GEOSPATIAL MODELING OF MAIZE

GEOSPATIAL MODELING OF MAIZE PRODUCTION TECHNOLOGY IN ETHIOPIA

by

Feyera Merga Liben

A DISSERTATION

Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of the Requirements For the Degree of Doctor of Philosophy

Major: Agronomy and Horticulture (Crop Physiology and Production)

Under the Supervision of Professor Charles S. Wortmann

Lincoln, Nebraska August, 2018



   

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GEOSPATIAL MODELING OF MAIZE PRODUCTION TECHNOLOGY IN ETHIOPIA Feyera Merga Liben, Ph.D. University of Nebraska, 2018 Advisor: Charles S. Wortmann Maize (Zea mays L.) is an important food crop in Ethiopia but yield is low due to numerous biotic, abiotic and management constraints. Conservation agriculture (CA) and better nitrogen management in targeted technology extrapolation domains (TED) could reduce some of these constraints. Generation of good agronomic practices for all of the diverse TED of Ethiopia through field research alone is not feasible due to resource scarcity in Ethiopia but use of crop simulation models coupled with geographic information systems (GIS) may greatly complement field research. A robust procedure was developed for the application of geospatial modeling of CA and N management practices in Ethiopia. Field study results indicated improvements in soil properties and crop yield may require some yr of CA before crop yield and soil benefits are achieved. Evaluation of CERES-Maize, CROPGRO-Dry bean, and CROPGRO-Soybean crop models under different cropping conditions suggested their suitability for simulating maize and legume responses to N rates and CA in Ethiopia. Split application of N at planting, at 40, and 60 days after planting greatly reduced N leaching and slightly improved maize yield at all TED. Either CAr (reduced tillage with 30% of crop residue retention and 75 kg N ha-1 under maizesoybean/dry bean rotation) or CAr+N (CAr but with 100 kg N ha-1) may be used for sustainable maize production across the target TED. Model generated maize N response coefficients varied for the conventional and conservation agriculture production conditions and the coefficients can be applied to optimize N fertilizer use at their respective TED in Ethiopia.

iii DEDICATION

Dedicated to poor smallholder farmers in sub-Saharan Africa.

iv ACKNOWLEDGEMENTS

God’s guidance in my life is always great. I would like to thanks Charles Wortmann, my advisor during this period. Charles trusted in me, and gave me the opportunity of developing a doctorate program with entire freedom and resources availability. He always had his door wideopen and unlimited time to discuss ideas even on phone when I was far apart due to an unusual situation. Thanks a lot, I only wish I can someday give you back at least a part of what you gave me. I had the pleasure of having on my committee not only renowned but also generous scientists. I enjoyed interacting with and gain much direction from Haishun Yang, John Lindquist, Tsegaye Tadesse, and Hae Koo Kim, and I appreciate their efforts in working with me although separated by much physical and situational distance. Tesfaye Tesso and Kindie Tesfaye, thank you for the recommendation letters. I appreciate the University of Nebraska, Department of Agronomy and Horticulture, which provided a friendly environment, the financial assistance and other innumerable resources, and the possibility of multiple interactions. My field research work was funded by the Australian Centre for International Agricultural Research (ACIAR) through the Sustainable Intensification of Maize-Legume Cropping Systems for Food Security in Eastern and Southern Africa Program (SIMLESA). The institutional support of the Ethiopian and Oromia Agricultural Research Institutes were essential to the completion of the field work. Thanks to Gemechu Gadisa, Bahiru Tilahun, Diriba Hika, Tadesse Brihanu and Mulugeta Shuba for your unreserved help during field experiment establishment and data collection in Ethiopia. I thank Alemu Tirfessa, Kasaye Negash, Dereje Ayalineh, Tolera Keno, Tewodros Mesfin, and Shiferahu Tadesse for allowing me to use their crop and soil data. I

v would like to thank my brother Bayisa Merga for editing this dissertation. Thanks go to Dagne Wegary for the encouragement and advice during thesis write up. Special thanks to Nagera Kenate, Aga Tefera Abetu, Ali Hassen and Temesgen Goshu for sharing office and encouragement during paper write up at Adama in Ethiopia. Social life has great value. Tadele and Fistum, special thanks to you for arranging things for me to adapt life in Lincoln easily. Thanks to Neway and Hirut, Abere and Meskerem, Getachew and Eleni, Tsegaye and Konjit, Teshome and family, Wendewesen and family, and Charles Wortmann family. Special thanks to Tewodros Yosef Yimer and Yared Ashenafi Bayisa for help and for making my stay in Lincoln joyful and pleasant. My wife Fenet Alemu, and our son Olansa and daughter Jitu, this accomplishment would have not been possible without your love and endurance in those years. Thank you for your patience and kindness when I was away for the study. Fenet, thank you so much for loving and caring for our children during this important time, and for motivating me to work hard. I would like to thank my parents Merga Liben and Aregash Siyoum for providing necessities till this period. I am grateful for the encouragement and prayer. I am also thankful to my mother-in-law (Chali Urgi) and father-in-law (Alemu Negewo) for family support when I was away. Thanks go to brothers-in-law, sisters-in-law and my brothers and sisters for your love and encouragement.

vi Table of Contents CHAPTER 1: GENERAL INTRODUCTION .............................................................................. 1 1.1. Background Information ..................................................................................................... 1 1.2. Statements of the Problems and Hypothesis ....................................................................... 3 1.3. Study Objectives ................................................................................................................. 4 1.3.1. Overall Objective ......................................................................................................... 4 1.3.2. Specific Objectives ...................................................................................................... 4 1.4. Methodological Overview of the Study.............................................................................. 4 References ..................................................................................................................................... 6 CHAPTER 2: CONSERVATION AGRICULTURE FOR MAIZE AND BEAN PRODUCTION IN THE CENTRAL RIFT VALLEY OF ETHIOPIA ........................................ 9 Abstract ......................................................................................................................................... 9 2.1. Introduction .......................................................................................................................... 10 2.2. Materials and Methods ......................................................................................................... 13 2.2.1. The Study Area .............................................................................................................. 13 2.2.3. Site Description and Crop Management ........................................................................ 14 2.3. Measurements ....................................................................................................................... 17 2.4. Statistical Analysis ............................................................................................................... 20 2.5. Results .................................................................................................................................. 21 2.5.1. Maize and Bean Phenology at Melkassa ....................................................................... 21 2.5.2. Maize Grain and Stover Yield ....................................................................................... 21 2.5.3. Bean Grain and Straw Yield .......................................................................................... 24 2.5.4. Rainfall Productivity...................................................................................................... 25 2.5.5. Stored Soil Water ........................................................................................................... 27 2.6. Discussion ............................................................................................................................ 29 2.7. Conclusion ............................................................................................................................ 32 References ................................................................................................................................... 33 CHAPTER 3: CONSERVATION AGRICULTURE EFFECTS ON CROP PRODUCTIVITY AND SOIL PROPERTIES IN ETHIOPIA ................................................... 39 Abstract ....................................................................................................................................... 39 3.1. Introduction .......................................................................................................................... 40 3.2. Materials and Methods ......................................................................................................... 42

