Can The Momentum be Sustained? An Economic Analysis of the Ministry of Agriculture/Sasakawa Global 2000's Experiment with Improved Cereals Technology in Ethiopia
A Joint Research Activity of:
Grain Marketing Research Project/Michigan State University
Sasakawa Global 2000
Ministry of Agriculture Department of Extension and Cooperatives
Ethiopian Agricultural Research Organization
September 1998
Funding for this research was provided by the Food Security II Cooperative Agreement (AEP- 5459-A-00-2041-00) between Michigan State University and the United States Agency for International Development, through USAID/Ethiopia and Africa Bureau's Office of Sustainable Development, Productive Sector Growth and Environment Division (AFR/SD/PSGE). CONTRIBUTORS TO THE STUDY
The authors of this report are:
Julie Howard*, Mulat Demeke**, Valerie Kelly*, Mywish Maredia*, and Julie Stepanek*
* Department of Agricultural Economics, Michigan State University ** Department of Economics, University of Addis Ababa
Others contributing to the research are:
Survey Coordinator: Ali Said Survey Supervisors: Demeke Abate Mengistu Buta Selamyihun Kidane
Interviewers: Negusie Belete Tsige Alemu Yemiserach Gebeyehu Jemal A/Faji Tamrat Yigezu Masresha Getachew Afework Dubero Kebede Negusie Belayneh A/Mariam Elfnesh Dubero Aytenew Shibru Biruk Seifu Zewdu Damenu Bullo Edossa Dabeta Eticha Birbo Bissa Befekadu Tesfaye Danil Tesfaye Duresso Chimsa Sebsibe Gemechu Derbe Gudissa Muluneh Kebede Girma Mulugeta Kumelcha Bulto Aynalem Mekuria Tesfaye Midikssa Warkaye Bekele Mame Kebede Bekele Wakjira Raya Kebede Nigatu Bekele
Data Entry Clerks: Abebech Demissies Senait Hundie
Data Cleaning Supervisors: Margaret Beaver Tesfaye Zegeye Mulat Demeke
Drivers: Asrat Gebre Workeneh Molla
i ACKNOWLEDGEMENTS
The research team wishes to thank the participating farmers, the Ministry of Agriculture Department of Extension and Cooperatives staff in Addis Ababa, East Shoa, West Shoa, and Jimma, and the staff of SG2000 in Ethiopia for their generous assistance during the course of the survey. GMRP staff members were also extremely helpful: Thanks go to Alemu Asfaw for his assitance with price data, to Samson Dejene for assistance with data entry and analysis, to Tigist Tesfaye for secretarial support. The researchers with the GMRP and the MSU Food Security II Project who provided encouragement and willingly shared their ideas with us on numerous survey and analysis issues are too numerous to mention individually, but Aklu Girgre, Thom Jayne, and Mike Weber deserve special mention.
We would also like to thank Don Beaver for assistance in the design of plot measurement procedures and software; Rick Ward and Urs Schulthess for help in designing data collection procedures and interpreting results for the yield determinants parts of the research; and Scott Swinton and Eric Crawford for their assistance in designing the data analysis procedures and interpreting the results.
ii TABLE OF CONTENTS
CONTRIBUTORS TO THE STUDY...... i
ACKNOWLEDGMENTS...... ii
LIST OF TABLES ...... v
LIST OF FIGURES...... v
LIST OF APPENDICES...... v
LIST OF ACRONYMS ...... vi
Section Page
1. INTRODUCTION ...... 1 1.1. Objectives ...... 1 1.2. Methods...... 2 1.2.1. Sample Selection...... 2 1.2.2. Questionnaire Design and Data Collection...... 5
2. CHARACTERISTICS OF PROGRAM PARTICIPANTS COMPARED TO THOSE OF TYPICAL AGRICULTURAL HOUSEHOLDS ...... 7
3. YIELD RESULTS AND FINANCIAL ANALYSIS ...... 10 3.1. Yield Results ...... 10 3.1.1. Maize...... 10 3.1.2. Tef...... 11 3.2. Financial Analysis...... 12 3.2.1. Data and Methods Used...... 12 3.2.2. Improved Technology is Profitable for both Maize and Tef, Even if Output Prices Decline by 25% or 50% ...... 13 3.2.3. Gains from Storage and Use of Storage Insecticide...... 13 3.2.4. Improved Seed and Fertilizer Costs Represent 50-80% of Total Costs . 14
iii 4. FACTORS AFFECTING CROP YIELDS...... 18 4.1. Factors Affecting Maize Yields...... 18 4.1.1. Types of Maize Technologies and Their Yields...... 18 4.1.2. Econometric Analysis of Maize Yield Determinants ...... 20 4.1.3. Descriptive Analysis of Factors Related to Maize Technology Choice . . 23 4.1.4. Graduate Farmers’ Decisions Concerning Choice of Maize Technology 23 4.2. Factors Affecting Tef Yields...... 26 4.2.1. Types of Tef Technologies and Their Yields...... 26 4.2.2. Econometric Analysis of Tef Yield Determinants...... 29 4.2.3. Graduate Farmers’ Decisions Concerning Choice of Tef Technology . . . 32
5. MOVING FROM DEMONSTRATION PLOTS TO SUSTAINABLE INCREASES IN AGRICULTURAL PRODUCTIVITY ...... 34
5.1 Challenges Facing the Extension Service...... 35 5.2. Evolution of the Agricultural Input Credit System ...... 35 5.3. Description of the Inputs Subsector ...... 37 5.3.1. Fertilizer Market Development -- Balancing the Need for Decentralization and Competition ...... 37 5.3.2. The Seed Sector - Much Work Ahead...... 37 5.4. Key Sustainability Issues ...... 38
6. CONCLUSIONS ...... 40 6.1. Yield and Income Gains from the Use of Improved Technology Are High.....40 6.2. Gains from Storage Are Potentially Great, Especially If Storage Insecticide Is Used ...... 40 6.3. Improved Seed and Fertilizer Are the Most Important Determinants of Yield ...... 41 6.4. Key Questions: How to Sustain this Successful Program in High Surplus- producing Regions, and How to Expand it Beyond Wealthier Farmers in Better Agroecological Areas? ...... 41
APPENDIX 1...... 44
APPENDIX 2...... 78
APPENDIX 3...... 89
REFERENCES...... 96
iv LIST OF TABLES
No. Title Page 1 Sample Composition ...... 4 2 Agroecological Characteristics and Recommended Technology Packages...... 4 3 Selected Characteristics of Participant Households Versus the Broader Population of Agricultural Households...... 8 4 1997 Fertilizer Use of Participants Versus the Broader Agricultural Population ...... 9 5 Summary of Key Yield and Profitability Results by Zone, Plot Type, and Yield Tercile ...... 15 6 Types of Maize Technology Represented in the Sample...... 19 7 Disaggregation of Maize Technology Types by Zone ...... 20 8 Regression Analysis of Factors Affecting Maize Yields in Jimma and West Shoa .....22 9 Labor Use in Maize Production by Technology Group...... 24 10 Graduate Farmers’ Response to Recommended Maize Practices...... 24 11 Reasons Given by Maize Graduates in West Shoa for Not Continuing a Given Technology Component ...... 25 12 Tef Seed and Fertilizer Use in East Shoa by Different Plot Types...... 27 13 Seeding Rate and Grain Yields by Tef Seed Varieties Used in East Shoa ...... 28 14 Types of Tef technology Represented in the Sample...... 29 15 Regression Analysis of Factors Affecting Tef Yields in East Shoa (Model 1)...... 30 16 Regression Analysis of Factors Affecting Tef Yields in East Shoa (Model 2) ...... 30 17 Graduate Farmers’ Response to Recommended Tef Practices, East Shoa...... 32 18 The Most Common Reasons Given by Graduates in East Shoa for Not Continuing a Given Tef Technology Component...... 32 19 Financial Budgets for Tef (East Shoa) ...... 90 20 Financial Budgets for Maize (West Shoa) ...... 92 21 Financial Budgets for Maize (Jimma) ...... 94
LIST OF FIGURES
No. Title Page 1 Location of Survey Sites...... 3
LIST OF APPENDICES
No. Title Page
1 Examples of GMRP/MSU/SG2000/MOA Survey Questionnaires ...... 44 2 Methods for Yield Estimation and Area Measurement ...... 78 3 Financial Budgets ...... 89
v LIST OF ACRONYMS
AFR/SD/PSGE Africa Bureau’s Office of Sustainable Development, Productive Sector Growth and Environment Division AISE Agricultural Input Supply Enterprise CERES Crop-Environment Resource Synthesis CSA Central Statistical Authority, Ethiopian Government DA Development Agent DAP Diammonium Phosphate EAL Ethiopia Amalgamated Ltd. EARO Ethiopian Agricultural Research Organization EGTE Ethiopian Grain Trading Enterprise FA Farmer Association FDRE Federal Democratic Republic of Ethiopia GMRP Grain Marketing Research Project GPS Global Positioning System MOA Ministry of Agriculture, Ethiopia MOA/SG Ministry of Agriculture/Sasakawa Global 2000 demonstration program MSU Michigan State University NEP New Extension Program, Government of Ethiopia PLGY POLYGON program SG or SG2000 Sasakawa Global 2000 SC Service Cooperative TGE Transitional Government of Ethiopia USAID United States Agency for International Development
1 quintal = 100 kg
vi 1. INTRODUCTION
Ethiopia, one of the most densely populated countries in Africa, faces increasing food insecurity unless it can dramatically boost agricultural productivity per hectare. In 1993, the Sasakawa/Global 2000 Program (SG2000) began work in Ethiopia in partnership with the Ministry of Agriculture’s Department of Extension and Cooperatives (MOA). The objective of their joint program was to demonstrate the productivity increases that could be achieved when farmers were provided with appropriate research messages, adequate extension assistance, and agricultural inputs such as improved seed, fertilizers and agrochemicals, delivered on time at reasonable prices (SG2000 1996).
The MOA/SG2000 program provided participating farmers with improved inputs on credit in amounts that were sufficient for one-half hectare demonstration plots. Farmers also received intensive assistance from extension agents. Participants agreed to provide land for the demonstration plot and to pay a 25-50% down payment on the input package before planting, with the balance due after harvest. In 1995, the MOA/SG2000 demonstration program reached more than 3,500 farmers in 4 regions. During the same year the MOA launched the New Extension Program (NEP) funded and managed independently of the MOA/SG program, but based on SG2000 principles. By 1997, the NEP was managing the bulk of the demonstration plots (about 650,000) as the MOA/SG program reduced its direct participation in the demonstration program to about 2,000 plots.
Although the MOA/SG program is widely considered to be a success, no formal analysis has been carried out to determine the farm-level profitability of the program’s improved technology packages. In September 1997 MOA/SG agreed to collaborate with the Grain Marketing Research Project (GMRP) and the Department of Agricultural Economics at Michigan State University (MSU) hto analyze the financial returns to the recommended technology packages and determine the major factors affecting yield response.
1.1. Objectives
Specific research objectives include:
(1) Describe input use and yields for three distinct categories of plots:
(a) those cultivated as program plots using recommended technical packages for maize or tef, (b) those cultivated by the same set of program participants using their ‘traditional’ methods for growing maize or tef, and (c) those cultivated by past graduates of the SG maize or SG tef program who are using the technology of their choice.
1 (2) Evaluate the profitability of the three different plot types, with particular attention to the profitability of the recommended packages.
(3) Analyze the relative contribution to yield of (a) different types of technologies, (b) environmental factors, and (c) management practices.
(4) Describe the key challenges faced by the government in its effort to expand the SG/NEP program, with particular attention to how the expansion is being affected by government efforts to decentralize decision making and liberalize and privatize input markets. Specific research objectives included:
1.2. Methods
1.2.1. Sample Selection
The study examined the experiences of 1997 participants and graduates of the MOA/SG program for maize and tef in three zones located in the Oromiya Region: (1) West Shoa (maize); (2) Jimma (maize); and (3) East Shoa (tef) . Figure 1 shows the location of the survey sites. Maize and tef were chosen because they have been the major foci for the MOA/SG program. Within each area, the study team (in consultation with MOA/SG staff) chose woredas that were agroecologically homogenous and had a large number of current and graduate MOA/SG participants. All three zones are considered to have good to excellent agroecological conditions for tef and/or maize production. Woredas chosen for maize were Woliso and Wanchi (West Shoa Zone) and Dedo, Kersa, Seka Chekorsa (Jimma Zone). Ada Woreda in East Shoa Zone was selected for tef.
In the case of tef, the study included 1997 participants in the Ministry of Agriculture’s NEP program because the MOA/SG program focused on an experimental plant hormone to reduce lodging that had not yet been extensively tested. The NEP technology package is the same one used by MOA/SG in previous years: improved seeds, DAP and urea, and herbicides.
Within each zone, the team worked with local extension officials to construct a list of 1997 maize and tef program participants and graduates (i.e., farmers who had previously participated in the MOA/SG program, usually for two years). A total of 383 farmers were included in the sample.1 In each of the three zones, 40-80 current participants and 40-60 graduates were chosen. In several cases all listed farmers were included in the sample. When it was necessary to make a selection among farmers this was done randomly.
In East Shoa (tef) and Jimma (maize), current program participants usually had one or more plots where they were using “traditional” technology or technology combinations different from the
1Three households were subsequently dropped from the analysis because of missing yield data.
2 Location of Survey Sites Sites Survey of Location . Figure 1 Figure
3 program plot. The survey collected information about yield, area and input use for these plots, in addition to the program plots.
Table 1 summarizes the number of farmers in each category, and Table 2 describes key agroecological characteristics of each zone and the recommended technology package.
Table 1. Sample Composition Characteristic Study Sample Total number Current Traditional plots MOA/SG Crop woredas of farmers MOA/SG of MOA/SG program Zone participants participants graduates
East Shoa Tef Ada 120 60 60 60
West Shoa Maize Woliso, 152 94 0 58 Wanchi
Jimma Maize Dedo, Kersa, 111 72 47 39 Seka Chekorsa Source: GMRP/MSU/SG2000/MOA 1997 Survey, Supervisor Field Reports.
Table 2. Agroecological Characteristics and Recommended Technology Packages Characteristic Study Altitude Avg. Soils Major crops Recommended inputs per crop (meters rainfall half hectare plot Zone above sea (mm) level)
East Shoa Tef 1850 850 black, tef, wheat, 50 kg DAP,50 kg urea, gray, red pulses 17.5 kg improved seed, herbicide
West Shoa Maize 1600-2800 1420 red, gray tef, barley, 50 kg DAP, 50 kg urea, maize, 12.5 kg BH660 hybrid sorghum, maize seed, 80 cm pulses between rows, 50 cm between plants
Jimma Maize 1060-3000 1400- red tef, maize, Ditto 2000 wheat, sorghum, barley, pulses Source: GMRP/MSU/SG2000/MOA 1997 Survey, Supervisor Field Reports.
4 1.2.2. Questionnaire Design and Data Collection
Primary data collection was carried out in two rounds between October and December 1997. During these rounds the survey coordinator was assisted by three supervisors.2 Ten to fifteen enumerators were hired in each zone.3 Training and questionnaire pre-testing were carried out during the second and third weeks of October 1997. Primary data collection began the last week of October and continued through mid-December 1997.
Additional data on the fertilizer subsector were collected in August 1998 to better understand the characteristics of the rapidly evolving fertilizer subsector and estimate the costs of procuring and transporting fertilizer to survey areas. Data were collected through informal interviews with private sector importers, wholesalers and retailers, and zonal and woreda-level agricultural officials, and a review of secondary documents.
Yield estimation and area measurement: The objectives of the first round of data collection were to mark, harvest and weigh the grain from sample plots for yield estimation; complete a short questionnaire on soil characteristics and history of the field being sampled; measure field area; and geo-reference the field site using handheld global positioning system (GPS) equipment. Round one questionnaires are located in Appendix 1. An explanation of the methods used for yield and area estimation are included as Appendix 2.
Collection of demographic and input use data: The objective of the second round of data collection was to gather demographic data on the household; general information for the whole farm on area/input use for major crops and changes in livestock holdings over the past five years; and specific information for the program, traditional, or graduate plot regarding (a) dates of major field activities; (b) household and non-household labor inputs and costs; (c) amounts used and costs of other inputs (including animals, tractor, fertilizer, seed); (d) farmer perceptions of the importance of purchased inputs; (e) farmer assessment of risk factors affecting maize/tef yield during the past five years, including rainfall, hail/frost, wild animals, insects, plant diseases and weeds; (f) farmer opinion of extension services received; and (g) marketing/consumption of maize/tef over the last five years. Round two questionnaires can be found in Appendix 1.
Data entry, cleaning, and analysis: All primary survey data were entered by clerks at Addis Ababa University during December 1997 and January 1998. Data cleaning and preliminary analysis were
2 The two coordinators were a professor of economics at Addis Ababa University and a senior staff economist at GMRP. The three supervisors held bachelor or graduate degrees in agricultural economics or agronomy.
3 All enumerators had secondary school certificates.
5 carried out at Michigan State University in February 1998 by GMRP, Ethiopian Agricultural Research Organization (EARO), and MSU researchers. Data analysis was completed in September 1998.
6 2. CHARACTERISTICS OF PROGRAM PARTICIPANTS COMPARED TO THOSE OF TYPICAL AGRICULTURAL HOUSEHOLDS
There is a tendency for extension programs to introduce new technologies to farmers with better resources and skills first, expanding the program to others once the yield response and profitability of the technologies have been demonstrated. To assess the extent to which this is the case with the MOA/SG program in Ethiopia we have compared selected characteristics of program participants covered by our 1997 survey with those of typical agricultural households located in the same vicinity and covered by the CSA Agricultural Sample Surveys conducted in 1995/96 (Table 3). 4 The picture emerging from these comparisons is that the households in our survey of program participants cultivate more land (both absolutely and per capita), have larger household sizes (i.e., more available labor), appear to be wealthier (more livestock and traction animals), and have better educated household heads than the typical households described by the CSA data.
The CSA Agricultural Sample Survey shows that less than one percent of farmers in the general area of our 1997 survey used improved seed for any crop during 1995/96. Among CSA farmers in the general area of West Shoa, only 4% used fertilizer on maize; the percent increased to 27 for CSA farmers in the Jimma area. There is a longer history of fertilizer use on tef, however, and 82% of CSA tef farmers in the area around East Shoa reported using fertilizer in 1995/96.
