An Object Based Alfalfa Model

An object based Alfalfa model Ted Sammis and John Mexal Dan Smeal

Crop simulation models that contain a growth model, a water balance subroutine and calculation of evapotranspiration can be used to direct and complement field experiments on irrigation and hydrologic systems (Loague et al., 1995) and to develop decision support systems. They can also in conjunction with remote sensing, allow the special calculation of water use by irrigated agriculture. IBSNAT or International Benchmark Sites Network for Agrotechnology Transfer project ( Uehara. and Tsuji, 1998) was developed to apply systems analysis and simulation models to problems faced by resource-poor farmers in the tropics and sub-tropics. The principal output of the Fortran based program was DSSAT, a decision support system software that provided a linked, computerized programming framework for crop simulation models, databases, and application programs. (Tsuji 1998.) The DSSAT program has been used extensively around the world to evaluate both crop field experiments and allocation of limited resources.

The DSSAT system consisted of a series of crop models each having the same soil water balance submodel but the growth submodels were unique to the crop. The first of the crop model developed was CERES-Maize a corn growth model (Jones and Kiniry 1986). Another model, SOYGRO , is a process-oriented soybean crop growth simulation model that simulates daily values of leaf area index, root length density, soil water content, transpiration, evapotranspiration, potential evapotranspiration, yield components, and water stress. (Pereira, L. S. Broek, B. J. van den. Kabat, P. Allen, R. G. 1993). The model like most crop models must be calibrated for a site because the SOYGRO model has a large bias in simulation of yield with latitude. ( Piper, Boote and Jones, 1998).

More physiological based crop models are developed to increase the understanding of the physiology of the crop and interface with insect models. Passioura (1996) makes the argument that models fall into two categories where models are developed for scientific understand of the process and are mechanistic in nature or they are developed to solve management problems and are functional in nature. The mechanistic models are based on a hypothesis of how a plant grows which may or may not be correct, and the models are very difficult to run because of the large number of inputs and state variable changes that occur in the models that cannot be measured. Consequently these model are more valuable to the developer to synthesize research understanding and to integrate knowledge across disciplines ( Kenneth et al. 1996) and teach process concepts to students. The functional models are robust and east to understand and run but are not necessarily accurate outside the environmental conditions that were used in the development of the models. The SOYGRO model is more functional in nature and demonstrated this principal. The functional model can illuminate to a limited degree the mechanistic aspect of plant growth but these models are better used to develop BMPs for farmers use with in the conditions for which for which they have been calibrated.

1 All the developed alfalfa models are mechanistic in nature. The first alfalfa model SIMED written by Holt et al. (1975) in FORTRAN incorporated most physiological process but not the regrowth process after cuttings. The second alfalfa model developed was ALSIM ( Fick 1976, 1981) written in FORTRAN which had subroutines to handle buds to regrowth after a cutting. The ALFALFA 1.4 model by Denison and Loomis (1989) was a physiological based Alfalfa model that simulates alfalfa growth and development begining with tissue- and organ-level information, and then growing leave and stem including internodes and underground structures (crown, taproot, and fibrous roots). Over wintering and stand persistence is also simulated. The code is again written in Fortran. The latest alfalfa model was developed by Kanneganti, et al (1998) to evaluate frieze injury to alfalfa yield and is a modification of ALSIM model by Fick. Fortran based model are ease to write for a trained computer scientist but are difficult to run by most biological or agricultural scientists. The models are not user-friendly.

A simulation model that is a user-friendly decision support systems for irrigated crops should include all objects necessary to grow the crop using either mechanistic or empirical functional relationships. (Acock and Reynolds 1989, Reynolds and Acock. 1997) Object –oriented decision support programs model real world objects with software counterparts and each object consists of encapsulated data (attributes) and methods (behavior, and interactions). Objects interact with each other and with their environment. Objects also interact with users such as a user change in an objects attribute ie the field capacity of a soil pedon. Object oriented programming takes advantage of class relationships where objects of a certain class share the same characteristics, attribute types and operations. An example would be a class of objects that simulated alfalfa grown at spatially different locations having different soil type.