vii 3.2.1. Characterization of the Study Sites................................................................................ 42 3.2.2. Field Experiments .......................................................................................................... 43 3.2.3. Soil Analysis and Data Collection ................................................................................. 45 3.2.4. Statistical Analysis......................................................................................................... 48 3.3. Results .................................................................................................................................. 49 3.3.1. Soil Properties ................................................................................................................ 49 3.3.2. Crop Growth and Yield ................................................................................................. 53 3.4. Discussion ............................................................................................................................ 57 3.4.1. Soil Properties ................................................................................................................ 57 3.4.2. Crop Performance .......................................................................................................... 59 3.4.3. Crop Growth Stages and Yields .................................................................................... 61 3.5. Conclusion ............................................................................................................................ 62 References ................................................................................................................................... 63 CHAPTER 4: CROP MODEL AND WEATHER DATA GENERATION EVALUATION FOR CONSERVATION AGRICULTURE IN ETHIOPIA ............................ 70 4.1. Introduction .......................................................................................................................... 71 4.2. Materials and Methods ......................................................................................................... 75 4.2.1. Field Experiments .......................................................................................................... 75 4.2.1.1. Experiment-I ........................................................................................................... 75 4.2.1.2. Experiment-II .......................................................................................................... 78 4.2.1.3. Experiment-III ......................................................................................................... 79 4.2.1.4. National Variety Trials............................................................................................ 79 4.3. Model Initialization .............................................................................................................. 80 4.4. Statistical Indices .................................................................................................................. 81 4.5. Weather Datasets and Their Evaluation ............................................................................... 84 4.6. Model Calibration and Evaluation ....................................................................................... 85 4.7. Results .................................................................................................................................. 89 4.7.1. Genetic Coefficients and Model Evaluation .................................................................. 89 4.7.2. Evaluation of Generated Weather Datasets Using Observed Weather .......................... 92 4.7.3. Combined Weather Dataset ........................................................................................... 98 4.7.4. Evaluation of Generated Weather Datasets Using Simulation Modeling ..................... 98 4.8. Discussion .......................................................................................................................... 101 4.8.1. Crop Model Performance ............................................................................................ 101

viii 4.8.2. Suitability of Generated Weather Datasets .................................................................. 102 4.8.2.1. Evaluation Using Observed Weather Dataset ....................................................... 102 4.8.2.2. Evaluation Using Simulation Modeling ................................................................ 104 4.9. Conclusion .......................................................................................................................... 104 References ................................................................................................................................. 106 Appendices ................................................................................................................................ 114 CHAPTER 5: GEOSPATIAL MODELING OF CONSERVATION AGRICULTURE AND NITROGEN MANAGEMENT STRATEGIES IN ETHIOPIA ..................................... 116 Abstract ..................................................................................................................................... 116 5.1. Introduction ........................................................................................................................ 117 5.2. Materials and Methods ....................................................................................................... 121 5.2.1. Site Description and Environmental Characterization ................................................ 121 5.2.2. Model Description ....................................................................................................... 123 5.2.3. Soil and Weather Data Sources ................................................................................... 125 5.2.4. Long Term Simulation Design .................................................................................... 126 5.2.4.1. Nitrogen Application Time ................................................................................... 126 5.2.4.2. Conservation Agriculture and Conventional Production ...................................... 127 5.2.4.3. Nitrogen Response Functions ............................................................................... 127 5.2.5. Data and Analysis ........................................................................................................ 128 5.2.5.1. Analysis of Variance ............................................................................................. 128 5.2.5.2. Trend Analysis over Time..................................................................................... 129 5.2.5.3. Stochastic Dominance Analysis ............................................................................ 129 5.2.5.4. Maize Nitrogen Response Function Determination .............................................. 130 5.2.5.5. Fertilizer Nitrogen Economic Analysis ................................................................. 130 5.3. Results ................................................................................................................................ 131 5.3.1. Nitrogen Application Time .......................................................................................... 131 5.3.2. Conservation and Conventional Strategies .................................................................. 134 5.3.2.1. Effects on Yield and Soil Properties ..................................................................... 134 5.3.2.2. Maize Yield and Soil Properties over Time .......................................................... 138 5.3.2.3. Management Effect on Yield Stability and Risk .................................................. 140 5.3.3. Maize Response to Nitrogen ........................................................................................ 142 5.4. Discussion .......................................................................................................................... 149 5.4.1. Effects of N Application Time Strategies .................................................................... 149

ix 5.4.2. Yield and Soil Improvement ........................................................................................ 149 5.4.3. Conservation Agriculture Performance over Time...................................................... 151 5.4.4. Yield Stability and Risk Reduction ............................................................................. 152 5.4.5. Optimizing Nitrogen Use in Ethiopia .......................................................................... 154 5.5. Conclusion .......................................................................................................................... 156 References ................................................................................................................................. 158 CHAPTER 6: GENERAL DISCUSION AND CONCLUSION .............................................. 167 6.1. Discussion .......................................................................................................................... 167 6.2. Conclusion .......................................................................................................................... 173 References ................................................................................................................................. 175

1

CHAPTER 1: GENERAL INTRODUCTION 1.1. Background Information Agriculture accounts for 46% of the gross domestic product (GDP) and 85% of total employment in Ethiopia (Admassu, 2013b). Ethiopian agriculture is mostly dependent on rainfall and often on degraded soil and the country suffers from frequent droughts and unsustainable agricultural production practices (CIA, 2012). Maize (Zea mays L.) is the second most widely cultivated crop and is first in production with smallholder farmers accounting for 94% its production in Ethiopia (Shiferaw et al., 2011; Tesfaye et al., 2015b).Maize grain is the most consumed food and very important to smallholder livelihood in Ethiopia. Despite this, maize production is not sufficient and yields remain among the lowest in the world because of different biotic, abiotic and management constraints (Ray et al., 2012). Land degradation and declining soil fertility, soil water deficits due to low and erratic rainfall, and inadequate use of good agronomic practices (such as for optimized fertilizer use, soil and water conservation, and maintenance of soil organic matter) are among the constraints ( Sheferaw et al., 2011;Admassu et al., 2013a; Tesfaye et al.,, 2015a). Increased climate variability will challenge the use of Good Agricultural Practices (GAP) for sustainable intensification and these will need to be well-targeted spatially and temporally to increase maize production (Tesfaye et al., 2015b). Estimates indicated that current maize yield in Ethiopia could be doubled if improved maize production GAP such as fertilizer use optimization and conservation agriculture specific to different maize production situations will be widely applied by the smallholder farmers. The means to achieve this could be through identifying and widely applying fertilizer use optimization practices (e.g., crop nutrient response function based application of optimum nutrient level, appropriate N application time) and