Given the high fertilizer application rates associated with program recommendations, one would expect participants to apply fertilizer at a higher rate than other farmers, even when averaging over all their fields for a particular crop. This is supported by comparisons of average fertilizer used per hectare by our 1997 survey participants and farmers in the Oromiya Region covered by the 1996/97 CSA survey (Table 4).5 Program participants applied an average of 155 and 204 kg of fertilizer per hectare of maize in West Shoa and Jimma, compared to 86 kg/ha for CSA farmers using fertilizer on maize in the Oromiya Region. There was also a large difference between program participants in our survey and CSA farmers using fertilizer on tef in the Oromiya Region: 184 kg/ha versus 110 kg/ha. Both the participant and the CSA ‘user’ application rates far exceed the average quantities of fertilizer applied to the total maize and tef area (fertilized and unfertilized) in Oromiya: 24 kg for maize and 73 kg for tef (Table 4).
4As the CSA Survey collected relatively few observations in any given woreda, the averages for the CSA comparisons are based on households in the woredas covered by our 1997 survey plus woredas that were located close to those in the survey (see full list in Appendix 4).
5 All three participant survey zones are located in the Oromiya Region; this is the most disaggregated level of fertilizer use data we were able to get from CSA for 1996/97.
7 These findings have implications for the expansion of the NEP to farmers who are more resource constrained and less accustomed to using improved inputs (see Section 5 for further discussion).
Table 3. Selected Characteristics of Participant Households Versus the Broader Population of Agricultural Households East Shoa (tef)1 West Shoa (maize)2 Jimma (maize)3 Ethi- opia Program CSA Program CSA Program CSA partici- sample partici- sample partici- sample pants farmers pants farmers pants farmers Mean area cultivated (ha/household) 3.0 2.0 2.6 1.5 2.1 1.0 1.0 Mean population (persons/household) 7.1 5.7 8.7 5.5 7.4 5.0 5.2 Mean hectares cultivated per capita4 0.62 0.36 0.34 0.31 0.31 0.21 0.21 Percent of literate household heads 95 22 85 36 95 19 22 Mean Livestock Units per household5 5.1 4.7 5.4 4.0 4.7 3.1 3.5 Mean number of draft animals per 2.7 1.9 2.3 1.7 2.3 1.5 1.1 household Sources: GMRP/MSU/SG2000/MSU 1997 data; our analysis of CSA Agricultural data base for meher crops, 1995/96. 1205 households from the CSA survey were used in the East Shoa analysis which covered Boset, Lome, Ada, Dugda, Arsi Negele, Shashemene, Seraro, and Akaki woredas. 2221 households from the CSA survey were used for the West Shoa analyis which covered Woliso, Becho, Ambo Zuria, Dano, Wonchi, and Dendi woredas. 3478 households from the CSA survey were used in the Jimma analysis which covered Limu Seka, Limu Kosa, Sokoru, Tiro Afeta, Kersa Mana, Goma, Gera, Seka Cherkorsa, Dedo, and Omanada woredas. 4 Calculated at the household level first, then averaged across households to give each household equal weight in the calculation; note that the same result will not be obtained when dividing sample mean area by sample mean population. 5 Calculated using following weights: cattle=1, sheep/goats =.5, horses/mules = .7.
8 Table 4. 1997 Fertilizer Use of Participants Versus the Broader Agricultural Population Program CSA Sample Farmers participants Fertilized fields All fields
(kilograms of fertilizer product per hectare) E. Shoa, fertilizer use on tef 184 W. Shoa, fertilizer use on maize 204 Jimma, fertilizer use on maize 155 Oromiya Region maize 86 24 average tef 110 74 National average maize 76 26 tef 99 54 Source: GMRP/MSU/SG2000/MOA 1997 Survey; CSA Statistical Bulletin 171, 1996/97.
9 3. YIELD RESULTS AND FINANCIAL ANALYSIS
Average maize and tef yields across all survey plots far exceeded national and regional averages. Yields for plots where farmers used high-input technologies were much greater than yields for plots using low-input technologies. Greater heterogeneity than anticipated within given plot types (due to variation in types and levels of inputs) produced mixed yield performance across the three plot types. Profitability was high, however, across the full range of plot types, with program and graduate plots for maize out-performing traditional plots, but no significant differences among the three types of tef plots.
Table 5 (see page 15) summarizes these results, presenting yields and financial enterprise budgets by crop, study zone, and plot type. The budgets also break each plot-type group into terciles (by crop yield) in order to examine the variation within each plot-type. We discuss details of the yield results in Section 3.1. and of the financial analysis for the various plot-types and terciles in Section 3.2.
3.1. Yield Results
The discussion here focuses on a presentation of the yield results for survey plots, how survey yields compare to national and regional averages, and the extent to which yields differ across plot-types. A detailed analysis of factors responsible for yield differences across technologies, zones, and plot-types is postponed until Section 4.
3.1.1. Maize
Jimma. Average maize yields on program and graduate plots in Jimma were 5.5 and 6.8 tons/ha, respectively. These yields were more than double the 1996/97 national and Oromiya Region averages (1.9 and 2.1 tons/ha, respectively). Program graduates in Jimma achieved yields that were 1.3 tons/ha higher than program participants although they used essentially the same technology (improved seed and approximately the same amounts of DAP and urea). This suggests the existence of a “learning curve” with farmers becoming more proficient in the application of improved technology and management techniques over time.
Traditional plots in Jimma were generally cultivated with local seed and DAP. The mean yield for these plots was 2.8 tons/ha. The large yield differences (more than 2 tons) between the traditional plots and those cultivated with the improved technologies used on the program and graduate plots clearly demonstrates the role improved inputs play in augmenting maize yields.
10 West Shoa. Program participants in this zone obtained the same average yields as participants in Jimma -- 5.5 tons/ha. The graduates did not do as well, however, as those in Jimma. West Shoa graduates were split into two groups: more than half reverted to traditional methods (local seed without fertilizer) while the rest continued to use improved seed and recommended levels of fertilizer. Those using low-input techniques achieved average yields of 3.8 tons -- far exceeding national and regional averages but still significantly lower than the 5.8 ton/ha yields obtained by those using the improved technologies.6
3.1.2. Tef
Average grain yields for tef were similar on all plots (program, graduate, and traditional) -- about 1.5 tons/ha or 50 % greater than national and regional average yields of 0.9 ton/ha and 1 ton/ha, respectively (FDRE 1997). The lack of a significant difference across plot types is due in large part to the use of both improved seed and fertilizers on both traditional and graduate plots. Further complicating the comparison is the flexibility allowed to participants in the 1997 tef program -- they were allowed to partially adopt the recommended package by using different levels of fertilizer (often less than recommended rates of urea) or substituting a different variety of seed.7 Although farmers often referred to these seeds as “local” varieties, according to Ethiopian Agricultural Research Organization (EARO) researchers, in the East Shoa region these are more likely to be saved seed from improved varieties that were previously distributed (e.g., DZ-Cross-37 or DZ-01-196) than traditional tef varieties.
Production of tef straw, which is becoming an important commercial crop, ranged from 2 to 2.2 tons/ha across survey plots. We are not aware of any national or regional statistics on tef straw yields that can be used for comparative purposes.
In summary, unlike the maize areas, most of the tef farmers surveyed had previously adopted the key components of the improved technology package for use on all tef plots. The use of improved technology allowed farmers to achieve yields that were substantially higher than national and regional averages.
6 More details on the different types of technologies used by West Shoa graduates and the yield impacts is presented in Section 4.
7 This type of substitution was not permitted by the MOA/SG program but was introduced as responsibility for demonstration plots shifted from MOA/SG to NEP.
11 3.2. Financial Analysis
3.2.1. Data and Methods Used
Data. All plot-level data came from our survey. Wholesale prices from main market towns in each zone (E. Shoa, West Shoa, Jimma) were obtained from the Ethiopian Grain Trading Enterprise (EGTE) and adjusted to farmgate prices using price data from our survey. The cost of fertilizer, seed, and pesticides was fixed for program participants. For other groups, farmer-reported costs were used. Maize participants paid no interest on seed, fertilizer and pesticide inputs while tef participants and graduate and traditional farmers who received either maize or tef inputs through the NEP program paid 10% interest annually.8 Information reported by farmers was used to value or calculate depreciation for inputs such as animal traction, tools, sacks, and family and hired labor.
Methods. Two measures of outcome are used in the financial analysis to evaluate profitability: net returns per hectare and net income per labor day.9 The latter measure is compared to wage rates (opportunity costs of labor) in the study areas to assess the relative attractiveness of the technology under varying yield and price levels.
Net income per hectare and per labor day are calculated for six different output price and yield scenarios identified as cases (a) through (f). Income is calculated for output prices prevailing in: (a) January 1998, (b) April-May 1998; and (c) August 1998 to assess potential gains from storage. In each case, interest payments are adjusted to reflect the length of the loan period and gross revenue is adjusted to reflect the opportunity costs associated with selling at different times of the year. During 1998, actual maize and tef prices rose throughout the season. Net income per hectare and per labor day are also calculated for hypothetical drops in output prices of (d) 25% and (e) 50% from their January 1998 values. In each of scenarios (a) through (e), maize is assumed to lose 2% of grain weight per month through storage losses, but no storage loss is assumed for tef. Scenario (f) calculates returns to maize if storage losses are reduced by half through the use of storage chemicals.
Key results from the financial budgets are presented in Table 5 at the end of this section (page 15). The complete budgets are included as Appendix 3. A review of these budgets leads to the three key conclusions about maize and tef profitability that are presented in Sections 3.2.2. through 3.2.4.
8 As a non-profit organization SG2000 is not allowed to earn income, hence they do not charge interest to farmers who participate in the MOA/SG demonstration program.
9 All financial analysis results are reported in Ethiopian birr (EBr). The exchange rate during the 1997 crop and marketing year was US$1.00=EBr6.70
12 3.2.2. Improved Technology is Profitable for both Maize and Tef, Even if Output Prices Decline by 25% or 50%
For both tef and maize, net income per hectare and per labor day are high (though variable between terciles) for farmers using improved technology under all price scenarios. Net income and returns per labor day increase with yield, but variable costs are covered and returns to family and mutual labor far exceed average daily labor rates (3-6 birr per day) for every program and graduate tercile.
In Jimma, net returns from program plots were double those from traditional plots, although for the traditional plots returns were also positive and returns per labor day exceeded the average wage rate. Returns per labor day were 1/3 higher on program plots compared to traditional plots, 15 birr/day versus 11 birr/day at January output prices. Jimma graduates received the highest net returns per hectare and per labor day in the zone. Graduates had significantly higher yields than program participants, while costs remained about the same. In West Shoa, net maize income per hectare was similar for program participants and graduates (less than half continued to use improved inputs), but net income per labor day was 3-4 birr/day higher for program participants.
As discussed above, differences between program types in East Shoa are blurred because tef farmers used improved varieties and fertilizer on all tef plots (program, traditional, graduate). While yield differences between program types were not significant, net income per hectare was significantly higher for traditional and graduate plots than for program plots. Net returns per labor day were highest for traditional plots because fewer family/mutual labor days were used. In general, returns per labor day for tef in East Shoa (14-57 birr/day) were far higher than returns per labor day in maize production (9-23 birr/day in West Shoa and 6-22 birr/day in Jimma).
If output prices drop by 25% from January 1998 price levels, net income per hectare and per labor day drop substantially, but in all zones net income/ha is positive and returns per labor day exceed average wage rates (Table 5). Even if output prices decline by 50%, net income per hectare and per labor day remain positive for all terciles and zones except the first tercile of program participants in Jimma. Returns per labor day remain at or above the average wage rates for the second and third yield terciles in all groups.
3.2.3. Gains from Storage and Use of Storage Insecticide
There were significant gains from storing tef for later sale in the 1998 season. Farmgate grain prices rose by 23% and straw prices doubled between January and August 1998. Farmers can increase net income by more than 40% by selling in August instead of January.
13 Maize prices also rose significantly between January and August 1998 in West Shoa (29%) and Jimma Zones (72%). Unlike tef, untreated maize deteriorates rapidly in storage. In Jimma, the price rise over time was steep. Even accounting for storage losses net income per hectare and per labor day rose by 44-46% between January and August. In West Shoa the price rise was less dramatic, and farmers lost 4-5% of net income if they stored and sold maize in August instead of January.
None of our survey farmers reported using storage insecticide following the 1997 production year. If Jimma and West Shoa farmers used insecticide and storage losses were reduced by half10, net income per hectare and per labor day would increase by an estimated 10-17%11 for all groups in West Shoa, and from 9-13% in Jimma, after the costs of storage insecticide are taken into account.
3.2.4. Improved Seed and Fertilizer Costs Represent 50-80% of Total Costs
Improved seed and fertilizer are by far the biggest cost component in the financial enterprise budgets. In East Shoa the costs of improved seed and fertilizer represent 52-56% of total costs (exclusive of family labor). They are 56-66% and 60-80% of total costs in West Shoa and Jimma, respectively. This analysis suggests that even small reductions in the farmgate cost of fertilizer and seed (e.g., by reducing transport and other marketing costs) could significantly increase farm profits.
10This is a conservative estimate. Recent research suggests that the application of storage insecticide can reduce storage losses to 2-13% of grain weight over a 5-9 month period (Abraham et al. 1993).
11Assumes farmers sell in August 1998.
14 Table 5. Summary of Key Yield and Profitability Results by Zone, Plot Type, and Yield Tercile
(a) MOA/SG/NEP Program Plot (b) Traditional Plot (c) Graduate Plot Yield Tercile Commodity/ Mean/ Mean/ Mean/ Zone Budget Item 1 2 3 Total 1 2 3 Total 1 2 3 Total MAIZE/ YIELD (ton/ha) 3.9 5.5 7.2 5.5* na na na na 2.9 4.6 6.9 4.8* WEST SHOA n used in calculations 30 31 31 92 na na na na 19 19 19 57 TOTAL FAMILY/MUTUAL LABOR DAYS 2/ (adult equiv. days/ha) 164 143 167 159 na na na na 177 240 200 206 FINANCIAL ANALYSIS a. Net Income Jan 98 Price 1531 2657 3850 2695 1616 2690 3695 2672 April-May 98 Price 1289 2315 3399 2349 1432 2402 3252 2368 August 98 Price 1446 2537 3691 2573 1550 2585 3517 2556 Aug 98 w/ storage insect. 1658 2838 4087 2877 1711 2836 3893 2819 Jan 98 Price - 25% 888 1747 2651 1774 1129 1931 2552 1876 Jan 98 Price - 50% 244 836 1452 854 643 1172 1409 1080 b. Net Income per Family and Mutual Labor Day Jan 98 Price 9 19 23 17 9 11 19 13 April-May 98 Price 8 16 20 15 8 10 16 12 August 98 Price 9 18 22 16 9 11 18 12 Aug 98 w/ storage insect. 10 20 25 18 10 12 20 14 Jan 98 Price - 25% 5 12 16 11 6 8 13 9 Jan 98 Price - 50% 2 6 9 5 4575
15 (a) MOA/SG/NEP Program Plot (b) Traditional Plot (c) Graduate Plot Yield Tercile Commodity/ Mean/ Mean/ Mean/ Zone Budget Item 1 2 3 Total 1 2 3 Total 1 2 3 Total MAIZE/ YIELD (ton/ha) 4.1 5.4 6.9 5.5** 1.6 2.7 4.1 2.8** 5.1 6.9 8.4 6.8** JIMMA n used in calculations 22 24 23 69 15 16 16 47 13 13 13 69 TOTAL FAMILY/MUTUAL LABOR DAYS (adult equiv. days/ha) 101 115 106 107 77 86 70 78 98 110 108 106 FINANCIAL ANALYSIS a. Net Income Jan 98 Price 1385 1963 2757 2044 459 933 1667 1030 1740 2627 3257 2543 April-May 98 Price 1495 2108 2941 2191 500 1002 1777 1104 1862 2798 3476 2711 August 98 Price 2087 2892 3936 2939 726 1385 2370 1508 2579 3778 4668 3674 Aug 98 w/ storage insect. 2321 3203 4329 3298 815 1537 2605 1668 2868 4171 5145 4060 Jan 98 Price - 25% 847 1250 1853 1323 252 584 1128 662 1076 1724 2170 1656 Jan 98 Price - 50% 309 538 949 602 46 235 589 293 412 821 1075 768 b. Net Income per Family and Mutual Labor Day nh Jan 98 Price 11 14 20 15 6 9 18 11 14 18 22 18 April-May 98 Price 12 15 21 16 6 10 20 12 15 19 23 19 August 98 Price 17 20 28 22 9 14 26 16 21 26 31 26 Aug 98 w/ storage insect. 19 23 31 24 10 16 29 18 23 29 34 29 Jan 98 Price - 25% 7 9 13 10 3 6 12 7 9 12 15 12 Jan 98 Price - 50% 3 4 7 5 12733676
16 (a) MOA/SG/NEP Program Plot (b) Traditional Plot (c) Graduate Plot Yield Tercile Commodity/ Mean/ Mean/ Mean/ Zone Budget Item 1 2 3 Total 1 2 3 Total 1 2 3 Total EAST GRAIN YIELD (ton/ha) 0.9 1.3 2 1.4 0.8 1.4 1.9 1.4 1.0 1.4 2.0 1.5 SHOA/TEF STRAW YIELD (ton/ha) 2.2 2.1 2.3 2.2 2.2 1.9 2.0 2.0 2.4 2.0 1.8 2.1 n used in calculations 20 20 20 60 20 20 20 60 21 18 21 60 TOTAL FAMILY/MUTUAL LABOR DAYS (adult equiv. days/ha) 69 65 56 63 52 65 55 57 92 73 67 78 FINANCIAL ANALYSIS a. Net Income Jan 98 Price 991 1718 2994 1904 1209 1945 3116 2091 1229 2097 3247 2194 April-May 98 Price 1006 1891 3224 2087 1357 2115 3332 2269 1395 2276 3461 2381 August 98 Price 1314 2553 4166 2787 1837 2785 4232 2952 1955 2958 4384 3105 Jan 98 Price - 25% 476 991 1930 1135 721 1200 2094 1339 649 1348 2185 1395 Jan 98 Price - 50% -40 264 866 366 233 454 1072 587 70 598 1122 596 b. Net Income per Family Jan 98 Price 14 26 54 30 23 30 57 37 13 29 49 28 April-May 98 Price 15 29 58 33 26 33 61 40 15 31 52 31 August 98 Price 19 39 74 44 35 43 77 52 21 41 65 40 Jan 98 Price - 25% 7 153518141938247 193318 Jan 98 Price - 50% -1 4 16 6 5 7 20 10 1 8 17 8
Source: Field data from 1997 GMRP/MSU/AAU/SG2000/MOA Survey. See Tables 19-21 in Appendix 3 for detailed budgets and notes. na= not applicable * yield differences between MOA/SG and graduate plots were significant at the 95% level. ** yield differences between MOA/SG and traditional plots belonging to the same household were significant at the 95% level; yield differences between MOA/SG and traditional plots, and MOA/SG and graduate plots, were also significant at the 95% level.
