con tecnología recomendada y regional, Distrito Temporal de Texcoco, México. Tesis, Agronomía en Economía Agrícola. Universidad Autónoma Chapingo, México.
Lozano-G., M. d. S. and Xelhuantzi-L., M. S., 1997. Some problems in the late quaternary pollen records of central Mexico: basins of Mexico and Zacapu. Cuaternary International, 43/44: 117-123.
Lu, Y. and Stocking, M., 2000. Integrating biophysical and socio-economic aspects of soil conservation on the Loess Plateau, China. Part II. Productivity impact and economic costs of erosion. Land Degradation & Development, 11 (2): 141-152.
Macias-D., R., 1992. Influencia de diferentes sistemas de labranza y coberturas del suelo sobre el control de la escorrentía, la erosión, y el rendimiento de forraje del maíz. Tesis, Maestría en Edafología. Colegio de Postgraduados, México.
McAuliffe, J. R., Sundt, P. C., Valiente, B. A., Casas, A., and Viveros, J. L., 2001. Pre- columbian soil erosion, persistent ecological changes, and collapse of a subsistence agricultural economy in the semi-arid Tehuacan Valley, Mexico's 'Cradle of Maize'. Journal of Arid Environments, 47 (1): 47-75.
Meadows, D. H., Randers, J., and Meadows, D. L., 2004. Limits to Growth: The 30-Year Update. First printing. Chelsea Green Publishing Company, White River Junction, Vermont. 212
Mearns, L. O., Easterling, W., Hays, C., and Marx, D., 2001. Comparison of agricultural impacts of climate change calculated from high and low resolution climate change scenarios: Part I. The uncertainty due to spatial scale. Climatic Change, 51 (2): 131-172.
Moen, T. N., Kaiser, H. M., and Riha, S. J., 1994. Regional Yield Estimation using a Crop Simulations Models: Concepts, Methods, and Validation. Agricultural Systems, 46 (1): 79-92.
Mohammed, A. and Gumbs, F. A., 1982. The effect of plants spacing on water runoff, soil erosion and yield of maize (Zea mays L.) on a steep slope of an ultisol in Trinidad. Journal of Agricultural Engineering Research, 27: 481-488.
Mokhtaruddin, A. M. and Maene, L. M., 1979. Soil erosion under different crops and management practices. Proceedings, International conference on agricultural engineering in national development. University Pertanian, Malaysia. Paper 79: 53.
Mora-G., M., 1990. Evaluación de técnicas de captación de agua de lluvia in situ en la producción de maíz (Zea mays L.) para grano y forraje en Tecamac, México. Tesis, Maestría en Edafología. Colegio de Postgraduados, México.
Morgan, R. P. C., Quinton, I. N., Smith, R. E., Govers, G., Poesen, J. W. A., Auerswald, K., Chischi, G., Torn, D., and Styczen, M. E., 1998. The European Soil Erosion Model (EUROSEM): a dynamic approach for predicting sediment transport from fields and small catchments. Earth Surface Processes and Landforms, 23: 527-544.
Morgan, R. P. C., 2005. Soil erosion and conservation. 3er edn. Blackwell Publishing, Oxford, UK.
Neter, J., Wasserman, W., and Kutner, M. H., 1985. Applied linear statistical models: regression, analysis of variance, and experimental designs. 2nd Ed. Irwin, Homewood, IL. USA.
Ortíz-S., C. A., 1987. Elementos de agrometeorología cuantitativa con aplicaciones en la república mexicana. 3er edition. Universidad Autónoma Chapingo, Chapingo, Méx, 88- 96 pp.
Ortiz-S., C. A. and Gutiérrez-C., M. C., 2001. Mexican Ethnopedology: the Experience in Soil Mapping. In: Science and Tradition: Roots and Wings for Developmen. Academie royale des sciences d'outre mer, Amsterdam. 107-136 pp.
Ortiz-S., C. A., Gutiérrez-C., M. C., and Nieves-F., J., 2005. Estimación de rendimientos de maíz con el método FAO en el ejido de Atenco, Estado de México. Revista de Geografía Agrícola, 35 (2): 57-65. 213
Ortíz-S., C. A., 1974. Evaluación de tierras según su producción de maíz en el área de influencia de Chapingo. Tesis, Maestría en Suelos. Colegio de Postgraduados, México.