2 conservation agriculture technology (crop rotation, minimum tillage, and crop residue retention) as suitable for heterogeneous maize production zones in Ethiopia. Nitrogen response functions, N application time, and conservation agriculture GAP are available only for few locations despite complex terrain in maize production regions in Ethiopia. Conducting field research to generate suitable levels of these GAP for countries with heterogeneous crop growing zones is costly and takes much time. In this regard, geospatial modeling using crop growth simulation models could be useful. Successful geospatial modeling with technology extrapolation domains (TED) could reduce research cost and shorten the time between development and transfer of agronomic GAP to farmers. Since soil varies from place to place, it is also costly to make geospatial modeling at the required homogeneous soil grids. So, though it neglects soil property, a region can be divided into agroclimatic zones based on homogeneity in weather variables that have greatest influence on crop growth and yield for geospatial modeling purpose (Van Wart et al., 2013b). Various simulation models have been evaluated for technology transfer (Thornton et al., 1997; Ollenburger and Snapp, 2015) and for scaling up of desirable GAP (Van Wart et al., 2013a; Van Wart et al., 2013b). Technology extrapolation domains and agroecological zones were used to identify yield variability and limiting factors for crop growth (Caldiz et al., 2002; Williams et al., 2008), to regionalize optimal crop management recommendations (Seppelt, 2000), to compare yield trends (Gallup and Sachs, 2000), to determine suitable locations for new crop production GAP (Geerts et al., 2006; Araya et al., 2010), and to analyze impacts of climate change on agriculture (Fischer et al., 2005). Van Wart et al. (2013a) reported Global Yield Gap Atlas (GYGA) developed climate-based TED to extrapolate maize yield potential

3 globally. The GYGA TED for Ethiopia can be used for geospatial modeling as it could reduce uncertainty associated with GAP transfer from field to regional scale.

1.2. Statements of the Problems and Hypothesis Nutrient response function, N application time and CA GAP may be TED-specific. Estimates of these GAP specific to maize production conditions derived from research plots are available only for a limited number of locations due to cost and time required for field research studies in Ethiopia. In a recent comparison of potential and actual yield of maize across a range of cropping systems and environments, van Ittersum et al. (2013) concluded that use of crop growth simulation with a long-term weather database provides a more robust estimate than research plots because simulation better accounts for the impact of variation in temperature, solar radiation, and rainfall over time. But use of crop models also requires reliable locationspecific crop, soil and weather data which are generally not available for most maize growing locations in Ethiopia. Therefore, a robust extrapolation procedure must minimize uncertainties to extend coverage of estimates for nutrient response functions, N application time and CA GAP to areas where research has not been conducted. Meeting the challenge depends largely on scientists’ ability to identify and transfer reliable GAP using geo-spatial modeling with reduced associated uncertainties. Therefore, well developed geospatial modeling could help to extrapolate maize production GAP to assist farmers increase their crop profit and reduce crop production risks.

4 1.3. Study Objectives 1.3.1. Overall Objective The overall objective of the study was to develop a robust procedure to extrapolate maize production GAP to areas where field research has not been conducted in Ethiopia. 1.3.2. Specific Objectives The specific objectives of the study were to: 1. study the effects of conservation and conventional maize-based systems on maize, bean and soybean productivity and soil properties in Ethiopia. 2. evaluate crop models and generated weather data for conservation agriculture and maize response to N simulations in Ethiopia. 3. model conservation agriculture and nitrogen management strategies for sustainable and profitable maize production in Ethiopia.

1.4. Methodological Overview of the Study Medium and short-term rainfed exploratory maize-based conservation agriculture trials were conducted at Melkassa (8˚24’N and 39˚21’E) and Bako (9o07’N and 37o03’E) agricultural research centers in Ethiopia. Melkassa and Bako represent low and high maize growing potential areas, respectively. Treatments were developed by combining two tillage types with four cropping systems. Tillage included no-till with 100% crop residue retention and conventional tillage (tilled 3 times per cropping season) with residue removal. The cropping systems were maize-legume rotation, maize-legume intercropping, sole maize, and sole legume. Crop growth and yield data as well as soil chemical and physical properties were collected, statistically analyzed for treatments comparison, and reported.

5 Crop growth and yield, soil properties, crop management information and yield data from national variety trials conducted at Melkassa and Bako were used to calibrate and evaluate the CERES-Maize model, CROPGRO-Dry bean and CROPGRO-Soybean models, embedded in the Decision Support System for Agrotechnology Transfer (DSSAT) package. The evaluated models were used to simulate maize N application time, maize N response function, and different maize management strategies under conservation and conventional systems. Longterm (30 yr) weather data were generated using a weather generator and coupled with HarvestChoice-generated soil profile data for model simulations at 16 sites within7 GYGA TED. Grain yield, soil organic C and N output data from the model simulations were analyzed. Analyses of variance, trend analysis, stochastic dominance, and profitability analysis were conducted. Based on results from model output analysis, suitable N application time and conservation agriculture strategies were identified, and N response functions were generated for the targeted TED in Ethiopia. Finally, procedures followed during the geospatial modeling were presented schematically for other similar applications in the future.

6 References Admassu, H., G. Getinet, T.S Timothy, M. Waithaka, and M. Kyotaliyme. 2013a. East African agriculture and climate change: a comprehensive analysis. In: M. Waithaka, G.C. Nelson, T.S. Timothy, and M. Kyotaliyme (Eds.), Climate Change in Africa. International Food Policy Research Institute, Washington, DC. pp. 377-379. Admassu, H.A. 2013b. Enhancing response farming for improved strategic and tactical agronomic adaptation to seasonal rainfall variability under the semi-arid conditions of Ethiopia. Ph.D. thesis, University of Agriculture, Morogoro, Tanzania. Araya, A., S.D. Keesstra, and L. Stroosnijder. 2010. A new agro-climatic classification for crop suitability zoning in northern semi-arid Ethiopia. Agric. Forest Meteorol. 150, 1057– 1064. Caldiz, D.O., A.J.Haverkort, and P.C.Struik. 2002. Analysis of a complex crop production system in interdependent agro-ecological zones: a methodological approach for potatoes in Argentina. Agric. Syst. 73: 297–311. CIA (Central Intelligence Agency of the United States of America). 2012. Ethiopian Economy.http://www.theodora.com/wfbcurrent/Ethiopia/Ethiopia_economy.htm. Accessed on 12 Apr. 2017. Fischer, G., M. Shah, F.N. Tubiello, and H. Van Velhuizen. 2005. Socio-economic and climate change impacts on agriculture: an integrated assessment, 1990–2080. Phil. Trans. R. Soc. Lond. B. 360: 2067–2083. Gallup, J.L., and J.D. Sachs. 2000. Agriculture, climate, and technology: why are the tropics falling behind? Am. J. Agric. Econ. 82, 731–737.