A crop model can be built around object-oriented programming where objects may be added or subtracted from the model with ease. One crop model may have a nitrogen balance component and another model may lack simulation of nitrogen because it is considered a non-limiting factor. In the past, computer crop models were written with subroutines to represent objects but the structure was such that they could not be easily removed because the attributes of the object were not part of the subroutine but were a separate object.

Another problem with mechanistic models is that they are usually written in FORTRAN or some other computer language not understood by the general scientific community. With the increase in computing speed, spreadsheet programs can replace the FORTRAN based crop simulation models as long as the model are simple in structure. Models must be simple enough to be comprehensible by others, but complex enough to be

2 comprehensive in scope. (Monteith, 1966). Reynolds and Ascock (1985) postulated that models errors arise from systematic bias resulting from oversimplifying to cumulative errors in the parameters with increase in model complexity. Consequently, a balance must be keep when developing a model to make sure that the individual components can be test independently of the other components and that the number of components do not become so large that the model acts like a multiple regression model. Models differ in conceptual development, complexity, and input requirements. Simple models can be used to approximate more complex models. Therefore, both mechanistic and structural models should be tested to identify their limitations under different management and environmental conditions.

The purposed of this research was to develop a simple use friendly object orientated daily growth-irrigation scheduling alfalfa model using an excel spreadsheet as the computer language that allows the user to easly change the mathematical functions in the program. The use of the model is to help make management decisions, and consequently the model is more functional in nature.

Growth-Irrigation Scheduling Model (GISM) description

The growth model was based on the relationship between Et, soil moisture availability, and growth. A line source alfalfa irrigation experiments in Las Cruces and Farmington NM was used to develop and test the model. A volume balance model served as the water balance component. Et was determined by using climate data to calculate a reference evapotranspiration (Eto) and a crop coefficient (Kc) to scale the daily Eto calculated with the equation developed by Samani and Pessarakli (1986) or modified Penman (Sammis et. al. 1986). Eto was calculated in a reference Et object in excel for Penman’s calculation of Eto or using a java language reference Et object for Samani’s calculation of Eto and then pasting the results into a water balance excel object. Climatic inputs for the Eto object consisted of daily maximum and minimum temperature and rainfall. These weather inputs were obtained from the New Mexico Climate Center Internet site. After the alfalfa has a closed canopy, the amount of Et that is soil evaporation is small because the solar radiation is intercepted by the leaves and branches and does not reach the soil surface. The calculated nonstressed Et for a closed canopy was reduced by a water stress function, located in the water balance object which was a function of the proportional available water in the root zone (Abdul-Jabbar et al., 1983) The growth simulation object required several initial growth conditions as inputs in order to calculate the first day’s growth. These initial conditions include initial alfalfa stem height, initial steam tree radius, wood density, specific leaf area, initial leaf weight, initial stem weight, and number of alfalfa stems per unit area. The biomass produced in each time step is based on the calculated Et for the alfalfa from the water balance object multiplied by the WUE. The alfalfa growth rate was modeled on a daily time step based

3 on the initial alfalfa biomass separated into leaves and stems plus branches. The leaf area per alfalfa stem (m2/stem) is modeled by multiplying the cumulative leaf growth per stem by the specific leaf area (m2/kg). The initial and subsequent calculated projected leaf area was calculated by dividing the total leaf area by a leaf area index specified in the model at 4. The new calculated Et was then converted to total biomass, using the WUE value, and subsequently partitioned into new leaf and stem and reproductive growth if enough growing degree days had occurred since the last cut to cause the alfalfa to switch from vegetative to reproductive growth. The percent allocated to each part was an input parameter of the growth object model. The cumulative alfalfa stem diameter (mm) and height (m) were modeled by converting stem growth to volume based on the wood density and then solving for the stem size with the calculated volume of a cone, and stem radius to height ratio specified as an input parameter. A cut object was developed based on a number of gdd between cuts or by a user input the cut object. A scaling factor developed to represent regrowth was calculated based on gdd and a growth coefficient and this scalling factor was passed to the water balance object to reduce Et following a cut. The cut object also calculated when the alfalfa would convert from vegetative to reproductive stage and then the switch was past to the grow object.