Ortiz-T., E. A., 1990. Rendimiento de híbridos de maíz (Zea mays L.) de la zona de transición el Bajio Valles Altos, por efecto de la utilización de semilla de generaciones F1 y F2. Tesis, Agronomía en Fitotecnia. Universidad Autónoma Chapingo, México.
Osorio-A., G., 1989. Optimización del agua de lluvia mediante captación in situ, cobertura orgánica y arreglo espacial en maíz en función del volumen de agua por planta. Tesis, Maestría en Edafología. Colegio de Postgraduados, México.
Osuji, G. E., Babalola, 0., and Aboaba, F. 0., 1980. Rainfall erosivity and tillage practices affecting soil and water loss on a tropical soil in Nigeria. Journal of Environmental Management, 10: 207-217.
Parra-V., M. R., 1981. Producción de maíz en condiciones de temporal, en Tequexquinahuac, Texcoco, Edo. de México. Tesis, Maestría en Botánica. Colegio de Postgraduados, México.
Parrott, N. and Marsden, T., 2002. The Real Green Revolution: Organic and agroecological farming in the South. Greenpeace Environmental Trust, London U.K..
Peña-O., B. V., 1973. Evaluaron del levantamiento fisiográfico de la región sur-oriental del Valle de México cundo se usa como base para desarrollar recomendaciones de productividad. Tesis, Maestría en Suelos. Colegio de Postgraduados, México.
Pierce, F. J., Larson, W. E., Dowdy, R. H., and Graham, W. A. P., 1983. Productivity of soils: Assessing long-term changes due to erosion. Journal of Soil Water Conservation, 38 (1): 39-44.
Pimentel, D., 1993. World soil erosion and conservation.Cambridge studies in applied ecology and resource management, Cambridge University Press, Cambridge.
Pimentel, D., Culliney, T. W., Buttler, I. W., Reinemann, D. J., and Beckman, K. B., 1989. Low-input sustainable agriculture using ecological management practices. Agriculture, Ecosystems & Environment, 27 (1-4): 3-24.
Pimentel, D., Harvey, C., Resosudarmo, P., Sinclair, K., Kurz, D., McNair, M., Crist, S., Shpritz, L., Fitton, L., Saffouri, R., and Blair, R., 1995. Environmental and economic costs of soil erosion and conservation benefits. Science, 267 (5201): 1117-1123.
Pimentel, D. and Krummel, J., 1987. Biomass energy and soil erosion: Assessment of resource costs. Biomass, 14 (1): 15-38. 214
Pimentel, D. and Pimentel, M., 1996. Food, energy, and society . University Press of Colorado, Niwot, Colorado, 363 pp.
Pulido, J. S. and Bocco, G., 2003. The traditional farming system of a Mexican indigenous community: The case of Nuevo San Juan Parangaricutiro, Michoacan, Mexico. Geoderma, 111 (3-4): 249-265.
Reicosky, D. C. and Forcella, F., 1998. Cover crop and soil quality interactions in agroecosystems. Journal of Soil and Water Conservation, 53 (3): 224-229.
Renard, K. G., United States, and Agricultural Research Service, 1997. Predicting soil erosion by water: a guide to conservation planning with the revised universal soil loss equation (RUSLE). no. 703.Agriculture handbook (United States. Dept. of Agriculture), U.S. Dept. of Agriculture, Agricultural Research Service, Washington, D.C.
Rhoads, F. M. and Bennett, J. M., 1990. Corn. In: Stewart,B.A. and Nielsen,D.R. (Editors), Irrigation of agriculture crops. Agron. Monogr. 30. ASA, CSSA, and SSSA, Madison, WI. 569-596 pp.
Richardson, C. W. and Wright, D. A., 1984. WXGEN: A model for generating daily weather variables. [ARS-8]. USDA-ARS.
Rifkin, J. and Howard, T., 1989. Entropy: Into the Greenhouse World. Revised Edition. Viking Penguin Inc., New York.
Ríos-B., J. D., 1987. Efecto de la cobertura vegetal en el proceso erosivo. Tesis, Maestría en Edafología. Colegio de Postgraduados, México.