7 Geerts, S., D. Raes, M.Garcia, C. Del Castillo, and W. Buytaert. 2006. Agro-climatic suitability mapping for crop production in the Bolivian Altiplano: a case study for quinoa. Agric. Forest Meteorol. 139: 399–412. Ollenburger, M., and S. Snapp. 2015. Model Applications for Sustainable Intensification of Maize-Based Smallholder Cropping in a Changing World. In: Ahuja, L. R., L. Ma, and R. J. Lascano (Eds.), Practical Applications of Agricultural System Models to Optimize the Use of Limited Water. Advances in Agricultural Systems Modeling Transdisciplinary Research, Synthesis, and Applications, Lajpat R. and Ahuja, Series Editor. Volume 5: 375-397. Ray, D.K., N. Ramankutty, N.D. Mueller, P.C. West, and J.A. Foley. 2012. Recent patterns of crop yield growth and stagnation. Nature Commun. 3:1293. Seppelt, R. 2000. Regionalized optimum control problems for agroecosystem management. Ecol. Model. 131: 121–132. Shiferaw,B., B.M.Prasanna, J. Hellin, and M. Bänziger. 2011. Crops that feed the world. Past successes and future challenges to the role played by maize in global food security. Food Security, Springer. 3: 307-327. Tesfaye, K., M. Jaleta, P. Jena, and M. Mutenje. 2015a. Identifying Potential Recommendation Domain for Conservation Agriculture in Ethiopia, Kenya, and Malawi. Journal of Environ. Manage. 55:330-346. Tesfaye, K., S. Gbegbelegbe, J. E Cairns, B. Shiferaw, B.M. Prasanna, K. Sonder, K. Boote, D. Makumbi, and R. Robertson. 2015b. Maize systems under climate change in sub Saharan Africa. Internat. J. Climate Change Strat. Manage. 7:247-271.

8 Thornton, P.K., W.T. Bowen, A.C Ravelo, P.W.Wilkens, G. Farmer, J. Brock, and J.E. Brink. 1997. Estimating millet production for famine early warning: an application of crop simulation modelling using satellite and ground-based data in Burkina Faso. Agric. For. Meteorol. 83: 95-112. Van Wart, J., L.G.J. van Bussel, J.Wolf, R. Licker, P. Grassini,A. Nelson, H. Boogaard, J. Gerber, N, D. Mueller, L. Claessens, M. K. van Ittersum, and K. Cassman.2013a. Use of agro-climatic zones to upscale simulated crop yield potential. Field Crops Res. 143:4455. Van Wart, J., K.C. Kersebaum, S. Peng, M. Milner, and K. G. Cassman. 2013b. Estimating crop yield potential at regional to national scale. Field Crops Res. 143:34-43. Williams, C.L., M. Liebman, J.W. Edwards, D.E. James, J.W. Singer, R. Arritt, and D. Herzmann. 2008. Patterns of regional yield stability in association with regional environmental characteristics. Crop Sci. 48: 1545–1559.

9 CHAPTER 2: CONSERVATION AGRICULTURE FOR MAIZE AND BEAN PRODUCTION IN THE CENTRAL RIFT VALLEY OF ETHIOPIA Abstract Conservation agriculture (CA) can be a means to soil improvement and increased crop productivity but had not been evaluated for maize (Zea mays L.)-dry bean (Phaseolus vulgaris L.) cropping systems in the semiarid Central Rift Valley of Ethiopia (CRV). Therefore, on-farm (2011-2014) and on-station (2010-2014) trials were conducted to compare CA with the current smallholder conventional practice (CP) for productivity of maize-bean cropping systems. Maize monoculture (MMC), bean monoculture (BMC), maize-bean rotation (MBR), and maize-bean intercropping (MBI) were compared with and without tillage on-station. In on-farm research, MMC under CP (CP_MMC) was compared with cropping systems under CA including MMC (CA_MMC), MBR (CA_MBR), and MBI (CA_MBI). On-station, CA had late tasseling, silking, and physiological maturity compared to CP. CA_MBR and CA_MBI had 28 and 19% more maize grain yield and 29 and 17% more stover yield compared with CA_MMC, respectively. Bean straw yield and intercrop bean grain yield were 13 and 7% more, respectively, with CA compared with CP. However, in the on-farm trials, maize grain and stover yield were 23 and 47% less with CA_MBR compared to CA_MMC, possibly due to observed soil crusting and compaction of the sandy clay soil with CA. Soil water at 0-30 and 0100 cm depths were 38% and 28%, respectively, more with MBR compared to MMC at the maize grain filling stage. Stored soil water was 21% more with CA compared with CP. We conclude that CA_MBR and CA_MBI are suitable for fine texture soil of the CRV. Abbreviations: BMC, bean monoculture; CA, conservation agriculture but is also applied for monoculture without tillage and with crop residue retention; CP, current smallholder practice; CRV, Central Rift Valley of Ethiopia; MMC, maize monoculture; MBR, maize-bean rotation;

10 MBI, maize-bean intercropping; RP, rainfall productivity; YTC, YT, YC, and TC are for the 3and 2-way interactions of year (Y), tillage (T) and cropping system (C).

Keyword: Crop rotation systems, dryland soils, intercropping systems, maize, grain, other legumes, tillage, water conservation

2.1. Introduction Crop production in Ethiopia is dominated by subsistence, rainfed and unsustainable practices. The conventional land preparation for crop production is 3 to 8 tillage passes per cropping season with an oxen drawn ard plow traditionally known as a “Maresha”. Nyssen et al. (2011) explicitly explained the historical and cultural significance of this implement and tillage practice in Ethiopia. Ard-tillage with its low speed of operation is much less aggressive than tillage with a moldboard or disk plow, a disk, or even a relatively fast moving chisel harrow, and rather than much soil inversion, it pushes soil to the side with partial inversion. However, the repeated tillage results in much exposure of the surface soil to erosion. Erosion losses for Ethiopia have been estimated to be 1.5 billion tons of soil per year of which 45% of it is lost from arable land at the rate of 20-93 Mg ha-1 yr-1 (Bewket and Teferi, 2009; Gelagay and Minale, 2016; Taddese, 2011). This rate of loss is much higher than the world average of 17 Mg ha-1 year-1 and the Africa average of 23 Mg ha-1 year-1. Soil losses due to erosion from crop land costs the Ethiopia economy 1.5 million tons of grain production per year and the cost of conservation practices needed to save 77% of crop land which has >8% slope was estimated to be 2 billion USD (Hurni et al., 2015). However, CA practices have been proven effective for reducing soil losses through water and wind erosion and also has other anticipated ecosystem benefits (Derpsch, 2008).