17 4. FACTORS AFFECTING CROP YIELDS AND TECHNOLOGY CHOICE
In Section 3 we examined the financial profitability of three plot types by zone and crop: program participants’ plots using the recommended technology package, traditional plots grown by program participants, and plots managed by graduates using the technology of their choice. The financial profitability was found to be highly correlated with the grain yield, which in turn was influenced by the seed and fertilizer inputs used by the farmers.
In the case of maize, the average yields obtained on program and graduate plots were significantly higher than those obtained on traditional plots. However, there was a wide variation in the crop yields within a given plot type, implying that the type and level of inputs used on these plots, especially on the traditional and graduate plots, were not always homogeneous for a particular crop and zone. For example, some maize graduates in West Shoa reverted to traditional production methods (local seed and no fertilizer) while others opted to continue using inputs similar to the recommended package; among the traditional tef plots, some farmers used improved seed, DAP and urea while others used only improved seed and DAP.
The objective of the analysis in this section is to identify the key factors affecting yields and to quantify the relative yield impact of these factors. To accomplish this we grouped the plots by types and levels of seed and fertilizer used rather than by the original sampling criteria (type of farmer and plot). This permits us to better control for the various technologies when examining the influence of other factors. The other factors examined fall into two broad categories: (1) exogenous factors that farmers respond to but cannot completely control such as rainfall, soil types, disease and pest attacks and (2) endogenous factors linked to management practices such as timing of critical operations, amount of labor used, and number of plowings.
We proceed by (1) describing the principal technologies used and their yields, (2) presenting econometric results that identify and quantify key yield determinants, (3) discussing descriptive statistics for factors that are correlated with the use of improved technologies and, therefore, thought to encourage their adoption, and (4) describing graduate farmers’ decisions about continued use of the high-input technologies. Section 4.1 discusses these topics for maize and Section 4.2 does so for tef.
4.1. Factors Affecting Maize Yields
4.1.1. Types of Maize Technologies and Their Yields
We grouped maize plots into the four technology types described in Table 6. We consider technology types 1 and 2 as ‘low-input’ technologies and types 3 and 4 as ‘high-input’ technologies. The only difference between the two high-input technologies is the level of fertilizer applied. In both high-input groups the amount of DAP and urea applied are equal within a given plot but not across plots. For plots in technology type-3 application rates range
18 Table 6. Types of Maize Technology Represented in the Sample Number of Average fertilizer plots using Average applied (kg/ha) a given yields Type of Maize Technology technology (kg/ha) DAP UREA
(1) Local seed, no fertilizer 37 3639 0 0
(2) Local seed plus DAP 44 2918 103 0
(3) Improved seed plus DAP and urea at < 103 5910 86 86 recommended dose
(4) Improved seed plus DAP and urea at >= 118 5786 119 119 recommended dose Source: Calculated from GMRP/MSU/SG2000/MOA survey data. from 50 to 98 kg of each product per hectare; for plots in technology type-4 the rates range from the recommended level of 100 kg per hectare to 208 kg. The median for type-4 plots is 113 kg/ha, hence at least 50% of the farmers in this group are applying fertilizer at approximately the recommended level.
A comparison of yields for each technology group shows statistically significant differences among all the groups but technology types 3 and 4. The lack of significant difference between these two high-input groups is not unexpected as the difference in fertilizer application rates is not very large. We were surprised, however, to find that the lowest technology level (local seed, no fertilizer) performed better than the next higher technology (local seed plus DAP). This result in the overall data is entirely attributable to 33 plots in the West Shoa zone cultivated by graduate farmers, as illustrated by the zone-disaggregated data presented in Table 7.
Although there is substantial variability in the yields obtained by this group of farmers (1.9 to 6.8 tons/ha), the overall average was about 3.9 tons/ha -- more than double the yields for the comparable technology in Jimma (1.8 tons, but only 4 observations) and statistically higher than the yields for the next higher technology in both Jimma (2.9 tons based on 43 observations) and West Shoa (3.5 tons, but only 1 observation). We have conducted numerous checks on the data to verify that these results are not due to measurement errors. We have also conducted numerous cross-checks using both quantitative and qualitative variables in a search for clues as to why this low-input technology performed so well in West Shoa during 1997.
We found no evidence that there was measurement error in the yield estimates, but have not yet found any clear explanation for the good performance of this group of graduates. The leading
19 Table 7. Disaggregation of Maize Technology Types by Zone Jimma West Shoa
Number Average Average kg/ha Number Average Average kg/ha Technology type of plotsa yield of plotsb yield kg/ha DAP UREAkg/ha DAP UREA
(1) Local seed with no 4 1835 0 0 33 3858 0 0 fertilizer
(2) Local seed plus DAP 43 2905 100 0 1 3480 208 0
(3) Improved seed plus 58 6007 87 87 45 5784 86 86 DAP and urea at < recommended dose
(4) Improved seed plus 50 5922 116 116 68 5685 121 121 DAP and urea at >= recommended dose Source: Calculated from GMRP/MSU/SG2000/MOA survey data. a For Jimma, all the plots for technologies 1 and 2 were traditional plots; among the 58 type-3 plots, 24 were graduates and 34 SG participants; among the 50 type-4 plots 15 were graduates and 35 SG participants. b For West Shoa, the type-1 and type-2 plots were all graduate plots; among the 45 type-3 plots were 11 graduates and 34 SG participants; among the 68 type-4 plots there were 10 graduates and 58 SG participants. Two farmers in West Shoa using intermediate technologies (local seed plus DAP and urea and improved seed with no fertilizer) were excluded from the analyses. hypotheses are: (1) the local variety performs better under poor rainfall conditions (31of the 34 farmers indicated that the total quantity and/or the distribution of rainfall was poor in their village during the 1997 season) and (2) there may have been some residual fertilizer effect from the previous year because most (25 of 34) of these farmers applied recommended levels of DAP and urea on the same plots in 1995/96. Other possible mitigating factors might be differences in land quality. Farmers in this group were more likely than those in other groups to report level fields (vs. gullied or sloped fields) and more likely to report high fertility soils.
4.1.2. Econometric Analysis of Maize Yield Determinants
As our objective is to identify and evaluate the relative importance of different factors affecting maize yields we planned to develop a model with a disaggregated set of input, crop management, and environmental variables that combined observations from all the available technology types. Unfortunately, the variability in the data did not permit us to do this. The main problem was very high correlation, among the input variables in particular, but also among some of the other variables. For example, local seed is used exclusively with the two low-input technologies while improved seed is used exclusively with both DAP and urea. This makes it impossible to separate
20 the seed effect from the fertilizer effect. The application of DAP and urea in equal quantities for all the high-input plots further increases problems of multicollinearity, making it impossible to evaluate the relative contributions of urea and DAP. Similar problems were encountered with some of the environmental and management variables as some tended to be highly correlated with each other and frequently correlated with the dummy variable used to differentiate the two maize production zones.
The data do, however, permit us to model the yield impact of the different technology types, and some environmental factors and key management practices. Table 8 summarizes the results by zone.
Contribution of technology factors on maize yields. Both in West Shoa and Jimma the high-input technologies explain a large amount of the yield variation observed across plots. In Jimma the technology types 3 and 4 (improved seed with both DAP and urea) yielded almost 3.5 tons/ha more than the technology type 1 using local seed without fertilizer. In West Shoa, the high-input technologies (type 3 and 4) also performed better than the low-input ones, but the difference was substantially less than in Jimma -- about 1.8 tons versus 3.5 tons increase when moving from low- to high-input technologies. This is due primarily to the very high yields obtained by the graduate farmers who opted to use local seed without fertilizer (see discussion in section 4.1.1 above).
Contribution of environmental factors on maize yields. An important environmental factor contributing to the maize yield variability in Jimma was soil type -- red soil produced almost 1.5 tons more output than gray or black soils in this zone. Fortunately 95% of farmers planted maize on red soils. Several soil variables were also significant in explaining the yield variability in West Shoa. Farmers in West Shoa who declared that their soils were poor (only 20% of the West Shoa sample) obtained yields about 700 kg lower than farmers having declared soil of medium or high fertility. Red soil was associated with yield increases of about 400 tons per hectare. But unlike in Jimma, red soils did not significantly impact the yield variability. About 87% of West Shoa farmers planted maize on red soils.
Contribution of management practices on maize yields. Certain management practices also affect yields. In Jimma, yields declined by about 200 kg for each week a farmer deviated from the optimal planting week. The average deviation was 1.3 weeks, suggesting an average loss due to late planting of 260 kg/ha. Each centimeter of deviation from recommended row distances resulted is a yield loss of about 35 kg/ha. In Jimma, the average deviation was 9 centimeters, resulting in an average yield loss due to poor row spacing of 315 kg/ha. The last management variable in the Jimma model that appears to be related to yields (.07 level of significance) is the number of plowings. Farmers in Jimma zone who plowed their fields more than 4 times before
21 Table 8. Regression Analysis of Factors Affecting Maize Yields in Jimma and West Shoa Jimma (Adj R2 0.55) West Shoa (Adj R2 0.30) Variables Coef. SE Coef. T Sig T Coef. SE Coef. T Sig T Constant 1021 895 1.14 0.26 3723 588 6.33 .00 Tech2 dummy: 855 725 1.18 0.24 Only one case of this technology in Local seed plus DAP. the zone; omitted from model. Tech3 dummy: 3459 727 4.76 0.00 1843 350 5.27 .00 Improved seed, lower than recommended fertilizer use. Tech4 dummy: 3532 731 4.83 0.00 1803 317 5.68 .00 Improved seed, recommended or higher fertilizer use. Diffrowdistance: -35 14 -2.57 0.01 -29 17 -1.64 .10 Absolute deviation in cm. from ideal distance. Diffplantdistance Not a significant variable in this -17 9 -1.79 .08 Absolute deviation (cm) zone. from ideal plant distances Diffplantdate: -194 90 -2.15 0.03 Not a significant variable in this Absolute deviation (weeks) zone. from ideal planting date Diffweeddate Not a significant variable in this -124 79 -1.58 .12 Absolute diviation (weeks) zone. from ideal weeding date. Plowing dummy 563 309 1.82 0.07 Not a significant variable in this 1 represents >4 plowings zone. Red soil dummy 1448 533 2.71 0.01 432 372 1.16 .24 1=redsoil Soil fertility assessmect Not a significant variable in this 744 314 2.37 .02 dummy zone. 1= medium to high fertility Source: Estimated from GMRP/MSU/SG2000/MOA survey data.
22 planting got about 550 kg more per hectare than those who plowed fewer times -- 84% plowed at least 4 times so this fairly substantial yield loss affected only 16% of farmers.
In West Shoa, the failure to follow recommendations concerning planting distances (both between plants and between rows) reduced yields by about 15-30 kg per centimeter (Table 8). The average deviation from the recommendation was 18 centimeters for plant spacing and 10 centimeters for row spacing, resulting in an average loss of 280 kg/ha for planting distance errors and 300 kg/ha for row spacing errors. Failure to weed at the optimal number of weeks following planting reduced yields by about 125 kg per week. The average deviation from recommended weeding time was 1.6 weeks, resulting in an average loss of 200 kg/ha.
4.1.3. Descriptive Analysis of Factors Related to Maize Technology Choice
These regression results present a rough picture of the factors that appear to have had the most important influence on maize yields during the 1997 production season, but it is important to note the the models still explain only a portion of the variability in the yield data -- 55% for Jimma and 30% for West Shoa.
We collected data on a much wider range of variables representing both agroecological variables and management practices, but attempts to incorporate a wider range of these variables in our models were thwarted by a substantial amount of correlation among the variables and a fairly high degree of compliance with recommended practices, which meant that there was not much variation in the data for some variables of interest.
The set of variables concerning labor inputs is a case in point. These variables were not significant in the multivariate yield models we estimated, but there do appear to be some important differences in labor use when households are classified by technology type. Table 9 summarizes the results of bivariate analyses conducted to test differences in labor use by type of technology. Farmers in the low-input technology groups used significantly less labor per hectare than those in high-input groups. Interestingly, the major differences seem to come from the access of the high- input farmers to mutual and hired labor, as there is no statistically significant difference in the amount of family labor used per hectare. This suggests that use of high-input technologies requires not only more resources for the purchase of inputs but also more resources to attract non-family labor.
4.1.4. Graduate Farmers’ Decisions Concerning Choice of Maize Technology
Graduate farmers in Jimma appear to be convinced that the recommended maize technologies and production practices are worthwhile as 100% of the graduates surveyed in the zone are continuing to use the new technologies (under the NEP program). This is not the case in West Shoa.
23 Table 9. Labor Use in Maize Production by Technology Group Low-input High-input Mean difference between low- Type of Labor technology technology input and high-input (Types 1 and 2) (Types 3 and 4) technology (Level of significance)
Total labor days/hectare (8 87 122 .00 hrs/day)
Total weeding days/hectare 28 46 .00
Number of plowings 4.8 5.2 .00
Days of mutual labor/hectare 14 41 .00
Days of hired labor/hectare 9 14 .04
Days of family labor/hectare 65 66 .78 Source: Calculated from GMRP/MSU/SG2000/MOA survey data.
Although the analysis of financial profitability in Section 3 shows that these high-input technologies are also profitable for farmers in West Shoa under a wide range of yield and price assumptions, 60% of the graduate farmers in this zone did not use the improved technologies in 1997. Table 10 summarizes the behavior of graduates in both zones with respect to a number of key recommendations. It is noteworthy that the 60% of West Shoa farmers who abandoned the technology abandoned it in its entirety, dropping both the improved seed and all fertilizers.
Table 10. Graduate Farmers’ Response to Recommended Maize Practices West Shoa Jimma Number of Graduates 58 39
Technology component (percent continuing a given technology)
Improved seed 36 100
Recommended seeding rate 38 100
Planting in rows 38 100
Recommended rate of DAP 38 100
Recommended rate of Urea 36 100
Recommended row spacing 36 100
Recommended spacing between plants 38 100 Source: Calculated from GMRP/MSU/SG2000/MOA survey data.
24 The reasons for discontinuing the technology package in West Shoa throw some light on the agronomics and the economics of maize production in the zone (see Table 11). The predominant reason for discontinuing both the seed and fertilizer technology was the unsatisfactory yield response in the previous year -- a year of relatively good rains but more hail damage than usual. Many farmers simply did not see enough difference in yield to justify the added risk and expense of improved seed (hybrids) and fertilizer. Some noted that they had suffered major crop losses due to hail and animal damage the previous year but were still obliged to repay the credit. Two such farmers mentioned having sold their oxen to take care of the debt. Another farmer claimed he was forced to sell his maize early at very low prices in order to reimburse the credit -- something he did not want to be forced into doing again.
Table 11. Reasons given by maize graduates in West Shoa for not continuing a given technology component % of graduates not using Common reasons for discontinuing a given technology the recommended practice Technology component component for a given reason
Improved seeds Unsatisfactory yield from previous year 54 Returned improved seed to distributor due to delayed rains 24 Inability to pay for the whole package 14
Seeding rate Broadcast seeding is easier and requires less labor 72
Row planting Demands fertilizer which is unaffordable 39 Requires more labor and time 28 Believe that broadcast is better 11
DAP rate Applying fertilizer has not increased yield 38 High cost of fertilizer 28 Shortage of rain 17
Urea rate High cost of fertilizer 27 Applying fertilizer has not increased yield 24 Shortage of rain 16
Row spacing No reasons given, but local seed is often broadcasted and considered a more rapid method of planting in years when the rains are late
Plant spacing Ditto Source: Calculated from GRMP/MSU/SG2000/MOA survey data.
Another frequently used explanation for abandoning the technology in West Shoa was the delayed start of the rains in 1997, which substantially increased the risk of not covering input costs. Farmers never specifically said that they believed local varieties performed better in years of poor rainfall; most alluded to the desire to reduce the risk of not being able to reimburse credit and the fact that the late planting could be done more quickly by broadcasting local varieties than
25 by planting improved varieties in rows. In general, farmers’ responses suggest that risk aversion is compelling them to dis-adopt improved seed and fertilizer technologies. Another frequently mentioned constraint was the high cost of the inputs, particularly fertilizer, and an inability to mobilize enough cash for the down payment. A less common explanation (2 cases) was a land constraint --i.e., the farmer could not find another farmer willing to combine land resources to meet the minimum plot size of 0.5 hectares.12 In sum, given the relatively poor rains in 1997 season, many farmers felt more secure using traditional maize technologies which are much less expensive and, therefore, less risky.
However, the follow-up report of the field supervisors in the West Shoa zone gives an impression that a majority of graduates who had returned to traditional practices in 1997 for a variety of reasons had re-adopted the technology package in the following year. Perhaps the strong evidence of good financial returns to the high-input technology enjoyed by other graduates who had continued the technology package in 1997 made many graduates realize the benefits of hybrid maize seeds and fertilizer even in poor rain conditions.
4.2. Factors Affecting Tef Yields
4.2.1. Types of Tef Technologies and Their Yields
Unlike maize, the composition of tef technology was less distinct across the program, traditional and graduate plots surveyed in the East Shoa region. All the 180 farmers surveyed (60 from each plot type) reported using improved seeds and only 6 reported no fertilizer application at all. Despite this uniformity in the use of improved seeds and fertilizer, tef yields across all the plot types ranged from about half a ton to 3.3 tons per hectare.