Materials and methods

The experiment was conducted from 1980 through 1987 at New Mexico State University Agricultural Science Center, Farmington, NM (36o 42’ N, 108o 15’ W) at an elevation of 1710 m. The average length of the growing season is 160 days (May to October) and average annual precipitation is 200 mm. The soil was a Wall sandy loam (Typic Camborthid of the coarse, loamy, mixed calcareous, mesic family) having a soil water holding capacity of about 240 mm in the top 1.2 m, and 270 to 360 mm in the next 1.8 m of the profile. The profile is deep, well-drained, and moderately permeable. The research site has no identifiable water tables and no known root restricting layers exist in the top four meters of the profile.

Alfalfa (cv. WL-309) was planted at a rate of 45 kg ha-1on 22 August 1980. The plot was irrigated uniformly at a rate of 15 mm per week to the end of the 1980 irrigation season (1 Oct.) to insure uniform germination and establishment. In the spring of 1981 through the last harvest of 1987, water was applied to the plot using a line-source sprinkler design similar to that described by Hanks et al. (1976). Eight sprinkler heads (Model 30H, Rainbird Co.) placed at 6.1 m intervals along the line were operated at 0.3 Mpa pressure during calm atmospheric conditions to provide a symmetrical gradient of irrigation water from the center to the edges of the plot 14.5 m away. The plot size was 24 m long by 30 m wide. Catchment cans for measurement of irrigation rates and neutron probe access tubes to determine soil water content (Sw) were installed in fourteen subplots. Each subplot was 24 m long by 1 m wide and the center of the subplots were 1.8, 3.6, 5.4, 7.2, 9, 10.8, and 12.6 m from each side of the sprinkler line. Additionally, three drainage- type lysimeters, 0.9 m wide, 6.1 m long, and 1.8 m deep were installed on one side of, and at distances 0.5, 3.6, and 7.2 m from the line. Each lysimeter contained four neutron probe access tubes and catchment-cans 1.5 m apart. Sw was measured at the 0.15 m depth

4 and in 0.30-m increments thereafter to a depth of 1.8m in the lysimeters, and to 3.0 m in the 14 subplots outside of the lysimeters on a 7 to 14 day schedule and on harvest dates with a neutron probe. The plot was irrigated two to three times per week at a rate sufficient to restore soil water to near field capacity in the top 1.8 m of the profile in the subplot adjacent to the sprinkler line. Water application on the subplots at the edge of the field was near zero. Rainfall (R) was measured with a 0.2 m USWB standard rain gauge at the experimental site. No water drainage was noted from the lysimeters. Runoff was negligible since the water application rate (20 mm per hour) did not exceed the soil infiltration rate (25 mm per hour) near the sprinkler line.

Evapotranspiration at each subplot was calculated using the water balance method: ET =1 + R + ∆Sw (1)

Where: ET = evapotranspiration, mm I = irrigation, mm R = precipitation, mm ∆Sw = change in soil water, mm

Roots of the crop were not influenced by a water table. Transpiration was separated from measured ET (Eq. 1) by using the modeling procedure of Sammis et al. (1986). Stage one evaporation in the model was estimated from calculated reference evapotranspiration and an exponential function based on leaf area index (Al-Khafaf et al. 1978). Stage two evaporation in the model was a function of time and soil type (Jensen 1973). Evaporation (E), as estimated by the model, was subtracted from ET to provide an estimate of T for each growth period between cuts.