Rodríguez-G., M., 1995. Descripción y análisis del programa especial de producción de maíz en el Distrito de Desarrollo Rural III Texcoco, Edo. de México ciclos agrícolas P. V. 1991-1994. Tesis, Agronomía en Fitotecnia. Universidad Autónoma Chapingo, México.
Rose, C. W., Williams, J. R., Sander, G. C., and Barry, D. A., 1983. A mathematical model of soil erosion and deposition process. I. Theory for a plane element. Soil Science Society of America Journal, 47: 991-995.
Ruíz-F., J. F., 1979. Evaluación de cinco tipos de terrazas en suelos de ladera de la cuenca del Rió Texcoco. Tesis, Maestría en Suelos. Colegio de Postgraduados, México.
Rykiel, E. J., 1996. Testing ecological models: the meaning of validation. Ecological Modelling, 90 (3): 229-244.
SAGARPA-SIAP, 2004a. Sistema de Información Agropecuaria de Consulta: SIACON 1980-2003. http://www.siap.sagarpa.gob.mx/ar_comdownload.html October/11/2004[Version 1.1]. México, D.F., Secretaría de Agricultura, Ganadería, 215
Desarrollo Rural, Pesca y Alimentación (SAGARPA). Servicio de Información Agroalimentaria y Pesca (SIAP), México.
SAGARPA-SIAP, 2004b. Situación Actual del Maíz en México 1990-2004. http://www.siap.sagarpa.gob.mx/ar_comdownload.html . Secretaría de Agricultura, Ganadería, Desarrollo Rural, Pesca y Alimentación (SAGARPA). Servicio de Información Agroalimentaria y Pesca (SIAP), México.
Sanders, W. T., Parsons, J. R., and Santley, R. S., 1979. The Basin of Mexico: Ecological Processes in the Evolution of a Civilization.Studies in Archaeology, Academic Press, New York.
Saxton, K. E., Rawls, W. J., Romberger, J. S., and Papendick, R. I., 1986. Estimating generalized soil-water characteristics from texture. Soil Science Society of America Journal, 50 (4): 1031-1036.
SEDAGRO, 2003. Estimación de rendimientos de maíz, trigo, cebada y avena forrajera: ciclo primavera-verano 2003. Secretaria de Desarrollo Agropecuario del Gobierno del Estado de México (SEDAGRO). Technical Report: Informe de Resultados 2003, Toluca, México.
Sharpley, A. N. and Williams, J. R., 1990a. EPIC Erosion/Productivity Impact Calculator: 1. Model Documentation. USDA Technical Bulletin No. 1768. U.S. Govt. Printing Office, Washington, DC., 235 pp.
Sharpley, A. N. and Williams, J. R., 1990b. EPIC Erosion/Productivity Impact Calculator: 2. User manual. USDA Technical Bulletin No. 1768. U.S. Govt. Printing Office, Washington, DC., 127 pp.
Simon, J., 1997. Endangered Mexico: An Environment on the Edge. Sierra Club Books, San Francisco, Cal..
Singh, G., Bhardwaj, S. P., and Singh, B. P., 1979. Effect of row cropping of maize and soybean on erosion lossess. Indian Journal of Soil Conservation, (7): 43-60.
Singh, V. P., 1995. Computer models of watershed hydrology. Water Resources Publications; Colorado; USA.
Solano de la Sala-T., J. A., 1982. Efecto de la relación precipitación escorrentía en el proceso erosivo en diferentes usos del suelo en la cuenca del Río Texcoco. Tesis, Maestría en Edafología. Colegio de Postgraduados, México.
Solís-A., R., 1988. Zonificación agroecológica para cinco cultivos en el Distrito de Desarrollo rural integral No. 111 Texcoco, Méx. Tesis, Agronomía en Suelos. Universidad Autónoma Chapingo, México. 216
Stocking, M. and Lu, Y., 2000. Integrating biophysical and socio-economic aspects of soil conservation on the Loess Plateau, China. Part I. Design and calibration of a model . Land Degradation & Development, 11 (2): 125-139.