The variation in the yield levels compelled us to look further into “the recommended tef technology package” and the alterations in the package made by the farmers on their traditional and graduate plots. As noted earlier, the tef technology package recommended and distributed to farmers for the extension plot consisted of 35 kg/ha improved seeds (Qoladma or Magna), 100 kg/ha each DAP and urea, and herbicides. However, the only mandatory purchase required for farmers to participate in the NEP program was the equal quantities of DAP and urea. Unlike the maize technology package, the participants in the tef extension program in East Shoa were allowed to use their own improved seeds saved from previous harvests (in place of the distributed seeds, Qoladma and Magna) and any quantity of herbicides. As indicated in Table12 almost 50% of the current participants used their own improved seeds retained from previous harvests. Also, unlike the maize technology package, the seed component of the tef technology
12 Note that the minimum size plot is only a requirement for participation in the demonstration plot program but the only way MOA/SG 2000 graduates could get access to maize improved seed was to participate in the NEP program -- hence the mention of a land constraint.
26 package was not exclusively for the current participants. Improved seeds, Qoladma and Magna, were also available for use by graduates and participants’ traditional plots. Thus, nine current participants and three graduate farmers reported using the improved seeds Qoladma or Magna on their traditional and graduate plots.
Table 12. Tef Seed and Fertilizer Use in East Shoa by different plot types Technology package Participants plot Traditional plot Graduate plots All
Seed Number of farmers using
Qoladma 24 4 2 30 Magna 7 5 1 13 “Local” 29 51 57 137
Mean application rate (kg/ha)
Seed 47 (a,b) 57 (b,c) 69 (a,c) 58
DAP 104 89 95 96
Urea 104 58 59 72
Herbicide 0.45 0.27 0.36 0.36 Source: Calculated from GRMP/MSU/SG2000/MOA survey data. a denotes that the independent t-test of the seed application rate was significantly different at 99% level. b denotes that the independent t-test of the seed application rate was significantly different at 90% level. c denotes that the independent t-test of the seed application rate was significantly different at 95% level.
Because of the flexibility in the seed input, the tef technology used by the current participants differed from the traditional and graduate plots only in the rate and ratio of DAP and urea application and the seeding rate (Table 12). Farmers altered the recommended fertilizer application rate by either lowering both the DAP and urea application rate, only the urea application rate, or not applying urea at all. The seeding rate on traditional and graduate plots was significantly higher than the seeding rate on participants plots. This is explained entirely by the higher seeding rate of farmer-saved seed (Table 13), which was used by a majority of farmers on the graduate and traditional plots.
The seeding rate of farmer-saved seed was significantly higher than the seeding rates of Qoladma nad Magna, which is closer to the recommended seeding rate of 35 kg/ha. Also, the farmer-saved seeds yielded significantly higher than the improved seeds Qoladma and Magna distributed by the NEP program (Table 13).
27 Table 13. Seeding rate and grain yields by tef seed varieties used in East Shoa Seed Variety Seeding rate (kg/ha)a Grain yields (kg/ha)a
Qoladma (N=30) 39 (a) 1096 (a)
Magna (N=13) 41 (b) 1110 (c )
Farmer saved (N=137) 63 (a,b) 1498 (a,c)
All 58 1403 Source: Calculated from GRMP/MSU/SG2000/MOA survey data. a Letters a and c denote that the independent t-test on differences in the seed application rate was statistically significantly at the 99% level. Letter b denotes that the t-test was statistically significant at 95% level.
Based on the various combinations of the improved seed type and the fertilizer application rates, the tef plots are grouped into 5 technology types described in Table 14. The technology types 1 and 2 correspond to the tef technology recommended by the NEP; with technology type 1, actually distributed as a package by NEP. In technology types 1 and 2, the DAP and urea fertilizer was applied in equal quantities on a given plot with a mean application rate of 105 kg/ha and 96 kg/ha, respectively, which is close to the recommended rate of 100 kg/ha. The technology types 3 and 4 reflect the alterations in the seed and fertilizer components of the technology encountered on the traditional and graduate plots. In technology types 3 and 4, the mean DAP application rate is close to the recommended rate. However, the mean applicaiton rate of urea is almost half the recommended rate and the rate observed in technology groups 1 and 2. Technology type 5 consists of improved seeds (mostly farmer retained) and no fertilizer. There was no significant variation in the herbicide application rate within a given technology type. A majority of the farmers used some herbicide in all the technology groups, as indicated in Table 14. In technology type 1 (extension program package), herbicide was used by all the 35 farmers in the group.
A comparison of tef grain yields for each technology group shows statistically significant differences among technology types with different seed types, but not among technology types with different rate and ratio of fertilizer application. Thus, yields of technology type 1 (Qoladma/Magna seed with receommended rate and ratio of DAP and urea) are statistically lower than that of technology type 2 (farmer retained improved seed with receommended rate and ratio of DAP and urea). Similarly, the yields of technology type 3 are statistically lower than yields of technology type 4. However, yields of technology type 1 and 3 (using the Qoladma/Magna seed with a different rate and ratio of fertilizer) and types 2 and 4 (using the farmer retained improved seed with a different rate and ratio of fertilizer) are not statistically different. The grain yields of technology type 5 (mostly farmer retained seed with no fertilizer) were not found to be statistically different than yields of other technology types.
28 Table 14. Types of Tef technology Represented in the Sample Number Average Average kg/ha of % of farmers in a of plots yields fertilizer given technology using a kg/ha group applying Type of Tef Technology given DAP Urea herbicides technolog y
(1) Improved seed (Qoladama 35 1082 105 105 100 or Magna) plus equal quantity of DAP and Urea
(2) Farmer retained improved 63 1523 96 96 73 seed plus equal quantity of DAP and Urea
(3) Improved seed (Qoladma or 8 1181 95 47 88 Magna) plus DAP and Urea quantity based on farmers’ discretion
(4) Farmer retained improved 69 1482 98 45 73 seed plus DAP and Urea quantity based on farmers’ discretion
(5) Any improved seed and no 5 1385 0 0 60 fertilizer Source: Calculated from GMRP/MSU/SG2000/MOA Survey data
4.2.2. Econometric Analysis of Tef Yield Determinants
Several models were developed to disaggregate the impacts of technology, environment, and crop management variables on tef grain yields based on the 160 observations across all the technology types described in Table 14. Results of two of these models are given in Tables 15 and 16.
Contribution of technology factors on tef yields. The regression results of Model 1, using the technology groups described in Table 14 to represent the different combinations of seed and fertilizer inputs, indicate that the technology group 2 and 4 using farmer retained seed yielded significantly higher (about 300 kg/ha) than the yields of technology group 1 (which is not included in the equation). The rate and ratio of DAP and urea application had no significant impact in explaining yield variation as indicated by the coefficients (and their lower significance level) of variables Tech 3 and Tech 5. The herbicide application had a negative but statistically insignificant impact on yield variability.
29 Table 15. Regression Analysis of Factors Affecting Tef Yields in East Shoa (Model 1) Variables Coef. SE Coef. T Sig T
Constant 1234 95 12.95 0.00
Tech2 dummy: Farmer retained improved seed plus 323 94 3.42 0.00 equal quantity of DAP and Urea
Tech3 dummy: Improved seed (Qoladma or Magna) 59 169 0.35 0.73 plus DAP and Urea quantity based on farmers’ discretion
Tech4 dummy: Farmer retained improved seed plus 295 93 3.08 0.00 DAP and Urea quantity based on farmers’ discretion
Tech5 dummy: Any improved seed and no fertilizer 118 207 0.57 0.57
Herbicide (kg/ha) -43 103 -0.41 0.68
Plowing dummy:1 = more than 4 plowings -135 75 -1.81 0.07
Rain distribution dummy (farmer assessment): 344 92 3.74 0.00 1 = good/excellent distribution
Gray soil dummy:1= gray color soil -227 72 -3.14 0.00 Source: Calculated from GMRP/MSU/SG2000/MOA Survey data. Note: Adjusted R2 = 0.24
Table 16. Regression Analysis of Factors Affecting Tef Yields in East Shoa (Model 2) Variables Coef. SE Coef. T Sig T
Constant 1227 105 11.7 0.00
Seed type dummy:1 = farmer retained improved seed 295 82 3.6 0.00
DAP (kg/ha) -0.17 0.8 -0.2 0.84
Urea (kg/ha) 0.33 0.9 0.4 0.71
Herbicide (kg/ha) -43 107 -0.4 0.68
Plowing dummy:1 = more than 4 plowings -137 75 -1.8 0.07
Rain distribution dummy (farmer assessment): 346 92 3.7 0.00 1 = good/excellent distribution
Gray soil dummy:1= gray color soil -216 72 -2.9 0.00 Source: Calculated from GMRP/MSU/SG2000/MOA Survey data. Note: Adjusted R2 = 0.24.
30 The higher yielding ability of farmer retained improved seed is also confirmed by the regression results of Model 2 given in Table 16. Ceteris Paribus, the plots that used farmer retained improved seeds obtained about 300 kg/ha more yields than their counterparts using Qoladma or Magna seed. The higher yield for seed retained by farmers was enjoyed by a majority of farmers in the sample, as 137 out of 180 farmers surveyed used their own seeds.
The inclusion of kg/ha DAP and urea variables in Model 2 did not change the results much. Both fertilizer (DAP and urea) and herbicide variables in Model 2 were not significant variables in explaining yield variations.
Contribution of environmental factors on tef yields. Various variables summarizing the information on environmental factors that could potentially impact grain yields were used in the regression models for tef. This included, soil types (distinguished by color, local names and clay/sand content) and farmer assessment of soil fertility, farmer assessment of various biotic and abiotic stresses (rain shortage, rain distribution, weed damage, diseases, insects, etc.). However, only two environmental variables turned out to be significant in explaining the yield variation. These were the farmer assessment of rain distribution and soil color.
Farmer’s who assessed the distribution of rainfall to be good or excellent (only 28 farmers) experienced about 350 kg/ha more yield than the farmers who assessed the rain distribution to be poor. The soil color gray, which was reported by 51 farmers, had a negative impact on yields. Gray soil types reduced the yields by more than 200 kg/ha compared with red or black soils in the zone. These results are consistent across both the models (Table 15 and Table 16).
Contribution of management practices on tef yields. The only management variable that showed significant impact on tef yield was the number of plowings. However, unlike in maize, the number of plowings had a negative effect on farmers yields. On an average, farmers who plowed more than 4 times got about 137 kg/ha less than those who plowed less. Fortunately, only 41 farmers in the sample plowed more than 4 times. These results are also consistent across both the models (Table 15 and Table 16).
The regression results in Table 15 and Table 16 present a rough picture of factors that appear to have had the most important influence on tef yields in the 1997 growing season in East Shoa. However, it is important to note that these models explain only a portion (24%) of the variability in the yield data.
31 4.2.3. Graduate Farmers’ Decisions Concerning Choice of Tef Technology
Table 17 lists the various components of tef technology practices and the number of graduates continuing a given practice. The reasons for not continuing a recommended practice given by the graudates are listed in Table 18.
Table 17. Graduate farmers’ response to recommended tef practices, East Shoa Number of Graduates 60 Technology component Percentage of graduates in the zone continuing a given technology Improved seed 40 Recommended seeding rate 45 Recommended rate of DAP 65 Recommended rate of Urea 25 Source: Calculated from GMRP/MSU/SG2000/MOA Survey data
Table 18. The most common reasons given by graduates in East Shoa for not continuing a given tef technology component % of farmers not using the Common reasons for discontinuing a given recommended practice for a Technology component technology component given reason
Improved seeds Couldn’t get improved seed 61 Seed isn’t supplied/local seed is better 39
Seeding rate Seed damage enforced second planting 55 To control weeding 30
DAP rate High cost of fertilizer 52 Land is more fertile 19
Urea rate Urea lodges the plant 51 High cost of fertilizer 25 Source: Calculated from GMRP/MSU/SG2000/MOA Survey data
The farmers in the East Shoa zone seem to be less convinced of the seed type and seeding rate recommended by the Extension program. As indicated in Table 17 only 40 percent of the graduates surveyed continued the use of recommended improved seed (Qoladma or Magna) and the seeding rate (35 kg/ha). In fact, the mean seeding rate in graduate plots was observed to be
32 69 kg/ha, almost double the recommended rate. The main reason for switching from the recommended seed variety to their own improved seed saved from the previous harvests given by the graduates was the unavailability of improved seeds (Table 18).13 However, 40% of the graduates had made a rational choice of switching to the better yielding improved seeds of their own. The main reason why many graduates had to increase the seeding rate was the seed damage that forced them to plant the second time. The other important reason for higher seeding rate was to increase the plant density and control the growth of weeds.
In the case of fertilizer technology, more graduates seem to be convinced of the importance of DAP than of urea as almost two-thirds of the graduates continued the recommended rates of DAP compared to only a quarter of graduates continuing urea. Although, high cost of fertilizer was cited as an important reason for discontinuing DAP and urea, the main reason for lowering the urea rate was the problem of lodging associated with urea application.
13 This reason reported by graduates does not seem quite convincing given the fact that Qoladma and Magna seeds were available and used by some graduates and on traditional plots.
33 5. MOVING FROM DEMONSTRATION PLOTS TO SUSTAINABLE INCREASES IN AGRICULTURAL PRODUCTIVITY
The results presented in Sections 3 and 4 clearly indicate that the use of improved technology significantly increased yields and income for our survey farmers. Analyses in Section 2, however, showed that MOA/SG participants are wealthier than average farmers, with larger land holdings and better access to resources such as animal traction and labor. The NEP is now turning to the task of extending improved technologies beyond this select group to a much larger group of farmers in more marginal agroecological areas. In this section we consider how these changes in the program and the nature of NEP clientele are likely to affect NEP and partner institutions in the seed, fertilizer, and credit subsectors.
The task of extending these technologies to a much broader group of farmers throughout the country is not an easy one, but it is a task that the current Ethiopian government made one of its top priorities in 1995 when it began the transition from the MOA/SG program, which covered a maximum of 3,500 farmers annually, to the NEP program, which has a goal of almost 3 million demonstration plots for 1998. The objectives of NEP are similar to those of MOA/SG: to introduce farmers to new technologies in a well-supervised environment so that after a year or two of participation in the program they (1) see the merits of the new technology and (2) are able to ‘graduate’ and continue using the new technology on their own with no special assistance. The main differences between the two programs are the scale and the fact that NEP aims to extend coverage of the program to poorer farmers in less favorable agroecological zones.
Plans to rapidly expand the number of demonstration plots raise serious challenges for the extension service as well as the banking and the input supply sectors. The extension agency will need to increase the efficiency with which it delivers services to farmers so that the costs of the program do not become prohibitive, yet it must be careful not to dilute the quality of the message or the supervision -- both of which appear to have been key ingredients in the success of the MOA/SG efforts. The banking sector (along with the government services that are now involved in processing and/or guaranteeing loans to farmers) will need to be sure that credit is available in a timely and low-cost manner for program participants, graduates, and key actors in the input distribution sector. Input suppliers will need to become more responsive to the needs of farmers graduating from the demonstration program by providing the desired quantity and quality of seeds, fertilizers, and pesticides at the right time for the lowest possible costs.
In the following pages of the report we describe the key characteristics of the extension, credit and input supply systems, focusing on important changes that have taken place during the 1998/99 cropping season and evaluating their likely impact on farmers’ ability to continue using the improved technologies promoted by MOA/SG and NEP.
34 5.1. Challenges Facing the Extension Service
Increases in the number of DAs (extension agents) have not kept pace with the rapid expansion in the numbers of NEP participants (from thousands to millions in three years). Extension experts in the Ministry of Agriculture recommend a ratio of about one DA per 100 demonstration plots. However, it is estimated that this ratio has expanded to one DA per 150 to 500 demonstration plots in some areas. At this level, the DAs may not be able to adequately provide sufficient technical assistance to each participating farmer. With the increased load come additional responsibilities for credit administration that also reduces the time agents can spend interacting with farmers on technical training.
As noted in Section 2, participants in the MOA/SG program and current participants in the NEP program who were covered by our survey manage farms that have a much better resource base than the average Ethiopian farm -- more land both absolutely and per capita, larger family sizes (i.e., more labor), better education, more livestock and more traction animals. This raises some questions about what will happen to yield response as the improved technologies are extended to less well endowed farmers. It also presents a real challenge for the extension agents expanding the demonstration plot program as they will progressively encounter farmers who have less capacity to adopt the technologies and use them effectively. Agents will increasingly be working with farmers who need more supervision than the previous round of participants, but the agents will have less time to devote to each one. The extension service has already begun to address this problem by encouraging previous graduates and local school teachers to serve as volunteer assistants to the extension agents during peak periods (planting, in particular), but this is a situation that will need to be monitored closely as the program evolves.
Another challenge for the extension service is ‘weaning’ the farmers from the program. This is a particularly difficult problem for maize given that the improved maize seed is still not available on the open market. Included in our survey are farmers who spent two years with the MOA/SG program and are now in the first or second year of the NEP because they cannot obtain improved seed outside the program. Unless a supply of hybrid seed can be developed that is independent of the NEP, it is unlikely that farmers will really graduate from the program. If this happens, the government risks carrying the costs of a very expensive program as more and more new farmers are added but few of the earlier participants are able to continue using the technology on their own.
5.2. Evolution of the Agricultural Input Credit System
Fertilizer sales in Ethiopia are primarily credit sales due to the poor cash position of most farmers. Since 1991 input credit has been made available to farmers through farmer associations or service cooperatives. With the launching of the NEP in 1995, farmers could also obtain credit by participating in that program.
35 Although historically the bulk of agricultural input credit has been channeled through farmers’ groups, in 1998 the availability of credit through this channel apparently declined in a number of zones and woredas. This left farmers with three options: to participate in the NEP, to purchase inputs for cash, or to revert to low-input production techniques. The decisions to limit credit to the NEP are being made locally (at the zone or woreda level) and the reasons appear to be diverse, including (1) a desire to meet NEP targets and (2) inadequate credit resources to cover both program and nonprogram farmers. This phenomenon could keep NEP graduates from continuing to use the improved technologies.