11 Conservation agriculture (CA) is a set of principles for sustained high crop yields and environmental protection. It requires minimal soil disturbance, permanent soil cover with crop residue and crops plus crop rotation (http://www.fao.org/ag/ca/la.html). The benefits and challenges of CA in different countries have been widely reported (Bolliger et al., 2006; Derpsch, 2008; Hobbs, 2007; Reicosky and Saxton, 2007; Wall, 2007). Soil degradation, including soil organic matter loss and reduced soil aggregate stability, can be reduced with CA (Derpsch et al., 1986). Other potential benefits of CA include reduced soil water evaporation, increased water infiltration, reduced water runoff and soil erosion, reduced weed problems, and increased soil biological activity (Derpsch, 1986; Sayre, 1998). Soil organic matter maintenance can be favored by retention of crop residues (Sayre, 1998). Less fuel is required and labor for crop production can be reduced by50 percent for small-scale farmers with reduced or no tillage compared with conventional tillage (FAO 2012). More timely planting is enabled with CA (Haggblade and Tembo, 2003) allowing for a longer growing season and reduced risk of crop failure due to low rainfall (Friedrich, 2008; Erenstein, 2003). Yield stability can be improved in cases of poorly distributed rainfall in semi-arid regions (Friedrich, 2008; Erenstein, 2003). Results from eastern Africa and Colombia show that between 10 and 22% of rainwater may be lost to runoff from an uncovered, plowed soil surface (Thierfelder, 2003; Rockstrom et al., 2001). As a consequence of higher infiltration rates and reduced evaporation, general improvements in soil water status and water-holding capacity in CA systems have been observed (Bescansa et al., 2006; Derpsch et al., 1986). Other findings showed higher infiltration rates and soil water contents from no-till with surface mulch cover (Derpsch et al., 1986; Roth et al., 1988; Roth, 1992) and less soil crusting (Shaxson and Barber, 2003) with no-till

12 compared with tilled soil. Crop residue retention impedes soil crusting due to intense rainfall and reduces evaporation of water from the soil surface by protecting it from direct solar radiation and by greater resistance to air flow across the soil surface (Lal, 1977). Comparing plow tillage with CA, Roth et al. (1988) and Thierfelder (2003) found that higher infiltration rates and more available soil water with CA, especially during critical crop development stages, resulted in higher grain yield. Crop failure and poor harvest due to low and erratic rainfall at planting and at critical maize and bean growth stages are common in the semiarid Central Rift Valley of Ethiopia (CRV) (Liben et al., 2015a, b). Soil degradation caused by repeated conventional tillage and uncontrolled grazing of crop land after harvest is associated with increased occurrence of soil water deficits (Edao, 2015). Conservation agriculture may be a means to less or reversed soil degradation and increased soil water availability. However, mixed crop-livestock farming is prevalent throughout the semiarid CRV and crop residue is used as fodder, including through uncontrolled dry season grazing, and is a likely challenge to CA adoption. While CA has been much studied throughout the world, little is known about the potential of CA with maize-bean production in the semiarid CRV. The semiarid CRV situation differs from environments of other studies through the combination of generally low productivity, a long history of excessive removal of crop residues and livestock traffic during the dry season, tillage with the oxen-drawn ard in comparison with other tillage options, andosol with weak soil aggregate stability and prone to crusting, and with both maize-bean rotation and intercropping in the cropping system. We hypothesize that CA is a means to improve maize and bean productivity by improving soil water availability and rainfall productivity. Therefore, the

13 objective of this study was to compare CA with CP for different maize and bean cropping systems.

2.2. Materials and Methods 2.2.1. The Study Area The study sites are located between 8o24’ and 8o27’ N and 39o18’ and 39o27’ E at an average altitude of 1500 m above sea level in the CRV and is part of Ethiopia’s hot to warm semiarid mid-altitude zone with mixed crop-livestock farming (FAO, 1978). The CRV is a corridor of the 500-km long Ethiopian Rift which formed high elevation escarpments separated by a valley. Crop residue is important as livestock feed. The area can be described as transitional between semi-arid to sub-humid. Rainfall has a weak bi-modal pattern and includes 175–358 mm in March–April and 420–680 mm in June–September (Kassie et al., 2013) but the rainfall is erratic and periods of severe soil water deficits occur often (Tesfaye et al., 2015; Liben et al., 2015b; Kassie et al., 2013). Most farmers use the March to April rain for land preparation and plant maize and bean in June or July. Maize and bean are the second and third most important crops next to tef (Eragrostis tef (Zuccagni) Trotter), a gluten-free tiny grain cereal widely grown for human consumption in the form of ‘injera’ bread and for fodder. Bean is an important protein source, especially for farmers who cannot provide milk for their children, but also an important market crop. Though less efficient in fixing N than other pulses, bean was reported to fix up to 125 kg N ha-1 (Wortmann, 2006) and it is the legume most associated with maize cropping systems in the semiarid CRV.

14 2.2.3. Site Description and Crop Management Trials were conducted at Melkassa Agricultural Research Station in 2010 to 2014 and at Bofa in 2011 to 2014 during the main crop growing season. The soil at both sites was an Andosol. The soil at Melkassa, locally called “gonbore”, had weak wet aggregate stability and was prone to crusting. Bofa soil, locally called “shakite”, had pumice fragments that tend to rise to the surface and provide some mulching effect. The slope ranged from 2 to 3% at Melkassa and 4 to 5% at Bofa. Soil bulk density at Bofa and Melkassa was 1.5 and 1.2 g cm-3, respectively (Table 2). Organic C (OC) was near 10 g kg-1at both locations. Total soil N was 0.6 to 0.8 g kg-1 and C: N was near 14 at Bofa. Total N was 1.2 to 2.1 g kg-1 and C: N ranged from 5.4 to 6.0 at Melkassa. Soil pH was above 7 at each location. Olsen P was28 and 17 mg kg-1 at Bofa and Melkassa, respectively, for the 0 to 15 cm soil depth. Bulk density and Olsen P increased with soil depth except for the deepest soil horizon at Bofa. Soil OC and total N decreased with soil depth at Melkassa. The soil texture was sandy clay for all depths at Bofa and loam and silty loam for the 0-30 and 30-90 cm soil depths at Melkassa. Rainfall during the maize and bean growing period was irregularly distributed in 2010, 2012 and 2013 but better distributed in 2011 and 2014 (Fig. 1). Rainfall received in 2014 between planting to physiological maturity was relatively low. About 1/3 of the growing season average rainfall (200 mm) was received within a 10-day period in 2010, 2011 and 2013. Improved water infiltration is needed because of torrential rain events and water conservation is needed to conserve water for the grain fill period. Rainfall during the bean and maize growing periods ranged from 120 mm to 635 mm and 180 mm to 800 mm, respectively. It was only in 2012 that rainfall received during the maize growing period was above the reported average

15 main season rainfall for the semiarid CRV. Bean and maize planting time were affected and varied over a 20 day range with the onset of rainfall.

Figure 1. Cumulative rainfall from planting to harvest maturity during the five crop seasons at Melkassa Agricultural Research Center in Ethiopia; planting date varied depending on onset of rainfall.