Stockle, C. O., Dyke, P. T., Williams, J. R., Jones, C. A., and Rosenberg, N. J., 1992. A method for estimating the direct and climatic effects of rising atmospheric carbon dioxide on growth and yield of crops. II. Sensitivity analysis at three sites in the midwestern USA. Agricultural Systems, 38 (3): 239-256.
Tapia-N., C. A., 1985. Adecuación de la tecnología agrícola de producción de maíz de temporal recomendada por el INIA para la región de Chalco-Amecameca, Méx. Tesis, Maestría en Desarrollo Rural. Colegio de Postgraduados, México.
Tapia-V., L. M., 1999. Escurrimiento y producción de sedimentos en sistemas de manejo agrícola en la cuenca del Lago de Patzcuaro. Tesis, Doctorado en Edafología. Colegio de Postgraduados, México.
Torres-B., E., Fernández-R., D. S., Oropeza-M., J. L., and Mejía-S., E., 2004. Calibration of the Hydrologic Model SWAT in the Watershed "El Tejocote ", Atlacomulco, State of Mexico. TERRA, 22 (4): 437-444.
Trueba-C., A., 1978. Evaluación de la eficiencia de cuatro prácticas mecánicas para reducir las pérdidas de suelo y nutrimentos por erosión hídrica en terrenos agrícolas de temporal. Tesis, Maestría en Suelos. Colegio de Postgraduados, México.
USDA, 1986. Urban hydrology for small watersheds. Soil Conservation Service, Engineering Div. tr-55.
USDA-SCS, 1985. Hydrology: Section 4 (NEH-4). In: National Engineering Handbook, Soil Conservation Service (SCS), Amendment. U.S. Government Printing Office: USDA, Washington D.C.
Valverde-N., J. G., 2002. Estructura, comportamiento, tecnologías y rentabilidad de la producción de maíz de grano en el DDR 075 Texcoco, Estado de México: informe analítico. Tesis, Agronomía en Fitotecnia. Universidad Autónoma Chapingo, México.
Vega-V., D. D. and Ramírez-M., P., 2004. Situación y perspectivas del maíz en México. http://www.economia.gob.mx . Universidad Autónoma Chapingo.
Véliz-F., E. H., 1993. Efecto de la densidad de siembra y niveles de humedad en el suelo sobre el rendimiento de cultivos asociados maíz-frijol (Zea mayz L. - Phaseolus vulgaris L.). Tesis, Maestría en Hidrociencias. Colegio de Postgraduados, México. 217
Ventura-R., E., 1988. Dinámica de la erosión hídrica bajo diferentes niveles de productividad en maíz (Zea mays L.) y cebada (Hordeum vulgare L.). Tesis, Agronomía en Suelos. Universidad Autónoma Chapingo, México.
Wanielista, M. P., 1990. Hydrology and water quantity control. John Wiley & Sons, Inc..
Whisler, F. D., Acock, B., Baker, D. N., Fye, R. E., Hodges, H. F., Lambert., J. R., Lemmon, H. E., McKinion, J. M., and Reddy, V. R., 1986. Crop Simulation Models in Agronomic Systems. Advances in Agronomy, 40: 141-208.
Williams, J. R., 1990. The erosion-productivity impact calculator (EPIC) model: A case history. Philosophical Transactions of the Royal Society of London.Series B, Biological Sciences, 329 (1255): 421-428.
Williams, J. R., Jones, C. A., Kiniry, J. R., and Spanel, D. A., 1989. The EPIC crop growth model. Transactions of the ASAE, American Society of Agricultural Engineers, 32 (2): 497-511.
Young, R. A., Onstad, C. A., Bosch, D. D., and Anderson, W. P., 1989. AGNPS: a nonpoint-source pollution model for evaluating agricultural watersheds. Journal of Soil and Water Conservation, 44: 168-173.
Zahniser, S. and Coyle, W., 2004. U.S.-Mexico Corn Trade During the NAFTA Era: New Twists to an Old Story. Electronic Outlook Report [FDS-04D-01]. www.ers.usda.gov , Economic Research Service, United States Department of Agriculture.
Zazueta-Z., G., 1984. Influencia de los sistemas de labranza y obras de conservación en la productividad de los suelos. Tesis, Maestría en Edafología. Escuela Nacional de Agricultura, México.