Another characteristic of the current credit program that needs to be evaluated is the amount of time government personnel (DAs and other extension staff in particular, but also other zonal and woreda administrators) are spending processing paperwork for both the NEP and the regular credit program. Many of the tasks performed by government personnel are ones that would normally be performed by farmer organizations or banking sector personnel. The current division of tasks not only increases the public cost of the credit program but also keeps key employees such as the DAs from devoting their full energies to advising and supervising farmers.
Another concern is the rate of reimbursement. The smaller MOA/SG program was characterized by very high rates of repayment (>95% in most years). As the NEP expands to a more diverse group of farmers the repayment rate may decline. Nevertheless, the sustainability of the program depends on the ability of both the NEP and the service cooperatives to ensure relatively high reimbursement rates.
Access to fertilizer credit is a concern because of the significant role credit plays in the expansion of fertilizer use. Of the three woredas in Jimma, Dedo is the only one that continues to extend a significant amount of fertilizer credit to farmers outside of the NEP. This means that those farmers that have graduated from the SG or NEP program will have to ask to participate in the program again to obtain fertilizer credit. In fact, the MOA/SG graduate survey participants in the Jimma zone were still participating in the NEP and were not sourcing their inputs from the market as intended by the programs. It is reported that the DAs enroll these farmers readily because they are known to repay their loans. However, this practice reduces the chance that a farmer that has not participated in the program will be chosen, given the limited capacity of the DA. Consequently, the transfer of technology may not be spreading as rapidly as intended.
36 5.3. Description of the Inputs Subsector
5.3.1. Fertilizer Market Development -- Balancing the Need for Decentralization and Competition
The government’s stated goal for fertilizer sector development is to promote a streamlined, competitive and efficient fertilizer importation and marketing system to ensure the availability of fertilizer on a sustainable basis (National Fertilizer Policy 1993, cited in World Bank 1995). The government’s concurrent objective to decentralize many administrative processes, however, has led to very uneven progress toward this goal. Regional governments have become more instrumental than the national government in designing the rules and regulations that shape fertilizer distribution activities within each region.
Up through 1997 the fertilizer sector (in all regions) was characterized by a high degree of market concentration. The regional market shares of the six fertilizer distributors operating in Ethiopia were determined by the regional governments without any pretense of competition. However, in 1998 the structure of the market in Oromiya Region changed markedly, and there was substantial progress toward the creation of a transparent, competitive market. In many woredas throughout Oromiya fertilizer suppliers were determined by a competitive auction. Meanwhile, other regional governments continued to control market access, usually designating suppliers of their choice without giving the relative prices of all potential suppliers adequate consideration.
5.3.2. The seed sector - much work ahead
Seed market development lags behind that of the fertilizer sector, despite the urgent need to increase the availability of maize hybrids being promoted by the NEP. Improved varieties of tef seed are available in local shops and markets, but hybrid maize seed is not. Apart from the National Seed Enterprise, a government parastatal that supplies the majority of hybrid maize seed, the multinational firm Pioneer Hi-bred is the only other major actor in the Ethiopian maize seed industry. Pioneer usually sells its hybrids to the National Seed Enterprise, but is now beginning to promote direct cash sales to farmers.
Unlike the fertilizer market, there is no credit for seed purchases outside of the NEP. The inaccessibility of hybrids has important implications for NEP graduates: without hybrid seed the responsiveness of fertilizer declines. In practice, many farmers who have already participated in the NEP for two years find ways to sign up again simply to obtain the seed.14
14 DAs anxious about filling their quotas for participants will often ignore the fact that an applicant has already been in the program for two years.
37 5.4. Key Sustainability Issues
Ethiopia faces a critical challenge at this juncture: assuring that better-off farmers who have already adopted improved technology continue to have access to inputs, while simultaneously adapting the successful SG model to the needs of poorer farmers in riskier environments. Several key issues need to be addressed by NEP to build on this current momentum for improved technology adoption and increased production in Ethiopia. These include:
Maintaining the quality of extension assistance. Our survey found that the high level of supervision by DAs contributed to farmer adoption of recommendations on plant distance and row spacing, and to increased yields, among program participants. There are concerns that DAs may become overextended with the rapid expansion of the NEP and thus the quality of the extension service may be compromised.
Financial sustainability. Financial sustainability is also another concern for the long-term growth of the program. The Ministry of Agriculture is paying the salaries of the DAs and their supervisors, as well as administrators in the woreda bureaus of agriculture. Many woreda bureaus of agriculture report that they have devoted most of their resources to processing fertilizer loans and have not been able to perform their regular duties. In addition, many of the woreda bureaus of agriculture, particularly in Oromiya Region, report that they have borne unexpected costs to deliver the NEP inputs to the farmers.
Cost reductions in the input system. Input costs (especially fertilizer, but also seed) are a large component of the financial budgets for farmers in the MOA/SG program (see section 3). Reducing the cost of inputs will enhance the accessibility of the program to a broader population. Areas for potential cost reductions that merit further investigation include the timing of fertilizer imports to minimize port and storage costs, and improvements in competition both at the import and distribution stages of the fertilizer marketing chain. At the distribution stage, the practice of holding fertilizer auctions (as conducted in parts of Oromiya Region) may be one avenue for inviting competition and reducing the cost of fertilizer.
Access to credit. There are concerns that (a) the NEP is absorbing more and more of the agricultural credit resources as it expands, and (b) the total amount of credit available for for agriculture is not increasing proportionally. Although the NEP has expanded, it is reported that in two of the three MOA/SG survey zones total credit declined between 1997 and 1998. In Jimma the amount of fertilizer credit disbursed fell by 5.9 percent between 1997 and 1998. This may explain the reduced quantities of fertilizer sold on credit in the three SG woredas in Jimma from
38 1997 to 1998. In another example, the amount of fertilizer credit disbursed in East Shoa fell by 23 percent between 1997 and 1998.15 Where there were once multiple channels for credit accessible to farmers, now in some areas NEP is the only credit source. Graduate NEP farmers and non-participants are finding it more difficult to obtain fertilizer credit. Overall, the reduced access to credit may mitigate the success of the adoption of new technologies.
Perhaps improved credit targeting and simultaneous development of the fertilizer cash market could expand effective fertilizer demand. The resourceful farmers often join the NEP credit program to obtain the improved seed that are not otherwise available on the market. Perhaps these farmers could be directed toward fertilizer cash sales while relatively resource-poor farmers could purchase on credit.
Policy uncertainty. In 1998 fertilizer importers and distributors faced considerable policy uncertainty: stated fertilizer polices were often abandoned and markets were not as open as the fertilizer distributors originally perceived. Given the unpredictable policies across Ethiopia, fertilizer importers and distributors are unsure what fertilizer policy will be enacted in the coming season. The national government has stated its goal of developing a free market for fertilizer, but regional policies often carry different and changing messages. Private wholesalers have frequently been unable to obtain a market share because the regional government’s policies favor certain importers and regional government companies. These policy conflicts many be due to the government’s efforts to decentralize the national governing body, but simultaneously set national goals. Policy uncertainties at both the national and regional levels raise the cost of investing in the fertilizer sector and may discourage new entrants.
15 Credit information for 1997 and 1998 from unpublished data provided by the Commercial Bank of Ethiopia.
39 6. CONCLUSIONS
In this report we have analyzed the factors underlying the success of the MOA/SG program and looked at its evolution to the NEP. Four principal conclusions emerge from our research.
6.1. Yield and Income Gains from the Use of Improved Technology are High
Farmers in our study areas who used improved seed and technology achieved yields that far exceeded national and regional averages. For maize, average program and graduate plot yields ranged from 5.5 to 6.8 tons/ha, compared to national and regional averages of 1.9-2.1 tons per hectare. Tef grain yields were 1.4-1.5 tons/ha, 50% higher than national and regional averages of 0.9 to 1 ton/ha.
Net income per hectare and per labor day were also very high for improved technology users. Net income and returns per labor day increased with yield, but variable costs were covered and returns to family and mutual labor far exceeded average daily wage rates (3-6 birr per day) for all program and graduate terciles. In Jimma, net returns from program plots were double those of traditional plots, and returns per labor day were 1/3 higher for program plots compared to traditional plots. In general, returns per labor day for tef in East Shoa were greater than returns per labor day in the maize zones. Net returns per labor day ranged from 14-57 birr/day (at January output prices) for tef in East Shoa, compared to 9-23 birr/day for maize in West Shoa and 6-22 birr/day for maize in Jimma.
Profitability is extremely robust when output prices change. Even if output prices drop by 25% from January 1998 price levels, net income per hectare and per labor day remain positive and returns per labor day exceed average wage rates. If output prices decline by 50%, net income per hectare is positive and net income/labor day is still comparable to average wage rates in all but the lowest yield terciles.
6.2. Gains from Storage are Potentially Great, Especially if Storage Insecticide is Used
In 1998 farmers in East Shoa and Jimma could have increased net income by more than 40% by storing grain and selling in August rather than immediately after harvest (January). Tef grain prices rose by 23% and straw prices doubled between January and August 1998; maize prices in Jimma rose by 72% during the same period.
Unlike tef, untreated maize deteriorates rapidly in storage, losing an estimated 2% of grain weight per month to weevils and other pests. In Jimma, the price rise over time was so steep that farmers storing until August 1998 still achieved net income gains of more than 40%. In West Shoa the price rise was less dramatic, and farmers would have lost 4-5% of net income if they had stored and sold maize in August instead of January. None of our survey farmers used storage insecticide
40 in the 1997-98 crop season. If they had used insecticide and storage losses were reduced by half, net income per hectare and per labor day would increase by an estimated 9-17%.
6.3. Improved Seed and Fertilizer are the Most Important Determinants of Yield
Regression analysis indicated that the use of technology itself -- improved seed and fertilizer-- explained the largest part of yield variability. For maize, the package recommended by MOA/SG -- hybrid maize, DAP and urea--clearly outperformed the “traditional” technology of local seed plus DAP only. In West Shoa, even when rains were poor, those who used improved technology still did substantially better than those who returned to traditional technology -- in this case, local varieties and no fertilizer.
For tef, our analysis indicates that farmers’ saved seed (also an improved variety) yielded significantly more than the improved variety distributed by NEP in East Shoa for the 1997 season. Farmers used DAP and urea fertilizers on all plots.
In addition to technology, some management practices were also found to be important in explaining yield variability. For example, deviations from the recommended row spacing and planting distance significantly reduced maize yields in Jimma and West Shoa, suggesting that the recommended planting technique is a critical component of the improved technology package. The results also confirm the importance of the extension program and its role in conveying management practices to farmers.
6.4. Key Questions: (1) How to Sustain this Successful Program in High Surplus-Producing Regions? (2) How to Expand it Beyond Wealthier Farmers in Better Agroecological Areas?
The MOA/SG program has been extremely successful in demonstrating the powerful impact that improved technology can have on raising yields and household incomes. In our view, the most important factors contributing to this success are (a) good technology, well adapted to the target agroecological zones; (b) the timely delivery of inputs; (c) intensive, high quality extension assistance; and (d) the availability of credit.
An additional important factor is that MOA/SG program households have relatively larger landholdings and are wealthier and better educated than the average population. Up to now the MOA/SG program has focused on these wealthier farmers in better agroecological areas. Other GMRP/MSU research indicates that countrywide, the top 20% of farmers (by production) contribute up to 80% of total marketed production. Continued adoption of technology by this group of farmers is very important and can lead to significant increases in marketed production and result in lower food prices and increased food security for Ethiopia.
41 At the same time, over the past several years MOA has sought to extend high-input technology to a much broader group of smallholders beyond the better-off agroecological zones through the NEP program. The country faces a critical challenge at this juncture: assuring that better-off farmers who account for most of marketed production continue to have access to inputs, while simultaneously adapting the successful SG model to the needs of poorer farmers in riskier environments.
In our view there are five key lessons that are important to consider as Ethiopia strives to meet the dual goals of sustaining adoption among high-surplus producers and introducing high-input technologies to smaller farmers in riskier agroecological areas. First, the most important concern for graduate farmers is ensuring their continued access to inputs, particularly seed and fertilizer, after they leave the program. In the past the general farming population had relatively easy access to credit through service cooperatives, development banks or private banks. Our analysis indicates that in some areas the expansion of the NEP is drawing credit resources away from these alternative sources, making it more difficult for farmers who are not enrolled in NEP to obtain credit. Another problem is that farmers report it is very difficult to obtain hybrid maize seed if one does not participate in the NEP. Further investigation is necessary and potential actions need to be explored to ensure that surplus producers continue to have access to improved seed, fertilizer and credit.
Second, technology packages will need to be adjusted as NEP moves into riskier areas with less reliable rainfall and poorer soils. Drought-tolerant varieties will be more important, but are likely to yield less than longer-season varieties suited to areas with better rainfall. Fertilizer recommendations may need to be adjusted downward to avoid exposing farmers to too much risk. Technologies that allow farmers to extend cropped area by reducing labor requirements, such as herbicides or improved hand or animal-drawn equipment, may also be important. Computer crop simulation models such as CERES (for maize) can be used to predict how technology packages will perform under different agroecological conditions and can partially substitute for expensive on-farm trials of technology.
Third, it may be necessary to modify credit programs for smallholders in riskier agroecological areas. Up to now repayment rates in the SG/MOA program have been extremely high (over 95%) but yield and income gains have also been high with few incidences of crop failure. With expansion into drought-prone areas, NEP will need to prepare for a greater number of legitimate loan defaults due to crop failure. In a few of our survey villages program graduates said that they had experienced total crop failure because of weather problems. Although the problems were confirmed by the extension agent the farmers were forced to repay the full amount of the input loan. In several cases farmers had to sell their oxen in order to repay the loan. It will be important to explore options for protecting farmers who are affected by natural factors beyond their control from total asset depletion (e.g., crop insurance). These approaches should be pursued in conjunction with efforts to fine-tune technology packages, especially reducing levels of
42 high-cost inputs, with the objective of increasing farm yields while moderating farmers’ exposure to risk.
Fourth, as the program expands into marginal areas yield and income gains from improved technology use may be far lower than the results we report for the better agroecological zones. To keep profit levels up, it will be important to reduce costs, especially seed and fertilizer costs, which represented 50-80% of total costs for our survey farmers. More research is needed to explore alternatives for reducing costs throughout the seed and fertilizer subsectors. Regions and zones throughout the country are actively experimenting with alternative fertilizer distribution systems. It will be important to track these efforts (such as Oromiya Region’s auction system) carefully to understand which systems are best able to procure and deliver fertilizer on time at the lowest possible price.
Finally, intensive, high quality technical assistance from extension agents has been vital to the success of the SG program. Program farmers in our survey reported an average of nine extension visits per season. Farmers in our survey had relatively high levels of education. The expansion of NEP means that future program farmers will be less educated and may need even more assistance from extension agents. Three of the biggest challenges ahead for the program will be to (a) keep the extension agent to farmer ratio relatively low to enable agents to effectively teach and supervise program farmers; (b) maintain high standards for staff qualifications and performance among new extension agents joining MOA; and (c) explore ways to reduce the burden of credit administration on extension agents and other MOA personnel.
43 APPENDIX 1: EXAMPLES OF GMRP/MSU/SG2000/MOA SURVEY QUESTIONNAIRES
44 Ministry of Economic Development and Cooperation Grain Marketing Research Project with the collaboration of Sasakawa-Global 2000
Survey of Input Utilization and Marketing in the Smallholder Sector (Part I) (October-November 1997) Maize Plot QTYPE Zone ZON
Woreda WOR
Farmer Association FA
Household Number HH
Farmer Name
Enumerator ENUM
45 ZON WOR FA HH QTYPE
1. Maize Plot
Field Map: Using a compass and tape, measure all sides and angles of the field. Sketch the field below, noting side and angle measurements.
Points AB BC C - D - E- F- G- H- I- J- K- Bearing (degrees) Mc^_ g_[mol_g _hn "g_n_lm#
Total area of field (square meters) Coordinates:
Hilnb ?[mn
46 ZON WOR FA HH QTYPE
1. Maize Plot
Table 1. Description of the Maize Field
Area of field (farmer Other crops in Soil type Type of soil Slope Soil Farmer’s estimate of Form Tenancy estimate) this field (local name) Fertility yield from this plot (intercropped) No. Units 1. koticha 1.clay 1.red 1.level 1. High Qt Unit 1.grain 1. own land 1.timad (use codes below) 2. gonbore 2.medium 2.black 2.gullies 1.50 kg 2.ears 2. rented * 2.Medium 2.kert 3. shewi 3.sandy 3.gray 3.slope 3. Low 2.100 kg 3. 3.ha 4.other (specify) 4.steep 3.other sharecropped slope (spec.) (1/3, 1/2)
I1 I 2 I3 I4 I5 I0 I1 I2 I3 I+* I++ I+, I+- I+/
* 1.teff 2.maize 3. wheat 4.barley 5. sorghum 6.millet 7.pulses 8. oilseeds 9.fallow 10.other (specify)
47 ZON WOR FA HH QTYPE
1. Maize Plot
Table 2. Plot History
What crops were planted in this Use of fertilizer and manure field?** (use codes below) 95/96 94/95 93/94 92/93
95/96 94/95 93/94 92/93 DAP Urea Manur DAP Urea Manur DAP Urea Manur DAP Urea Manur e e e e
II1 II1 II1 II1 II2 II3 II4 II2 II3 II4 II2 II3 II4 II2 II3 II4
** 1.tef 2.maize 3. wheat 4.barley 5. sorghum 6.millet 7.pulses 8. oilseeds 9.fallow 10.other (specify)
48 ZON WOR FA HH QTYPE 1. Maize Plot
N[\f_-(M[gjf_Jfin>[n[`ilSc_f^?mncg[ncih
Selected No. plants No. ears Distance Distance No. Amt. of fertilizer used Amt. of fertilizer used Weight of Moisture 2 x 4 meter between between seeds/hill per hill during planting per hill as a top dressing grain after content plot plants rows (cm) (basal) harvest (%) (cm) (kg) Qt. Units Qt Units 1.Coke cap 1.Coke cap 2.spoon 2.spoon 3.other (specify) 3.other (specify)
III1 III6 III7 III8 III9 III10 III11 III12 III13 III14 III3 III5
1
2
49 Ministry of Economic Development and Cooperation Grain Marketing Research Project with the collaboration of Sasakawa-Global 2000
Survey of Input Utilization and Marketing in the Smallholder Sector (Part I) (October-November 1997)
1. To be filled out only by farmers currently participating in the SG2000/Government Extension Program program Tef Plot
QTYPE
Zone ZON
Woreda WOR
Farmer Association FA
Household Number HH
Farmer Name
Enumerator ENUM
50 ZON WOR FA HH QTYPE
1. Tef Plot
Field Map: Using a compass and tape, measure all sides and angles of the field. Sketch the field below, noting side and angle measurements.