At Melkassa, the experiment had eight treatments in a split plot design with three replications. The main plot treatments were CP with pre-plant ard tillage and CA with no tillage. Four cropping systems were the sub-plots treatments. With CP, the land was tilled three times at 10–20 cm depth using the oxen-drawn “maresha” ard-type of plow (Nyssen et al. 2010;

16 Melesse, 2007) after >90% of crop residue was grazed or removed with the remaining stubble incorporated. In CA, seeds were placed in holes made with a hoe in untilled land with 100% of crop residue retained from the previous harvest including maize or bean residue for monoculture and both for maize-bean residue for crop rotation and intercrop treatments. The cropping systems were maize monoculture (MMC), bean monoculture (BMC), maize-bean rotation with first planting of bean in 2010 (MBR), and intercropping 50% plant density of bean sown into 100% plant density of maize two weeks after maize planting (MBI). The treatment combinations were CA_MMC, CP_MMC, CA_BMC, CP_BMC, CA_MBR, CP_MBR, CA_MBI, and CP_MBI. Plot size was 8 x 12.5 m. The maize and bean varieties were the locally popular and early maturing openpollinated Melkassa-II (ZM521) and Nassir, respectively. Average growing degree days to emergence, anthesis and physiological maturity were 78 and 66, 447 and 585, and 1011 and 1305 for Nassir and Melkassa-II, respectively (Table 1). Maize was planted at 75 cm and 25 cm inter and intra row spacing, respectively, for 53,333 plant ha-1 for all cropping systems. Bean was planted at 40 cm and 10 cm inter-and intra-row spacing, respectively, for 250,000 plant ha1

, except for intercropping where one row of bean was planted between maize rows. Fertilizer

application included 100 kg ha-1 diammonium phosphate, of 18% N and 20% P, band applied at planting and 50 kg ha-1 urea of 46% N applied at the 5-leaf stage of maize for all cropping system treatments with maize. Only 100 kg ha-1diammonium phosphate was band applied at planting time to bean sole crop. Fertilizers were not applied to intercropped bean. Weed control was by pre-plant application of glyphosate ((2-(phosphonomethylamino) acetic acid), 41%

17 active ingredient) before crop emergence at 3 L ha-1 followed by regular hand weeding as necessary for CA plots. Weed control was manual for the CP plots.

Table 1. Seasonal variation of accumulated growing degree days (GDD) to emergence (DE), flowering (DF) and physiological maturity (DPM) of maize and bean grown under no-till with residue retention management at Melkassa Agricultural Research Center in the semiarid Central Rift Valley of Ethiopia. Maize GDD Year 2010 2011 2012 2013 2014 Mean

Bean GDD

DE 60 59 63 67 80 66

DF 583 505 568 545 723 585

DPM 1322 961 1342 1494 1420 1308

DE 81 76 79 78 78 78

DF 455 417 449 465 447 447

DPM 1026 801 1042 1094 1091 1011

On-farm field trials were conducted at Bofa on eight farmer fields from 2011-2014. The four on-farm trial treatments were MMC with three tillage passes and no residue retention (CP_MMC) compared with no-till and 35-45% crop residue retained for CA_MMC, CA_MBR, or CA_MBI. Each farmer field was considered as a replication with a randomized complete block design. Maize cv Melkassa-II and bean cv Awash-I were the varieties used. Plot size was 10 x 10 m. Tillage, crop residue retention, plant populations, fertilizer rates, weed management, and herbicide use were as for the Melkassa on-station trials.

2.3. Measurements Weather data were obtained from Melkassa Agricultural Research Station within 1 km of the research site. Cumulative rainfall from maize or bean planting to physiological maturity was determined for each crop year by adding together daily rainfall. Days for the duration of

18 between growth stages were determined for maize and bean from planting to physiological maturity. Soil profiles of experimental sites were characterized with samples from the 0-15, 15-30, 30-45, 45-60 and 60-90 cm soil depths. Three composite soil samples were analyzed for each soil profile. The laboratory analyses on ground and sieved samples included: particle size distribution by the hydrometer Bouyoucos method (Van Reeuwijk, 1992); pH in 1:2.5 soil to water ratio after the soil suspension had been equilibrated at 25C (Houba et al., 1989); determination of organic carbon by Walkley-Black wet digestion method (Houba et al., 1989); total Kjeldahl N (Houba et al., 1989); available P (Olsen et al., 1954) and exchangeable cations by ammonium acetate extraction (Van Reeuwijk,1992) (Table 2). Growing degree days and days to emergence, flowering, physiological maturity for maize and bean, and days to silking for maize were recorded when 50% of the population reached the phenology stage. Except for border rows, plants in all other rows were harvested and air-dried for grain and stover or straw yields. Grain yield was adjusted to 120 g kg-1 water content. Stover and straw were air-dried to constant weight and weighed. Only grain and stover or straw yields were reported for the on-farm trials. Grain and stover yields were separately measured for maize and dry bean for the maize-bean intercrop at both locations. Maize yields from all the cropping systems were compared statistically. Intercropped bean yields were compared for CA and CP, but not with sole crop bean yields. Soil water was measured at 0-10, 10-20, 20-30, 30-40, 40-60 and 60-100 cm depths at Melkassa with a meter-long access tube installed in each plot starting with the 2013 crop season. Capacitance probe (PR-2 probes, Delta-T Devices Ltd., UK) were used to measure

19 stored soil water bi-weekly starting from planting until harvest. Soil water was measured at physiological maturity in 2013 and 2014 and at seed set and grain fill in 2014.

Table 2. Soil properties of study sites at Bofa and Melkassa, Ethiopia. Depth (cm) 0-15

Soil properties Bofa, ‘Shakite’ soil Bulk density (g cm-3) 1.51 -1 Organic C (g kg ) 10.2 Total N (g kg-1) 0.60 C:N 17 pH 7.3 -1 Olsen P (mg kg ) 28 -1 Sand (g kg ) 470 -1 Silt (g kg ) 230 -1 Clay (g kg ) 280 Soil texture* SC Melkassa, ‘Gonbore’ soil Bulk density (g cm-3) 1.19 -1 Organic C (g kg ) 10.6 -1 Total N (g kg ) 1.2 C:N 6.0 pH 7.3 -1 Olsen P (mg kg ) 17 -1 K (mg kg ) 1564 -1 Ca (mg kg ) 3400 -1 Mg (mg kg ) 436 Sand (g kg-1) 360 -1 Silt (g kg ) 450 -1 Clay (g kg ) 200 Soil texture L *SC, sandy clay; L, loam; SL, silty loam

15-30

30-45

45-60

60-90

1.52 9.7 0.69 14 7.4 27 460 240 290 SC

1.56 9.6 0.73 13 7.1 25 460 240 290 SC

1.58 8.1 0.80 10 7.3 14 460 230 300 SC

1.47 7.3 0.82 9 7.4 17 520 200 270 SC

1.23 10.4 1.3 4.9 7.5 12 978 3600 436 350 470 190 L

1.24 10.4 1.6 5.1 7.6 10 978 3800 484 330 490 190 SL

1.25 10.4 2.0 5.3 7.8 7 1017 4000 557 310 510 180 SL

1.24 10.2 2.1 5.4 7.9 6 1056 4200 520 300 510 180 SL

Rainfall productivity (RP, kg mm-1) was determined for each cropping system in terms of maize grain or stover yield for the on-station trial. The RP was calculated as the ratio of maize grain or stover yield (kg) to rainfall amount (mm) from planting to physiological maturity

20 (Zerihun et. al, 2014). Grain and straw yields of intercropped bean were converted based on the current price to equivalent maize grain and stover yields, respectively, and added to intercrop maize grain yield to determine RP of the maize-bean intercropping system. Farmers hosting on-farm trials at Bofa were interviewed with open-ended questionnaires for the possible causes of poor maize performance with CA during the four growing seasons.