The number of growing degree days (Ge) from the breaking of dormancy each year to the first cut and in subsequent growth periods were calculated as follows: N

Ge =  [((Mx + Mn)/2) –Mb] (2) i=1 where: o Mx = daily maximum temperature ( C) for day i o Mn = daily minimum temperature ( C) for day i o Mb = base temperature (5.0 C) N = number of days since beginning of growing period.

Each year, measurements began as soon as new green shoots and leaves were visible on the plants . Cuttings were made when approximately ten percent of alfalfa stems near the sprinkler line were in flower. From 1981 to 1985, 14 strips of alfalfa, parallel to the sprinkler line, 0.9 m wide and 24 m long, centered on each catchment can and access tube, were cut to a 5-cm height with a Kinco, sickle-bar mower. In 1986 and 1987, the width of the subplots was increased to 1.3 m but remained centered on the tubes and cans.

5 They were cut with an Almaco forage harvester. Alfalfa in the lysimeters was clipped to a 5 cm height using hedge clippers.

Immediately after weighing the forage from each cutting, water content was determined. Single representative samples from each subplot were weighed in the field and then dried in a forced-convection drying oven at 103o C for at least 48 h before being re-weighed. Yields were then expressed on an oven-dry weight basis.

The height to radius ratio was measured in the flood irrigated alfalfa field located 16 km south of Las Cruces NM on a ------soil. The field was irrigated by the farmer following a cut and then two week later.

Results The input parameters for the model (Table 1) were the same for all the irrigation treatments. However, the irrigation dates and amounts were different for each run. The WUE value was obtained from a previous study (Sammis 1981) and includes the evaporation component.

Table 2. Alfalfa growth irrigation scheduling model setup parameters.

Parameter Value

Assume leaf area index per projected area 4 Ratio of height to radius 100 Wood density (kg/m3) 300 Initial alfalfa plant diameter (m) 0.00007 Initial plant height (m) 0.01 Percent of leaves (%) 45 Starting root depth (cm) 152 Max root zone depth (cm) 152 Percent of stem % 55 WUE (kg/ha/mm) 12 No. of steams /ha 15000000 Specific leaf area [SLA] (m2/kg) 22.4 Calculated initial leaf weight (kg) 0.0000002 Calculated initial trunk weight (kg) 0.0000000015 Root growth coefficient (cm/GDD C) 0.03 Soil water holding capacity-loam soil (m/m) 0.16

6 Irrigation amounts (cm) Specified at applied times in column V of spreadsheet.

The Kc for a closed canopy was derived from Sammis et al. (1985).

The actual soil water content influences alfalfa water use. As soil dries, it becomes more difficult for the plant to extract water from the soil. At field capacity (maximum water content), plants use water at the maximum rate. When the soil water content drops below 30% of the available water in the soil, plants start to close their stomata and use less water. Irrigation should begin when the crop comes under water stress, which will lead to reduced crop yield and quality. The level of water stress depends on the kind of crop and its stage of development. Alfalfa is normally stressed for water because if it is flood irrigated, only two irrigations are possible between cuts in order for the soil to dry sufficiently to be able to operated

The steam density (no. /ha) was reported by Saeed and El-Hadi ( 1997) for a fully irrigated alfalfa field, and the steam density decreased with cuttings until the last cutting where the steam density again increased. This function was put into the model. Specific leaf area was reported by Leavitt et al. (1979) as 22.4 m2/kg and percent leaves by weight as 45 percent.

References

Abdul-Jabbar, A.S., T.W. Sammis, D.G. Lugg, C.E. Kallsen, and D. Smeal. 1983. Water use by alfalfa, corn and barley as influenced by available soil water. Agricultural Water Management, 6:351-363.

Acock B., and Reynolds J.F., 1989. The rationale for adopting a modular generic structure for crop simulators. Acta Horticulturae 248:391-396.