Points AB BC C - D - E- F- G- H- I- J- K- Bearing (degrees) Mc^_ g_[mol_g _hn "g_n_lm#
Total area of field (square meters) Coordinates:
Hilnb ?[mn
51 ZON WOR FA HH QTYPE
1. Tef Plot
Table 1. Description of the Tef Field
Area of field (farmer Other crops in Soil type Type of soil Slope Soil Farmer’s estimate of Form Tenancy estimate) this field (local name) Fertility yield from this plot (intercropped) No. Units 1. koticha 1.clay 1.red 1.level 1. High Qt Unit 1.grain 1. own land 1.timad (use codes below) 2. gonbore 2.medium 2.black 2.gullies 1.50 kg 2.ears 2. rented * 2.kert 3. shewi 3.sandy 3.gray 3.slope 2.Medium 2.100 kg 3. 3.ha 4.other (specify) 4.steep 3. Low 3.other sharecropped slope (spec.) (1/3, 1/2)
I1 I 2 I3 I4 I5 I0 I1 I2 I3 I+* I++ I+, I+- I+/
*1.tef 2.maize 3. wheat 4.barley 5. sorghum 6.millet 7.pulses 8. oilseeds 9.fallow 10.other (specify)
52 ZON WOR FA HH QTYPE
1. Tef Plot
Table 2. Plot History
What crops were planted in this Use of fertilizer and manure field?** (use codes below) 95/96 94/95 93/94 92/93
95/96 94/95 93/94 92/93 DAP Urea Manur DAP Urea Manur DAP Urea Manur DAP Urea Manur e e e e
II1 II1 II1 II1 II2 II3 II4 II2 II3 II4 II2 II3 II4 II2 II3 II4
** 1.tef 2.maize 3. wheat 4.barley 5. sorghum 6.millet 7.pulses 8. oilseeds 9.fallow 10.other (specify)
53 ON WOR FA HH QTYPE 1. Tef Plot
N[\f_-(M[gjf_Jfin>[n[`ilSc_f^?mncg[ncih
Selected Weight of grain and straw Weight after threshing (kgs) Moisture content(%) 2 x 4 meter plot before threshing (kgs) Grain Straw
III1 III2 III3 III4 III5
1
2
54
MINISTRY OF ECONOMIC DEVELOPMENT AND COOPERATION GRAIN MARKETING RESEARCH PROJECT with the collaboration of Sasakawa-Global 2000
Survey of Input Utilization and Marketing in the Smallholder Sector — PART II October-November 1997 Tef
1. ______QTYPE
Zone ZON
Woreda WOR
Farmer Association FA
Household Number HH
Name of Farmer
Enumerator ENUM
In what years have you participated in the SG2000 program (mark all appropriate)? No=0 Yes=1
96/97 season (this season)? ______S9697 95/96 season? ______S9596 94/95 season? ______S9495 93/94 season? ______S9394 92/93 season? ______S9293
In what years have you participated in the government extension program (mark all appropriate)? No=0 Yes=1
96/97 season (this season)? ______P9697 95/96 season? ______P9596 94/95 season? ______P9495 93/94 season? ______P9394
AF1 Household head’s level of education
0 Illiterate 1,2,...12 Last year of school completed 99 Did not attend public school, but knows how to read and write
55
PART I. THE FARM Table IV. AREA AND INPUT USE ON MAJOR CROPS IN MEHER SEASON YEAR CROP #1 CROP Area — Own Land Area — Rented, Input #1 Input #2 Input #3 Sharecropped or Borrowed Land
No. Unit No. Unit Type Qty. Unit Type Qty. Unit Type Qty. Unit * 1=timad 1=timad 1=50 kg 1=50 kg 1=50 kg (use 2=kert 2=kert ** 2=100 kg ** 2=100 kg ** 2=100 kg codes 3=ha 3=ha (use codes 3=kg (use codes 3=kg (use codes 3=kg below) 4=fachasa 4=fachasa below) 4=liter below) 4=liter below) 4=liter 20=other 20=other 20=other 20=other 20=other (specify) (specify) (specify)ce (specify) (specify)
YEAR CROP IV1 IV2 IV3 IV4 IV5 IV6 IV7 IV8 IV9 IV10 IV11 IV12 IV13
9697
9596
9495
9394
9293
* Crop Codes: 1=tef 2=maize 3=wheat 4=barley 5=sorghum 6=millet 7=pulses 8=oilseeds 20=other (specify)
** Input Type Codes 100=Seed treatment 200=improved seed 300=DAP 400=Urea 500=herbicide 600=field insecticide 700=storage insecticide 800=fungicide
56
AF2 ______Has the total area (owned, rented, sharecropped or borrowed) planted to tef changed between 1992-93 and the current season? 0 = no change 1 = area planted to tef has increased slightly 2 = area planted to tef has increased significantly 3 = area planted to tef has decreased slightly 4 = area planted to tef has decreased significantly
If there was a change in tef area between 1992-93, give the three most important reasons for the area increase/decrease in order of importance:
AF3______
AF4______
AF5______
AF6 ______Do you plan to increase, decrease or maintain the same area planted to tef during the 1997-98 season (next season?) 0 = no change 1 = will slightly increase area planted to tef 2 = will increase area planted to tef significantly 3 = will slightly decrease area planted to tef 4 = will decrease area planted to tef significantly
If you plan to increase or decrease the area planted to tef next season, give the three most important reasons why:
AF7______
AF8______
AF9______
57
Table V. LIVESTOCK HOLDINGS
YEAR LIVESTOCK 1 LIVESTOCK 2 LIVESTOCK 3 LIVESTOCK 4 LIVESTOCK 5 LIVESTOCK 6
Type No. Type No. Type No. Type No. Type No. Type No.
* * * * * * (use codes (use codes (use codes (use codes (use codes (use codes below) below) below) below) below) below)
YEAR LIVE NO LIVE NO LIVE NO LIVE NO LIVE NO LIVE NO
9697
9596
9495
9394
9293
* Livestock type codes 1= plowing oxen 2= steers 3= cows/heifers 4= calves (< 2 years) 5= horses 6= donkeys 7= sheep and goats
58
PART II. THE HOUSEHOLD Table VI. DEMOGRAPHIC DATA ABOUT THE HOUSEHOLD*
Name No. Relationship to household Age** Sex head 1 household head 1 m 2 spouse 2 f 3 son/daughter 4 father/ mother 5 other relative 6 hired help eating with the household 7 other (specify)
NO VI1 VI2 VI3
(Household head) 1 1
2
3
4
5
6
7
8
9
10
11
12
59
NOTES FOR ENUMERATORS
* The HOUSEHOLD is defined as persons living in the same compound who regularly eat together.
** AGE variable
1. Enumerators should first ask household helds for the exact age of household members in years.
2. The age of children less than 1 year of age (e.g., 3 months) should be recorded as “1."
3. If household heads cannot recall the exact age of household members, prompt for the birth year using the following list of significant historical events:
4. If household heads still cannot recall the birth year, as a last resort categorize the age of family members as follows:
101 = < 7 years of age 102 = (>=7, <=8) 103 = (>=9,<=12) 104 = (>=13, <=15) 105 = (>=16, <=54) 106 = (>=55)
60
PART III. THE SG2000/GOV’T EXTENSION PROGRAM TEF PLOT 1
WORKSHEET: TEF FIELD ACTIVITIES
Activity Power Source When was it carried out?
1=Tractor 2=Animal 3=Human Month Week (START DATE)* 4=Human and (1,2,...,12 or indicate that (1,2,3,4) Animal not done) 2 Clearing New Land 3 Removing Crop Stubble 10 1st Plowing 11 2nd Plowing 12 3rd Plowing 13 4th Plowing 14 5th Plowing 15 6th Plowing 20 Broadcasting seeds 21 Broadcasting seeds and 1st application of fertilizer (DAP and/or Urea) AT THE SAME TIME 30 1st application of fertilizer (DAP and/or Urea) 23 Trampling/leveling 40 Application of herbicide 41 1st weeding 31 2nd application of fertilizer (Urea) 42 2nd weeding 50 1st application of insecticide 51 2nd application of insecticide 70 Harvest 80 Transport to threshing area 90 Threshing and winnowing 81 Transport to storage area 100 Other (specify)
* Enumerators should try to get the farmer to recall the specific WEEK in which the activity was carried out. If the farmer cannot remember the week, prompt for a 2-week period and record this as e.g., WEEK 1-2, WEEK 3-4.
AF10 ______Is the 1996/97 threshing complete? 0 = no 1= yes
61
Table VII. LABOR USED IN THE SG2000/GOV’T EXTENSION PROGRAM TEF PLOT 1 Activity When was it How many Household labor Non-household labor carried out? total labor days were spent on this Mo. Week activity How many Name Days Hours Total number of Type of No.of No. Hrs Total cash If in-kind payment was made Type of meal provided 1,2., 1,2,3,4 (household persons in per day non-household non- (indiv- of per payment ** (use codes below) 12 and non- the workers for household idual) days day (birr) household household activity labor worker labor)? worked in 1=debo Type * Qty. Unit Total est. Type# Type# Type# this activity? 2=wonfel (use codes 1=50 kg value of 1 2 3 3=friends/ below) 2=100 kg in-kind relatives 3=kg payment in 4=hired 4=liter birr 5=exch. 5=ox days for oxen 20=other (specify)
ACT VII1 VII2 VII3 VII4 VII5 VII6 VII7 VII8 VII9 VII10 VII1 VII12 VII13 VII14 VII15 VII16 VII17 VII18 VII19 VII20 1 1
2
3
4
1
2
3
4
1
2
3
4 * In-kind Payment Codes: ** Meal Codes: 1=tef 2=maize 3=wheat 4=barley 5=sorghum 1=lunch 2=dinner 3=local drink =millet 7=pulses 8=oilseeds 20=other (specify) 50=use of oxen
62
Table VIII. INPUTS USED IN THE SG2000/GOV’T EXTENSION PROGRAM TEF PLOT 1 Input How much was used? (For Did you pay How did you When was it How much did it cost? Did you pay animals/tractor no.days/hrs) cash or in get it? applied? immediately kind for this (Copy dates from after input? field worksheet) receiving the input or Qt Unit 1 SG2000/ Cash In-Kind Payment did you 1=50 kg Gov’t extension (Birr) receive 2=100 kg 0 no (skip to program Month Week Type Qty. Unit Est. Total credit? 3=kg the next 2 (1,2,...1 (1,2,3,4) 1=tef 1=50 kg Value in 4=liter input) Trader/Market 2) 2=maize 2=100 kg Birr 1 immediate 5=oxen-days 1 yes (proceed 3 Rented 3=kg payment 6=tractor hours to next (animals/tracto 3=wheat 4=liter 2 Credit 7=tractor ha column) r) 4=barley 5=oxen- 3 Both 20= other 4 Own/Saved 5=sorghum days (indicate (specify) 20 Other 6==millet 20=other amt. input (specify) 7= (specify) received on pulses credit) 8=oilseeds 20=other (specify) INPUT VIII1 VIII2 VIII3 VIII4 VIII5 VIII6 VIII7 VIII8 VIII9 VIII10 VIII11 VIII12
100 Seed Treatment Type______Type______
200 Seed Variety (ies) ______Animal Traction 10 First Plowing 11 Second Plowing 12 Third Plowing 13 Fourth Plowing 14 Fifth Plowing
63
Input How much was used? (For Did you pay How did you When was it How much did it cost? Did you pay animals/tractor no.days/hrs) cash or in get it? applied? immediately kind for this (Copy dates from after input? field worksheet) receiving the input or Qt Unit 1 SG2000/ Cash In-Kind Payment did you 1=50 kg Gov’t extension (Birr) receive 2=100 kg 0 no (skip to program Month Week Type Qty. Unit Est. Total credit? 3=kg the next 2 (1,2,...1 (1,2,3,4) 1=tef 1=50 kg Value in 4=liter input) Trader/Market 2) 2=maize 2=100 kg Birr 1 immediate 5=oxen-days 1 yes (proceed 3 Rented 3=kg payment 6=tractor hours to next (animals/tracto 3=wheat 4=liter 2 Credit 7=tractor ha column) r) 4=barley 5=oxen- 3 Both 20= other 4 Own/Saved 5=sorghum days (indicate (specify) 20 Other 6==millet 20=other amt. input (specify) 7= (specify) received on pulses credit) 8=oilseeds 20=other (specify) INPUT VIII1 VIII2 VIII3 VIII4 VIII5 VIII6 VIII7 VIII8 VIII9 VIII10 VIII11 VIII12
15 Sixth Plowing 23 Trampling/leveling 80 Transport to threshing area 81 Transport to storage area
64
Input How much was used? (For Did you pay How did you When was it How much did it cost? Did you pay animals/tractor no.days/hrs) cash or in get it? applied? immediately kind for this (Copy dates from after input? field worksheet) receiving the input or Qt Unit 1 SG2000/ Cash In-Kind Payment did you 1=50 kg Gov’t extension (Birr) receive 2=100 kg 0 no (skip to program Month Week Type Qty. Unit Est. Total credit? 3=kg the next 2 (1,2,...1 (1,2,3,4) 1=tef 1=50 kg Value in 4=liter input) Trader/Market 2) 2=maize 2=100 kg Birr 1 immediate 5=oxen-days 1 yes (proceed 3 Rented 3=kg payment 6=tractor hours to next (animals/tracto 3=wheat 4=liter 2 Credit 7=tractor ha column) r) 4=barley 5=oxen- 3 Both 20= other 4 Own/Saved 5=sorghum days (indicate (specify) 20 Other 6==millet 20=other amt. input (specify) 7= (specify) received on pulses credit) 8=oilseeds 20=other (specify) INPUT VIII1 VIII2 VIII3 VIII4 VIII5 VIII6 VIII7 VIII8 VIII9 VIII10 VIII11 VIII12
Tractor 10 First Plowing 11 Second Plowing Other Inputs 300 DAP Fertilizer
400 Urea Fertilizer
500 Herbicide Type/form. ______
600 Field Insecticide Type/form. ______
65
AF11 _____ Did you split the application of urea during the current season?
0 = no 1 = yes
If yes, how did you split it?
AF12 ______kgs at broadcasting
AF13 ______kgs as top dressing
66
Table IX. Impact of Purchased Inputs on Tef Yield and Future Input Use Input Impact on yield When did you receive this input? If you had to pay for Rank each input in this input order of its immediately (instead importance of receiving credit), would you purchase it?
1 Improved Month Week Timing If late, reason why 0 Would not buy (1=most important, yield/storage 1 Would buy 6=least important) 2 No impact on 1...12 1...4 1=on time or early 1=delay in receiving yield/storage 2=slightly late credit (specify why) 3 Reduced 3=very late 2=lack of cash yield/stored grain 3=input unavailable 4 Doesn’t know in shops 4=other (specify)
INPUT IX1 IX2 IX3 IX4 IX5 IX6 IX7
200 Improved seed
300 DAP
400 Urea
500 Herbicide
600 Field Insecticide
700 Storage Insecticide
67
Table X. FARMER ASSESSMENT OF FACTORS AFFECTING TEF YIELD 1993/94 - 97/98
Note to enumerator: For each topic, ask the farmer for his assessment of this year (96-97), last year (95-96), two years ago (94-95), three years ago (93-94), four years ago (92-93). Finally, ask what he expects the situation to be next year (97-98).
YEAR Total amount of rainfall Distribution of rainfall Hail and frost damage Wild animal damage Insect infestation Plant disease problem Weed infestation received 1=excess rain 1=excellent 1=hail damage 1=light 1=light 1=light 1=light 2=good rains 2=good 2=frost damage 2=medium 2=medium 2=medium 2=medium 3=shortage of rain 3=poor 3=hail and frost damage 3=heavy 3=heavy 3=heavy 3=heavy 4=can’t recall 4=can’t recall 4 =no damage 4=can’t recall 4=can’t recall 4=can’t recall 4=can’t recall 5=can’t recall YEAR X1 X2 X3 X4 X5 X6 X7 9697 (this season) 9596 9495 9394 9293
9798 (expectation for next season)
68
PART IV. EXTENSION
AF14 _____ During this season (96/97), how many times were you visited by the extension agent?
AF15 _____ How do you view the services provided by the extension department?
1 Very useful 2 Useful 3 Not very useful 4 No comment
What are the two most important extension messages you received during this season (96/97)?
AF16 ______
AF17 ______
CP1 ______If the SG2000/government extension program continues next year, would you like to participate or do you prefer to leave the program?
1 Would like to participate 2 Prefers to leave
CP2 ______If you prefer to leave, why?
______
CP3 ______Do you have additional comments about the SG2000/government extension program or the technologies used in the program?
______
PART V. MARKETING/CONSUMPTION
AF18 ______How does the color of improved tef compare to traditional varieties?
1 Prefers improved tef 2 Doesn’t see any difference 3 Prefers the traditional varieties 4 Doesn’t know
AF19 ______How does the taste of improved tef compare to traditional varieties? 1 Prefers improved tef 2 No difference 3 Prefers the traditional varieties 4 Doesn’t know
AF20 ______What is the principal destination for the TRADITIONAL varieties of tef you produce?
1 Market 2 Home consumption 3Both
69
AF21 ______What is the principal destination for the improved varieties of tef you produce?
1 Market 2 Home consumption 3Both
AF22 ______How does the PRICE that traders pay for improved tef compare to the price paid for traditional varieties?