2.4. Statistical Analysis For the on-station trial, plot data were grouped for bean as the MBR crop in 2010, 2012, and 2014 and maize as the MBR crop in 2011 and 2013. Similarly, for the on-farm trials, the data were grouped into 2011 and 2013 and into 2012 and 2014 with maize and bean being the respective MBR crop. Maize yields and phenology and bean phenology were statistically compared for all the cropping systems. Bean grain and straw yields were compared for the rotation and monoculture but intercrop bean yield data, which were available for all years, were analyzed for the year x tillage interaction. Combined analyses across years were carried out using Statistix10.0 (Analytical Software, Tallahassee FL) for grain yield, stover or straw yield, RP and days to emergence, flowering, silking, and physiological maturity. Combined analyses were conducted for soil water data taken at physiological maturity for 2013 and 2014. Soil water data at seed set and grain filling for 2014 were also analyzed. Data were tested for normality before doing the analysis of variance (ANOVA) following the General Linear Model (GLM) procedure at 0.05 probability level. When significant treatment effects occurred, means were compared using LSD 0.05.

21 2.5. Results 2.5.1. Maize and Bean Phenology at Melkassa Bean phenology was not affected by treatments and interactions. Maize phenology varied more with year compared with tillage and cropping system due to slow development in 2012 compared with other years. The year x tillage x cropping system interaction (YTC) was significant when the MBR crop was bean for days accumulated for all maize development stages except for time to emergence (DE) (Table 3). The YTC was due to treatment effects in 2014 but with no effects in 2010 and 2012. This resulted in significant YT and YC effects for days to all stages of development except for DE. Days to tasseling, silking, and maturity in 2014 were 15, 13, and 21% more, respectively, with CA compared with CP and more so with CA_MBI. Time from tasseling to silking averaged 5.5 days with little variation except for a 4day duration with CA_MMC in 2014. For 2011 and 2013 when the MBR crop was maize, YT was significant due to later maturity with CA compared with CP in 2013 but no effect in 2011 (Table 4). Days to silk were affected by TC interactions due to cropping system effects with CA but not with CP.

2.5.2. Maize Grain and Stover Yield Maize grain and stover yield were affected by YTC at Melkassa due to treatment effects in 2014 but with no effects in 2010 and 2012 (Table 3). This also resulted in significant YT and TC for grain yield. Maize grain and stover yields averaged across cropping systems were 26 and 19% more with CA compared with CP in 2014, respectively, but 28 and 29% more with CA compared with CP for MBI (Table 3). Tillage and cropping system did not affect grain and stover yield in 2010 and 2012. Only maize stover yield at Melkassa was affected by YTC when the MBR crop was maize due to a high MBR yield with CA in 2013 compared with other yields

22 (Fig. 2). Maize grain yield was affected by YT and TC (Table 4). Grain yield was 17.5% more with CA compared with CP in 2013 but there was no tillage effect in 2011. Maize grain yield was 26 and 20% more for MBI and MBR, respectively, with CA compared with CP, but tillage did not affect MMC yield.

Table 3. The three-way interaction effect of year (Y) x tillage (T) x cropping system (C) on maize grain and stover yield and days to emergence (DE), tasseling (DT), silking (DS) and physiological maturity (DPM) at Melkassa Agricultural Research Center in Ethiopia. Grain Stover DE DT DS DPM -1 Days Mg ha 7.0 2010 CA_MBI 3.62d‡ 5.92e 49d 55cd 95c 7.0 2010 CA_MMC 4.31cd 5.71e 49d 55cd 95c 7.3 2010 CP_MBI 4.21cd 5.02e 49d 55cd 95c 7.2 2010 CP_MMC 4.60cd 5.20e 49d 55cd 95c 2012 CA_MBI 4.53cd 8.91ab 7.0 74a 80a 120a 2012 CA_MMC 3.82d 8.91ab 7.0 73a 79a 119a 2012 CP_MBI 3.92d 8.82ab 7.3 73a 76ab 119a 2012 CP_MMC 3.61d 8.64ab 6.3 73a 79a 119a 6.5 2014 CA_MBI† 6.54a 9.03a 59b 64b 113b 6.5 2014 CA_MMC 6.03ab 8.10bc 52c 56c 99c 8.0 2014 CP_MBI 4.70cd 7.00d 48d 53d 87d 2014 CP_MMC 5.21bc 7.43cd 8.0 48d 53d 87d †CA, conservation agriculture; CP, conventional practice; MMC, maize monoculture; MBI, maize-bean intercropping. ‡Means followed by the same letter are not significantly different at 0.05 probability level, LSDtest. YxTxC

23 Table 4.The year (Y) x tillage (T) and T x cropping system (C) interaction effects on maize grain yield and days to tasseling (DT) and silking (DS) at Melkassa Agricultural Research Center in Ethiopia. YxT

Grain DT DS -1 Mg ha Days 2011 CA† 4.40b‡ 60c 65c 2011 CP 4.30b 60c 65c 2013 CA 5.31a 68a 72a 2013 CP 4.03b 66b 70b TxC Grain DS CA_MBI 4.82b 70ab CA_MBR 5.43a 71a CA_MMC 4.40bcd 69bc CP_MBI 3.84d 68c CP_MBR 4.51bc 68c CP_MMC 3.92cd 68c †CA, conservation agriculture; CP, conventional practice; MBR, maize-bean rotation; MBI, maize-bean intercropping; MMC, maize monoculture; MBI, maize-bean intercropping. ‡Means followed by the same letter are not significantly different at 0.05 probability level, LSD-test.

At Bofa, maize grain and stover yield were more with CP_MMC compared with other treatments (Fig. 3a and c). Maize grain yield was 23% more and stover yield was 46% more with CP_MMC compared to CA_MMC. A significant YT effect on stover yield was due to a relatively greater tillage effect in 2013 compared with 2011 although the trend was directionally the same in both years (Fig. 3b). Mean maize grain and stover yield were similar for MBR, MBI, and MMC with CA.