Denison, R.F., and R.S. Loomis. 1989. An Integrative Physiological Model of Alfalfa Growth and Development. Univ. Calif. Div. Agric. Natural Resources, Publ. 1926, 73 pp. Fick , G. W. 1977 The mechanism of alfalfa regrowth: A computer simulation approach. Search Agric. 7(3):1-25.

Fick, G. W. 1981, ALSIM 1 (Level 2) Users manual. Mimeo. 81-35 Ithaca: Cornell University, Dept . Agronomy.

7 Holt, D. A. , R. J. Bula, G. E. Miles, M.M. Schreiber, and R. M. Peart. 1975. Environmental physiology, modeling and simulation of alfalfa growth. I. .Conceptual development of SIMED . West Lafayette, Ind. :Purdue University, Agric Exp. Sta. Bull. No 907., 26 pp

Jones, C. A. and J. R. Kiniry 1986. Ceres-Maize: A simulation model grow and development. College Station Texas A and M University Press.

Kanneganti, V. Rao., William L. Bland, and Daniel J. Undersander 1998. Modeling Freezing Injury in Alfalfa to Calculate Forage Yield: II. Model Validation and Example Simulations. Agron. J. 90:698-704

Leavitt, J. .R. C. , A. K. Dobrenz and Joe E. Stone, 1979. Physiological and Morphological Characteristics of Large and Small Leaflet Alfalfa Genotypes. Agronomy Journal 71: 529-533

Monteith, J. L. 1996. The quest for balance in crop modeling. Agron. J. 88:695-697

Paassioura, J. B. 1996. Simulation Models: Science, Snake Oil, Education , or Engineering? Agron. J. 88:690-694

Piper, E. L. Boote, K. J. Jones, J. W. 1998. Evaluation and improvement of crop models using regional cultivar trial data. Applied Engineering in Agriculture. 1998. 14: 4, 435-446

Pereira, L. S. Broek, B. J. van den. Kabat, P. Allen, R. G.. 1993. The soybean model SOYGRO: field calibration and evaluation of irrigation schedules. Crop-water- simulation models in practice: selected papers of the 2nd Workshop, 15th Congress of the International Commission on Irrigation and Drainage (ICID) at The Hague, Netherlands, 1993.

Reynolds, J.F. and B. Acock 1985. Predicting the response of plants to increasing carbon dioxide: A critique of plant growth models. Ecol. Modell. 29:107-129

Reynolds, J.F. and B. Acock. 1997. Modularity and genericness in plant and ecosystem models. Ecological Modeling 94: 7-16.

Saeed, I. A. M. and A. H. El Nadi 1997 Irrigation effects on the growth, yield and water use efficiency of alfalfa. Irrig Sci 17:63-68

Sammis, T.W., W. Williams, D. Smeal, and C.E. Kallsen. 1986. Effect of soil moisture stress on leaf area index evapotranspiration and modeled soil evaporation and transpiration. Trans. of ASAE, 29(4) 956-961.

8 Sammis T. W. 1981. Yield of Alfalfa and Cotton as Influenced by Irrigation. Agronomy Journal 73:323-329

Sammis T. W. , C. L. Mapel, D. G. Lugg, R. R. Lansford , J. T. Mc Guckin 1985. Evapotranspiration Crop Coefficients Predicted Using Growing Degree Days ASAE 28(3):773-780

Samani, Z.A., Pessarakli, M., 1986. Estimating potential crop evapotranspiration with minimum data in Arizona. Transactions of the ASAE 29(2), 522-524.

Tsuji, G. Y. 1998. Network management and information dissemination for agrotechnology transfer. Understanding options for agricultural production. Kluwer Academic Publishers, Dordrecht, Netherlands: 1998. 367-381.

Uehara, G. Tsuji, G. Y 1998 Overview of IBSNAT. Understanding options for agricultural production. Kluwer Academic Publishers, Dordrecht, Netherlands: 1998. 1-7.