1 Pay more for improved tef 2 Pay the same 3 Pay less for improved tef 4 Doesn’t know
70
TABLE XI. MARKETING OF TEF
YEAR TOTAL TOTAL QUANTITY MONTH MAIN DISTANCE METHOD PRICE RECEIVED PRODUCTION OF CONSUMPTION BY MARKETED WHEN BUYER TO MAIN OF TEF HOUSEHOLD LARGEST BUYER TRANS- QTY OF PORT TEF SOLD
Qty. Unit Qty. Unit Qty. Unit Month 1=village kms 1=human Price Unit Opinion Source of trader 2=animal (Birr) about price price 1=50 kg 1=50 kg 1=50 kg 1...12 2=local 3=motor 1=50 kg received information 2=100 kg 2=100 kg 2=100 kg market vehicle 2=100 kg 3=kg 3=kg 3=kg 3=trader 4=human 3=kg 1=low 1=radio 20=other 20=other 20=other with truck and 20=other 2=avg. 2=neighbor (specify) (specify) (specify) 20=other animal (specify) 3=high 3=local (specify) marketplac e 20=other (specify)
YEAR XI1 XI2 XI3 XI4 XI5 XI6 XI7 XI8 XI9 XI10 XI11 XI12 XI13 XI14
9596
9495
9394
9293
Plans for 9697
71
PART VI. TRADITIONAL TEF PLOT 2
WORKSHEET: TEF FIELD ACTIVITIES
Activity Power Source When was it carried out?
1=Tractor 2=Animal 3=Human Month Week (START DATE)* 4=Human and (1,2,...,12 or indicate that (1,2,3,4) Animal not done) 2 Clearing New Land 3 Removing Crop Stubble 10 1st Plowing 11 2nd Plowing 12 3rd Plowing 13 4th Plowing 14 5th Plowing 15 6th Plowing 20 Broadcasting seeds 21 Broadcasting seeds and 1st application of fertilizer (DAP and/or Urea) AT THE SAME TIME 30 1st application of fertilizer (DAP and/or Urea) 23 Trampling/leveling 40 Application of herbicide 41 1st weeding 31 2nd application of fertilizer (Urea) 42 2nd weeding 50 1st application of insecticide 51 2nd application of insecticide 70 Harvest 80 Transport to threshing area 90 Threshing and winnowing 81 Transport to storage area 100 Other (specify)
* Enumerators should try to get the farmer to recall the specific WEEK in which the activity was carried out. If the farmer cannot remember the week, prompt for a 2-week period and record this as e.g., WEEK 1-2, WEEK 3-4.
CP4 ______Is the 1996/97 threshing complete? 0 = no 1= yes
72
Table XII. LABOR USED IN THE TRADITIONAL TEF PLOT 2 Activity When was it How many Household labor Non-household labor carried out? total labor days were spent on this Mo. Week activity How many Name Days Hours Total number of Type of No.of No. Hrs Total cash If in-kind payment was made Type of meal provided 1,2., 1,2,3,4 (household persons in per day non-household non- (indiv- of per payment ** (use codes below) 12 and non- the workers for household idual) days day (birr) household household activity labor worker labor)? worked in 1=debo Type * Qty. Unit Total est. Type# Type# Type# this activity? 2=wonfel (use codes 1=50 kg value of 1 2 3 3=friends/ below) 2=100 kg in-kind relatives 3=kg payment in 4=hired 4=liter birr 5=exch. 5=ox days for oxen 20=other (specify)
ACT VII1 VII2 VII3 VII4 VII5 VII6 VII7 VII8 VII9 VII10 VII1 VII12 VII13 VII14 VII15 VII16 VII17 VII18 VII19 VII20 1 1
2
3
4
1
2
3
4
1
2
3
4 * In-kind Payment Codes: ** Meal Codes: 1=tef 2=maize 3=wheat 4=barley 5=sorghum 1=lunch 2=dinner 3=local drink =millet 7=pulses 8=oilseeds 20=other (specify) 50=use of oxen
73
Table XIII. INPUTS USED IN THE TRADITIONAL TEF PLOT 2 Input How much was used? (For Did you pay How did you When was it How much did it cost? Did you pay animals/tractor no.days/hrs) cash or in get it? applied? immediately kind for this (Copy dates from after input? field worksheet) receiving the input or Qt Unit 1 SG2000/ Cash In-Kind Payment did you 1=50 kg Gov’t extension (Birr) receive 2=100 kg 0 no (skip to program Month Week Type Qty. Unit Est. Total credit? 3=kg the next 2 (1,2,...1 (1,2,3,4) 1=tef 1=50 kg Value in 4=liter input) Trader/Market 2) 2=maize 2=100 kg Birr 1 immediate 5=oxen-days 1 yes (proceed 3 Rented 3=kg payment 6=tractor hours to next (animals/tracto 3=wheat 4=liter 2 Credit 7=tractor ha column) r) 4=barley 5=oxen- 3 Both 20= other 4 Own/Saved 5=sorghum days (indicate (specify) 20 Other 6==millet 20=other amt. input (specify) 7= (specify) received on pulses credit) 8=oilseeds 20=other (specify) INPUT VIII1 VIII2 VIII3 VIII4 VIII5 VIII6 VIII7 VIII8 VIII9 VIII10 VIII11 VIII12
100 Seed Treatment Type______Type______
200 Seed Variety (ies) ______Animal Traction 10 First Plowing 11 Second Plowing 12 Third Plowing 13 Fourth Plowing 14 Fifth Plowing 15 Sixth Plowing 23 Trampling/leveling
74
Input How much was used? (For Did you pay How did you When was it How much did it cost? Did you pay animals/tractor no.days/hrs) cash or in get it? applied? immediately kind for this (Copy dates from after input? field worksheet) receiving the input or Qt Unit 1 SG2000/ Cash In-Kind Payment did you 1=50 kg Gov’t extension (Birr) receive 2=100 kg 0 no (skip to program Month Week Type Qty. Unit Est. Total credit? 3=kg the next 2 (1,2,...1 (1,2,3,4) 1=tef 1=50 kg Value in 4=liter input) Trader/Market 2) 2=maize 2=100 kg Birr 1 immediate 5=oxen-days 1 yes (proceed 3 Rented 3=kg payment 6=tractor hours to next (animals/tracto 3=wheat 4=liter 2 Credit 7=tractor ha column) r) 4=barley 5=oxen- 3 Both 20= other 4 Own/Saved 5=sorghum days (indicate (specify) 20 Other 6==millet 20=other amt. input (specify) 7= (specify) received on pulses credit) 8=oilseeds 20=other (specify) INPUT VIII1 VIII2 VIII3 VIII4 VIII5 VIII6 VIII7 VIII8 VIII9 VIII10 VIII11 VIII12
80 Transport to threshing area 81 Transport to storage area
75
Input How much was used? (For Did you pay How did you When was it How much did it cost? Did you pay animals/tractor no.days/hrs) cash or in get it? applied? immediately kind for this (Copy dates from after input? field worksheet) receiving the input or Qt Unit 1 SG2000/ Cash In-Kind Payment did you 1=50 kg Gov’t extension (Birr) receive 2=100 kg 0 no (skip to program Month Week Type Qty. Unit Est. Total credit? 3=kg the next 2 (1,2,...1 (1,2,3,4) 1=tef 1=50 kg Value in 4=liter input) Trader/Market 2) 2=maize 2=100 kg Birr 1 immediate 5=oxen-days 1 yes (proceed 3 Rented 3=kg payment 6=tractor hours to next (animals/tracto 3=wheat 4=liter 2 Credit 7=tractor ha column) r) 4=barley 5=oxen- 3 Both 20= other 4 Own/Saved 5=sorghum days (indicate (specify) 20 Other 6==millet 20=other amt. input (specify) 7= (specify) received on pulses credit) 8=oilseeds 20=other (specify) INPUT VIII1 VIII2 VIII3 VIII4 VIII5 VIII6 VIII7 VIII8 VIII9 VIII10 VIII11 VIII12
Tractor 10 First Plowing 11 Second Plowing Other Inputs 300 DAP Fertilizer
400 Urea Fertilizer
500 Herbicide Type/form. ______
600 Field Insecticide Type/form. ______
76
CP5 _____ Did you split the application of urea during the current season?
0 = no 1 = yes
If yes, how did you split it?
CP6 ______kgs at broadcasting
CP7 ______kgs as top dressing
77 APPENDIX 2: METHODS FOR YIELD ESTIMATION AND AREA MEASUREMENT
78 1. Yield Estimation
The method used for maize plot selection (2 sample plots per field) was as follows.16 First, enumerators asked the farmer to identify the center of the field, then stretched two ropes to transect the field at right angles. The ropes were marked with knots every 4 meters. Standing at the center of the field, the enumerator numbered the quadrants (#1 quadrant is closest to the northwest, numbering continues clockwise). The enumerator then selected two of the four quadrants for the yield sample using a random number table. S/he next (for each selected quadrant) located the starting point for the 2 m x 4 m sample plot by using a random number table and starting from the corresponding knot on the rope. The enumerator marked the area with a 2 m x 4 m bamboo frame, set stakes and cordoned off the plot with twine. S/he then recorded the number of plants and maize ears in the plot, measured the between-plant and between-row spacing, and interviewed the farmer about soil fertility, plot history, and the farmer’s expected yield from the plot.
Following sample plot marking, the farmer was asked to advise the enumerator when he was ready to harvest the field. On that day the enumerator and the farmer harvested the sample plot together, and the enumerator placed the ears in a bag and carried it to the extension agent’s house for safekeeping. When the farmer finished harvesting his field he came to the extension agent’s house to thresh the maize. The supervisor then weighed the grain sample, took a moisture reading and returned the grain to the farmer.
The selection of sample plots in tef fields was done in a slightly different way to minimize crop damage caused by walking in the field. Farmers first identified the boundary points for the field, the enumerator numbered the points (with the point closest to the northwest labeled #1, and continuing clockwise) and used the random number table to identify a starting point. The farmer stood at this point and threw a ball of twine into the field (he was not told why). The point where the twine fell was the starting point for the 2m x 4m quadrant, and the quadrant was marked with stakes and twine as above. After the tef in the sample plot was harvested it was taken to the extension agent’s house to dry. Both the grain and straw were weighed. The farmer threshed the tef and the supervisor weighed and returned the grain to the farmers.
2. Notes on measuring and calculating farm area by Don Beaver and Julie Howard
During round one of the survey, enumerators recorded the coordinates of the field using a handheld GPS and measured the area of the field using compasses and tapes. Prof. Don Beaver of MSU’s Department of Zoology developed a program for the Hewlett Packard (HP) 48G programmable calculator to enable enumerators to calculate field area based on bearings and side
16 This method was based on recommendations from Drs. Rick Ward and Richard Harwood of MSU’s Department of Crop and Soil Sciences and Mr. Tewabe Mihret of the Central Statistical Agency, Addis Ababa.
79 measurements while they were at the site. Notes on measurement techniques and use of the POLY program follow.
A. Basics. The basic idea behind field area measurement is to identify the corners of the field, use a compass to take the bearing (from North) between consecutive (moving clockwise) points, and measure the side lengths between each of the points. The bearing and side length data will be entered into the HP 48 programmable calculators and area and the percent error will automatically be calculated (see point 3 for instructions on using the calculators). The program works by dividing the field up into triangles, calculating the area of each triangle and summing them. The beauty of using the programmable calculators is that you can have the enumerators take the bearings and side measurements, then immediately check the area while everyone is still in the field. If the closing error is greater than 5%, the enumerators should redo the bearings and side measurements.
B. How to measure and record field area. Starting from point A (see figure p. 3), use a compass to take the bearing to the next point (moving clockwise). We found it easiest to do this work in teams, with one person standing at point A taking the bearing to point B, and the second person standing at point B, if possible marking it with a long stick with a piece of cloth tied to it. As the second person walks from point A to point B he or she can also be taking the side measurement AB at the same time with the tape measure. And so on, around the field, one person stands at point B and takes the bearing to point C, the second person marks point C with a stick and cloth and measures BC side length, etc.
The enumerators should be very careful about how they record the bearings and corresponding side lengths. They also need to roughly sketch the shape of the field, marking the corners (A,B,C,D etc.). This is important because in some cases we will have concave rather than convex polygons (see figure) . For a convex polygon, it doesn’t matter what point you start with when entering data into the HP48 program. If the polygon is concave, though, the starting point (that is, the first point that is entered into the HP48 program) MUST be the first point beyond the concavity, moving in a clockwise direction. Otherwise the program will overestimate the field area.
Some enumerators will have a natural aptitude for using a compass, others won’t. We found it useful to spend a day training all enumerators in taking bearings and measuring side lengths, then we tested them to see how well they could read the bearings on their own. We chose the best ones for specialized area measurement teams, and let the rest focus on other survey tasks.
C. Instructions for using HP 48G to determine farm plot area 1. Turn the calculator ON by pressing ON key at lower left-hand corner of calculator.
2. Start the PLGY (POLYGON) program by pressing the white-capped key aligned with the "PLGY" name in the list at the bottom of the calculator display screen. If you don’t see the name "PLGY" at the bottom of the screen, then press the "NXT" key (last right-hand key in the second row of calculator keys, under the row of white-capped
80 keys). If it still doesn’t appear, press the VAR key, then press NXT again. If you have started the PLGY program successfully, you will see "Irregular Polygon Area--key requested data, PRESS ENTER (any key to start) on the screen.
3. Enter the number of decimals (in your side measurements), then press enter.
4. Enter the number of sides of the field, then press enter.
5. The program will prompt you to key in each angle.
6. The program will prompt you to key in the measurement of each side (in meters).
7. The program then automatically calculates the total AREA, and gives you the percent closing error. If the percent error is greater than 5%, the field angles and sides should be re-measured.
8. The program will ask if you want to do ANOTHER RUN. Type is Y or N by pushing the alpha key (4th row from the bottom, 1st key on the left) and then pressing the corresponding key with the letter "Y" or "N," then enter.
9. To TURN THE CALCULATOR OFF, press the right-shift key (RS) (the green key located just above the ON button), then press OFF (same key as the ON key, with OFF written in green above.
10. If you make a mistake and want to reenter the data on that line, press CANCEL (the ON key).
(a). EXAMPLE: Enter the following data (a rectangle of 2 hectares): Angles: 0,90,180,270. Enter each value followed by the ENTER key. Sides: 100,200,100,200. Enter each value followed by the ENTER key. Read Hectares: 2.00 and Percent Error: 0.00 in the display
81 Figure 2. Measuring area for convex and concave polygons. The starting point must be adjusted manually for the concave case to prevent inclusion of extra area, or in very complex shaped concave polygons (more than one concavity), overlapping areas within it.
D. Notes on concave and convex polygons and formulas used in the HP48G POLY program.
Entering Data: For a convex polygon (see figure above), enter data for each angle and its corresponding side length, in meters, from any point, moving clockwise from that point until all data are entered.
For a concave polygon, enter data starting with the first point beyond the concavity in a clockwise direction, (point C above), and continue clockwise from there. When more than one concavity occurs. I suggest breaking the polygon into two or more polygons. Computing the area for each separately, and then add them.
Formula used for the HP48 program.
n Y n X n 1 n n A M (Yi Xi Xi Yi ) M Xi M Yi 2 i 1 n i 1 n i 1 Where Xj Xj Sin and Yj Yj Cos
and
i i Xi M Xj and Yi M Yj j 1 j 1 for j angles and aj sides, with i verticies
82 The correction formula is:
X 2 Y 2 C n n # 100 n
M aj j 1
E. POLY Program
Key-Stroke Definitions: Special Symbols HP48 Code Key Strokes HP48 Code Key Strokes :: RS, :: CLEAR LS, CLEAR ? , LS, (or special chars) CLLCD PRG, NXT, OUT, CLLCD () LS, () DISP PRG, NXT, OUT, DISP {} LS, {} DO UNTIL PRG, BRCH, DO, (DO) END (UNTIL) (END) same for any + - / * FROM KEYBOARD DUP LS, STACK, NXT, DUP = = PRG, TEST, = = FIX LS, MODES, FMT, FIX g PRG, TEST, g GET PRG, LIST, ELEM, GET «» LS, << >> GETI PRG, LIST, ELEM, GETI "" RS, "" IF THEN PRG, BRCH, IF, (IF) ELSE (THEN) (ELSE) Same for any % , RS, U (or special chars) INPUT PRG, NXT, IN, INPUT Next key press a capital OBJ PRG, LIST, OBJ letter, ends automatically Next and subsequent key SAME PRG, TEST, NXT, SAME presses capital letters; end with LS all lower case letter, ends SQ LS, x2 with
83 LS LS First letter upper case, all STO STO rest lower case. (See Manual pg 2.4) BEEP PRG, NXT, OUT, NXT, BEEP RS = Right Shift key (lower left side, blue). LS = Left Shift key (lower left side, purple). Entering a program using the LS «» keys shows in the HP48 as « program codes ». The markers enclose the program. Sub programs within a program are similarly marked. When entering program codes, the editor automatically inserts codes between the symbols. The editor does the same for ( ), { }, : :, and " " codes. I HAVE SHOWN THE CODES BELOW AS THEY APPEAR IN THE HP48 SCREEN. Most commands require a space, the SPC key, between them. When in doubt, put in a space. When entering letters in single or double quotes, after keying to end letters, remember to key right cursor, RC, to move the cursor out of the quotes before entering the next command.
"BEEP" SUBROUTINE HP48 Codes Notes: « Key ENTER to save and 1500 0.1 BEEP end » [[ at this point, key ENTER to save and end]] ‘TN’ STO [[key to store program Saved as TN in a variable name ]]
INPUT SUBROUTINE HP48 Codes Notes: « Key ENTER to save and INPUT OBJ end » [[ at this point, key ENTER to save and end]] ‘I’ STO [[key to store program in Saved as I a variable name ]]
84 STARTUP PROGRAM: POLY HP48 Codes Notes: « Key ENTER to save and TN TN CLLCD " Irregular end. Polygon Area" 2 DISP " Key Requested Data, PRESS ENTER" 4 DISP " (Any key to start)" 7 DISP 0 WAIT 0 FIX CLEAR 1 ‘TT’ STO 1 ‘NN’ STO TN "Decimals in Answer?" ":number:" I ‘D’ STO TN REQA » [[ at this point, key ENTER to save and end]] ‘POLY’ STO [[key to store Saved as POLY program in a variable name ]] INPUT ANGLES SUBROUTINE: REQA HP48 Codes Notes; « TN "How many sides?" ":number:" I ‘CC’ STO 1 ‘TT’ STO 1 ‘NN’ STO WHILE ‘CC gTT-1' REPEAT TN "Key angle" NN + ":degrees:" I 1 NN STO+ 1 ‘TT’ STO+ END CC LIST ‘BNG’ STO 1 ‘NN’ STO 1 ‘TT’ STO TN TN REQL » [[ at this point, key ENTER to save and end]] ‘REQA’ STO [[key to store Saved as REQA program in a variable name ]]
85 INPUT SIDE LENGTHS SUBROUTINE: REQL HP48 Codes Notes: « Key ENTER to save and IF ‘CC gTT-1' end. THEN TN "Key Side" NN + ":length (m):" I 1 ‘NN’ STO+ 1 ‘TT’ STO REQL ELSE TN CC LIST ‘LEN’ STO LEN BNG SIN * ‘XL’ STO LEN BNG COS * ‘YL’ STO CLLCD "Calculating. . ." 4 DISP CALC1 END » [[ at this point, key ENTER to save and end]] ‘REQL’ STO [[key to store Saved as REQL program in a variable name ]]
SUBROUTINE CALC: CALC1 HP48 Codes Notes: « Key ENTER to save and 1 ‘NN’ STO XL NN GETI ‘XC’ end. STO XC NN LIST ‘Xi’ STO DO GETI XC + ‘XC’ STO Xi XC + ‘Xi’ STO UNTIL DUP 1 = = END Xi « + » STREAM ‘XiS’ STO Xi CC GET ‘LSTXI’ STO 1 ‘NN’ STO YL NN GETI ‘YC’ STO YC NN LIST ‘Yi’ STO DO GETI YC + ‘YC’ STO Yi YC + ‘Yi’ STO
86 UNTIL DUP 1 = = END Yi « + » STREAM ‘YiS’ STO Yi CC GET ‘LSTYI’ STO FCALC D FIX CORCT " (Any key to cont.)" 7 DISP 0 WAIT TN TN CLLCD {A PCNT SUM1 SUM2 SUML SUMX SUMY XiYL YiXL LSTYI LSTXI YiS XiS YC XC Yi Xi XL YL LEN BNG CC NN TT} PURGE "ANOTHER RUN? (Y/N)" "" INPUT IF "Y" SAME THEN REQA ELSE TN TN {D} PURGE CLLCD "PROGRAM OVER" 4 DISP 3 WAIT CLEAR END OFF » [[ at this point, key ENTER to save and end]] ‘CALC1' STO [[key to store Saved as CALC1 program in a variable name ]]
87 SUBROUTINE FINAL CALC: FCALC HP48 Codes Notes: « Yi XL * ‘YiXL’ STO Xi YL * ‘XiYL’ STO YiXL XiYL - ‘SUM1' STO SUM1 « + » STREAM ‘SUM2' STO SUM2 LSTYI CC / XiS * LSTXI CC / YiS * - + 2 / 10000 / ‘A’ STO » [[at this point, key ENTER to save and end ]] ‘FCALC’ STO [[key to store Saved as FCALC program in a variable name ]]
SUBROUTINE CORRECT: CORCT HP48 Codes Notes: « LEN « + » STREAM ‘SUML’ STO XL « + » STREAM ‘SUMX’ STO YL « + » STREAM ‘SUMY’ STO SUMX SQ SUMY SQ + SUML / 100 * ‘PCNT’ STO CLEAR CLLCD " Area is: " 2 DISP A " Hectares" + 3 DISP "Percent error:" PCNT + "%" + 5 DISP » [[ at this point, key ENTER to save and end]] ‘CORCT’ STO [[key to store Saved as CORCT program in a variable name ]]
88 APPENDIX 3: FINANCIAL BUDGETS
89 Table 19. Financial Budgets for Tef (East Shoa)
------NEP PROGRAM PLOTS------TRADITIONAL PLOTS------GRADUATE PLOTS------
Budget Item 1.00 2.00 3.00 Total 1.00 2.00 3.00 Total 1.00 2.00 3.00 Total
n used in calculations 20 20 20 60 20 20 20 60 21 18 21 60 1. YIELD 1/
1.A. Grain Yield (kg/ha) 891 1312 1964 1389 838 1357 1896 1364 1006 1361 1985 1455 1.B. Straw Yield (kg.ha) 2174 2092 2272 2180 2149 1913 2013 2025 2369 1991 1842 2071 2. PRICE 2/ 2.A. Estimated farmgate price for grain (birr/kg) 2.04 2.04 2.04 2.04 2.04 2.04 2.04 2.04 2.04 2.04 2.04 2.04 2.B. Estimated farmgate price for straw (birr/kg) 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 3. GROSS REVENUE 3/ 2062 2909 4257 3076 1951 2980 4089 3007 2318 2997 4250 3198 4. TOTAL PACKAGE COSTS 4/ 639 685 643 655 454 651 513 540 637 520 545 571 4.A. Treated seed (birr/ha) 131 159 161 150 145 189 166 167 194 193 182 190 4.B. DAP 242 270 239 251 175 266 200 214 264 200 212 227 4.C. Urea 229 227 223 226 114 175 134 141 152 105 125 129 4.D. Herbicide 372920282021131827222625 4.E. Insecticide 0 0 0 0 0 0 0 0 0 0 0 0 4.F. Fungicide 0000 00000000 5. INTEREST 5/ 30.0 29.7 24.2 28.0 16.6 25.1 18.4 20.0 23.8 15.1 19.5 19.7 6. LABOR 6/ 6.A. Total family/mutual labor days 69 65 56 63 52 65 55 57 92 73 67 78 6.B. Total wage labor (birr/ha) 7/ 145 191 241 192 116 121 190 142 169 178 203 183 7. ANIMAL TRACTION COST (birr/ha) 8/ 251 279 343 291 153 233 244 210 255 182 228 224 8. HAND TOOLS AND SACKS (birr/ha) 5.5 6.1 11.2 6.5 2.9 5.7 7.0 4.9 4.6 5.5 7.4 5.7 8.A. hand tools (birr) 9/ 2.0 2.1 2.5 2.2 1.2 1.8 1.9 1.6 1.7 1.4 1.7 1.6 8.B. Sacks (birr) 10/ 3.5 4.0 8.7 4.3 1.7 3.9 5.1 3.3 2.9 4.1 5.7 4.1 9. NET INCOME/HA 11/ 991 1718 2994 1904 1209 1945 3116 2091 1229 2097 3247 2194 10. NET INCOME/FAMILY-MUTUAL LABOR DAY 12/ 14.4 26.4 53.5 30.2 23.2 29.9 56.7 36.7 13.4 28.7 48.5 28.1
90 Notes to Table 19 1/Yield estimates based on crop cuts taken as part of the GMRP/MSU/MOA/SG2000 Survey. Assumes no grain or straw was lost during threshing. 2/Source: EGTE price monitoring unit and GMRP/MSU/MOA/SG2000 Survey. Local market prices collected by EGTE were adjusted to farmgate prices using survey data on prices reported by farmers. Average prices for white tef during January 1998 are used. 3/(1.A*2.A) +(1.B. *2.B) 4/ 4.A + 4.B. + 4.C. + 4.D. + 4.E. + 4.F. MOA tef package consists of (quantities/ha) 35 kg seed, 100 kg DAP, 100 kg urea, U-46 herbicide. 5/Source: GMRP/MSU/MOA/SG2000 Survey and rate information from MOA/SG2000. MOA program participants pay 10% interest annually. Assumes that period of loan is 7 months. 6/Source: GMRP/MSU/MOA/SG2000 Survey. 7/Valued at cash/in-kind payment rates provided by survey participants. 8/Sum of (a) rental costs reported by survey respondents and (b) for owned/borrowed oxen, maintenance + depreciated value of animals and animal traction equipment multiplied by percentage of total farm represented by the program, traditional or graduate plot. Purchase price, life and salvage value of equipment based on supervisors’ field reports. 9/Depreciated value of 2 sickles, 2 hoes, and 2 spades. Purchase price, life and salvage value of equipment based on supervisors’ field reports. 10/Depreciated value of sacks needed to transport tef marketed in 1998 season. Since sacks are retained by farmer and used for other purposes, cost is apportioned by multiplying depreciated sack value by percentage of total farm represented by program, traditional or graduate plot. Purchase price, life and salvage value based on supervisors’ field reports. 11/(3) = (4 + 5 + 6B + 7 + 8) 12/(9)/(6.C.)
91 Table 20. Financial Budgets for Maize (West Shoa)
------MOA/SG PLOTS------GRADUATE PLOTS------
Budget Item 1.00 2.00 3.00 Total 1.00 2.00 3.00 Total
n used in calculations 30 31 31 92 19 19 19 57 1. GRAIN YIELD (kg/ha) 1/ 3883 5493 7234 5554 2935 4579 6895 4803 1.A. Adjusted grain yield (kg/ha) 1/ 3732 5279 6952 5337 2821 4400 6626 4616 2. FARMGATE PRICE (birr/kg) 2/ 0.69 0.69 0.69 0.69 0.69 0.69 0.69 0.69 3. GROSS REVENUE 3/ 2575 3642 4797 3683 1946 3036 4572 3185 4. TOTAL PACKAGE COSTS 4/ 781 742 709 743 153 171 649.6 325 4.A. Treated seed (birr/ha) 142 136 130 136 74 80 124 93 4.B. DAP 281 268 256 268 56 47 231 111 4.C. Urea 264 252 241 252 19 33 217 90 4.D. Herbicide 4 0 0 1 0 0 3.6 1 4.E. Insecticide 90 86 82 86 4 11 74 30 4.F. Fungicide 0 0 0 0 0 0 0 0 5. INTEREST 5/ 0.0 0.0 0.0 0.0 2.2 6.0 39.4 15.9 6. LABOR 6/ 6.A. Total family/mutual labor days 7/ 164 143 167 159 177 240 200 206 6.B. Total wage labor (birr/ha) 8/ 138 124 108 123 103 75 54 77 7. ANIMAL TRACTION COST (birr/ha) 9/ 105 88 87 93 58 73 91 74 8. HAND TOOLS AND SACKS (birr/ha) 19.6 31.1 42.8 28.7 14.3 21.6 42.9 20.5 8.A. Hand tools (birr) 10/ 1.5 1.5 1.8 1.6 1.2 1.6 1.8 1.5 8.B. Sacks (birr) 11/ 18.1 29.5 41.0 27.1 13.1 20.1 41.1 19.0 9. NET INCOME/HA 12/ 1531 2657 3850 2695 1616 2690 3695 2672 10. NET INCOME/FAMILY-MUTUAL LABOR DAY 13/ 9.3 18.6 23.1 16.9 9.1 11.2 18.5 13.0
92 Notes to Table 20 1/Yield estimates based on crop cuts taken as part of the GMRP/MSU/MOA/SG2000 Survey. Assumes (a) no grain was lost during threshing; (b) maize was harvested in November; (c) storage losses were 1.98% per month, the average of various estimates from Abraham et al. 1993. 2/Source: EGTE price monitoring unit and GMRP/MSU/MOA/SG2000 Survey. Local market prices collected by EGTE were adjusted to farmgate prices using survey data on prices reported by farmers. Average prices for maize during January 1998 are used. 3/ (1)* (2) 4/ 4.A. + 4.B. + 4.C. + 4.D. + 4.E. + 4.F. MOA/SG maize package consists of (quantities/ha) 25 kg seed, 100 kg DAP, 100 kg urea. 5/Source: GMRP/MSU/MOA/SG2000 Survey and rate information from MOA/SG2000. MOA/SG2000 program participants pay no interest; NEP program participants pay 10% interest annually. Assumes that period of loan is 10 months. 6/Source: GMRP/MSU/MOA/SG2000 Survey. 7/Includes shelling labor. 8/Valued at cash/in-kind payment rates provided by survey participants. 9/Sum of (a) rental costs reported by survey respondents and (b) for owned/borrowed oxen, maintenance + depreciated value of animals and animal traction equipment multiplied by percentage of total farm represented by the program, traditional or graduate plot. Purchase price, life and salvage value of equipment based on supervisors’ field reports. 10/Depreciated value of 2 hoes, 2 axes, and 2 cutting knives. Purchase price, life and salvage value of equipment based on supervisors’ field reports. 11/Depreciated value of sacks needed to transport maize marketed in 1998 season. Since sacks are retained by farmer and used for other purposes, cost is apportioned by multiplying depreciated sack value by percentage of total farm represented by program, traditional or graduate plot. Purchase price, life and salvage value based on supervisors’ field reports. 12/(3) - (4 + 5 + 6B + 7 + 8) 12/(9)/(6.C.)
93 Table 21. Financial Budgets for Maize (Jimma)
Budget Item ------MOA/SG------TRADITIONAL PLOTS------GRADUATE PLOTS------
1 2 3 mean 1 2 3 mean 1 2 3 mean n used in calculations 22 24 23 69 15 16 16 47 13 13 13 39 1. GRAIN YIELD (kg/ha) 1/ 4113 5446 6907 5508 1579 2666 4119 2814 5073 6901 8369 6781 1.A. Adjusted grain yield (kg/ha) 1/ 3953 5234 6638 5293 1517 2562 3958 2704 4875 6632 8043 6516 2. FARMGATE PRICE (birr/kg) 2/ 0.54 0.54 0.54 0.54 0.54 0.54 0.54 0.54 0.54 0.54 0.54 0.54 3. GROSS REVENUE 3/ 2153 2851 3616 2883 827 1396 2156 1473 2656 3612 4381 3550 4. TOTAL PACKAGE COSTS 4/ 603 679 634 640 247 306 280 279 585 635 596 606 4.A. Treated seed (birr/ha) 122 137 128 129 45 37 37 40 118 128 120 122 4.B. DAP 248 279 260 263 202 269 243 239 240 261 245 249 4.C. Urea 233 263 245 248 0 0 0 0 226 246 231 235 4.D. Herbicide 001.20.400000000 4.E. Insecticide 0 0 0 0 0 0 0 0 1.3 0 0 0.4 4.F. Fungicide 0000 00000000 5. INTEREST 5/ 0 0 0 0 4.0 4.2 0 2.7 38.1 39.1 37.6 38.3 6. LABOR 6/ 6.A. Total family/mutual labor days 7/ 122 142 141 135 84 99 91 92 123 145 150 140 6.B. Total wage labor (birr/ha) 8/ 51 59 76 62 20 39 48 36 63 52 97 71 7. ANIMAL TRACTION COST (birr/ha) 9/ 86 106 101 98 89 101 143 112 175 165 300 213 8. HAND TOOLS AND SACKS (birr/ha) 27.8 43.9 47.8 39.2 7.6 12.6 17.8 13.5 54.3 94.1 84.7 77.7 8.A. Hand tools (birr) 10/ 2.6 3.0 2.9 2.8 2.7 3.1 3.0 2.9 4.9 4.2 7.4 5.5 8.B. Sacks (birr) 11/ 25.3 40.9 44.9 36.3 4.9 9.5 14.8 10.5 49.4 89.9 77.3 72.2 9. NET INCOME/HA 12/ 1385.2 1962.9 2756.6 2043.7 458.9 932.8 1667.3 1029.8 1739.8 2627.2 3265.6 2543.2 10. NET INCOME/FAMILY-MUTAL LABOR DAY 13/ 11.4 13.8 19.6 15.1 5.5 9.4 18.3 11.2 14.1 18.1 21.8 18.2
94 Notes to Table 21 1/Yield estimates based on crop cuts taken as part of the GMRP/MSU/MOA/SG2000 Survey. Assumes (a) no grain was lost during threshing; (b) maize was harvested in November; (c) storage losses were 1.98% per month, the average of various estimates from Abraham et al. 1993. 2/Source: EGTE price monitoring unit and GMRP/MSU/MOA/SG2000 Survey. Local market prices collected by EGTE were adjusted to farmgate prices using survey data on prices reported by farmers. Average prices for maize during January 1998 are used. 3/ (1)* (2) 4/ 4.A. + 4.B. + 4.C. + 4.D. + 4.E. + 4.F. MOA/SG maize package consists of (quantities/ha) 25 kg seed, 100 kg DAP, 100 kg urea. 5/Source: GMRP/MSU/MOA/SG2000 Survey and rate information from MOA/SG2000. MOA/SG2000 program participants pay no interest; NEP program participants pay 10% interest annually. Assumes that period of loan is 10 months. 6/Source: GMRP/MSU/MOA/SG2000 Survey. 7/Includes shelling labor. 8/Valued at cash/in-kind payment rates provided by survey participants. 9/Sum of (a) rental costs reported by survey respondents and (b) for owned/borrowed oxen, maintenance + depreciated value of animals and animal traction equipment multiplied by percentage of total farm represented by the program, traditional or graduate plot. Purchase price, life and salvage value of equipment based on supervisors’ field reports. 10/Depreciated value of 2 hoes, 2 axes, and 2 cutting knives. Purchase price, life and salvage value of equipment based on supervisors’ field reports. 11/Depreciated value of sacks needed to transport maize marketed in 1998 season. Since sacks are retained by farmer and used for other purposes, cost is apportioned by multiplying depreciated sack value by percentage of total farm represented by program, traditional or graduate plot. Purchase price, life and salvage value based on supervisors’ field reports. 12/(3) - (4 + 5 + 6B + 7 + 8) 12/(9)/(6.C.)
95 REFERENCES
Abraham Tadesse, Firdu Azerefagu, Assefa GebreAmlak, and Adhanom Negassi. 1993. Research Highlights on Maize Insect Pests and their Management in Ethiopia in Proceedings of the Firt National Maize Workshop of Ethiopia, Benti Tolessa and Joel Ransom (eds.). Addis Ababa: IAR and CIMMYT.1993.
Central Statistical Authority. 1998. Agricultural Sample Surveys, Meher Season Crops, 1997/98. CSA Bulletin 171. Addis Ababa: CSA.
Federal Democratic Republic of Ethiopia. 1997. Agricultural Sample Survey 1996-97: Vol. 1: Report on Area and Production for Major Crops. Statistical Bulletin 171. Addis Ababa: Central Statistical Authority.
Habtemariam Abate. June 1997. Targeting Extension Service and the Extension Package Approach in Ethiopia. Ministry of Agriculture, Addis Ababa, Ethiopia.
Sasakawa-Global 2000 Agriculture Project in Ethiopia. 1996. Annual Report Crop Season 1995. Addis Ababa: SG2000.
World Bank. April 24, 1995. Staff Appraisal Report, Ethiopia, National Fertilizer Sector Project. Report No. 13722-ET. Washington, D.C.
96