24

Figure 2. The year x tillage x cropping system interaction effect on maize stover yield (Mg ha-1) at Melkassa Agricultural Research Center in Ethiopia. CA, conservation agriculture; CP, conventional practice; MBR, maize-bean rotation; MBI, maize-bean intercropping; MMC, maize monoculture; MBI, maize-bean intercropping. 2.5.3. Bean Grain and Straw Yield Bean intercrop grain yield at Melkassa was 44% more with CA compared with CP in 2013 but was not affected by tillage in other years (Table 5). Intercrop straw yield was on average 45% more with CA compared with CP in 2013 and 2014 but was not affected by tillage in other years. In years when the MBR crop was bean, bean grain and straw yield were 11 and 19% more with MBR compared with BMC but were not affected by tillage or interactions. Straw yield for BMC was 46% more in 2013 with CA compared with CP but was not affected by tillage in 2011. The bean crops at Bofa failed due to water logging under CA system and no yield data was collected.

25

Figure 3. Maize grain and stover yield as affected by cropping system (a) in 2011 and 2013 and (c) in 2012 and 2014 combined, and (b) by treatment x year interaction for maize stover yield at Bofa area in the semiarid Central Rift Valley of Ethiopia. CP_MMC, maize monoculture under conventional practice (CP); CA_MMC, maize monoculture under conservation agriculture (CA); CA_MBI, maize-bean intercropping under CA; CA_MBR, maize-bean rotation CA.

2.5.4. Rainfall Productivity Rainfall productivity (RP) was affected by the main effects of tillage and cropping system when the rotation crop was maize (Fig. 4). The tillage x year interaction and the main effect of cropping system affected RP when the rotation crop was bean (Fig.4a&d). The RP was

26 18% and 20% greater with MBI compared with MMC for maize grain and stover yield, respectively, when the MBR crop was bean (Fig. 4a). When the MBR crop was maize, RP for maize grain yield with MBI was 18% more compared with MMC and RP was intermediate for MBR (Fig. 4b). The maize grain RP was 8% higher with CA compared with CP (Fig.4c). On average, RP was 45% higher in 2014 compared to the other crop years when the rotation crop was bean and YT was significant due to the greater year difference in 2014 with CA compared with CP (Table 5; Fig.4d).

Table 5. Bean yield at Melkassa Agricultural Research Center in Ethiopia as affected by the: year (Y) x tillage (T) interaction with intercropping for 2010-2014; cropping system (C) effect on sole crop grain yield for 2010, 2012 and 2014; and Y x T for sole crop straw yield for 2011 and 2013. The C treatments included bean monoculture (BMC) and rotation (MBR) in 2010, 2012 and 2014; BMC in 2011 and 2013; and maize-bean intercrop in all years of 2010-2014. Bean intercrop yields Bean sole crop yield in 2010, 2012, 2014 YxT Grain Straw Cropping system Grain Straw -1 -1 Mg ha Mg ha 2010 CA† 0.52d‡ 2.30a MBR 2.7a 6.3a 2010 CP 0.68d 2.4ab BMC 2.4b 5.1b 2011 CA 0.51d 0.58d Bean sole crop yield in 2011 and 2013 2011 CP 0.50d 0.67d YxT Straw 2012 CA 0.67d 1.30c 2011 CA 4.6ab 2012 CP 0.67d 1.42c 2011 CP 5.1ab 2013 CA 1.32a 2.41ab 2013 CA 5.7a 2013 CP 0.94c 1.64c 2013 CP 3.9b 2014 CA 1.20ab 2.71a 2014 CP 1.12bc 1.94bc †CA, conservation agriculture; CP, conventional practice; MBR, Maize-bean rotation; BMC, bean monoculture; MBI, maize-bean intercropping. ‡Means followed by the same letter are not significantly different at 0.05 probability level, LSD-test.

27

Figure 4. Rainfall productivity (RUE) as affected by (a) cropping system for maize grain and stover yield when the MBR crop was bean, (b) cropping system for maize grain yield when the MBR crop was maize, (c) tillage for maize grain yield when the MBR crop was maize, and (d) tillage x year interaction for maize grain yield when the MBR crop was bean at Melkassa Agricultural Research Center in the semiarid Central Rift Valley of Ethiopia. CP, conventional practice; CA, conservation agriculture; MMC, maize monoculture; MBI, maize-bean intercropping; MBR, maize-bean rotation.

2.5.5. Stored Soil Water Stored soil water at different maize growth stages varied with the main effects of tillage and cropping system (Fig. 5). Stored soil water for the 0 to 30 and 0 to 100 cm depths at seed

28 set and grain fill was more for BMC and MBR compared with MBI and MMC (Fig. 5 a, b). The stored soil water for MBR was 72 mm for the 0-30 cm depth compared with 48 and 53 mm for MBI and MMC, respectively (Fig.5a). Stored soil water at seed set for the 0 to 100 cm depth was 140, 130, and 110 mm for BMC, MBR and MMC, respectively (Fig. 5b). Compared to MMC, stored soil water at grain fill with MBR was 38 and 28% more for the 0 to 30 and 0 to 100 cm depths, respectively. Stored soil water for the 0 to 30 cm depth was more with BMC and MMC compared with MBR and MBI. Stored soil water at the 0 to 100 cm depth at physiological maturity was 21% more with CA compared with CP (Fig. 5c).

Figure 5. The main effects of cropping system on soil water (a) for soil depth of 0-30 cm during maize seed set (SS) and grain fill (GF) and at physiological maturity (PM), and (b) for soil depth of 0-100 cm during SS and at PM, and (c) the main effect of tillage on soil water at soil depth of 0-100 cm at PM at Melkassa Agricultural Research Center in the Central Rift Valley of Ethiopia. Different letters within sampling times indicate significant effects on stored soil water.

29 2.6. Discussion In contrast with delayed emergence with CA reported previously (Erenstein, 2003; Haggblade and Tembo, 2003; Friedrich, 2008), maize emergence was earlier with CA compared with CP (Table 3). In the calcareous soils at Melkassa, crop emergence is often delayed by surface soil crusting when rainfall does not wetten the soil prior to emergence (Liben et al., 2015a). The soil at Melkassa has been observed to have weak wet aggregate stability and to be prone to crusting and slow water infiltration and is often managed with tie-ridging to reduce runoff. The maize and sorghum yield increases were 43 and 17% with tie-ridging compared with CP (Mesfin et al., 2014). The soil crusting effect was likely less with CA compared with CP due to crop residue cover of the soil and improved surface soil water conservation (Liben et al., 2015a; Merga et al., 2014). Late tasseling, silking, and physiological maturity with CA compared with CP agrees with other findings (Naudin et al., 2010) and was possibly due to more soil water availability with CA (Table 3; Fig.5). The increased soil water availability with CA compared with CP may have been due to increased water infiltration and reduced evaporation with CA (Rockstrom et al., 2001; McHugh et al., 2007) while more frequent occurrence of soil water deficits with CP may have hastened phenological development (Naudin et al., 2010). Bean development time, however, was similar for CA and CP. Early ( 0.35) with observed data (Table 5). However, relatively greater error was associated with GYGA [ME = 27 mm; intercept (b) = 26 mm] and NASA (ME = -9 mm; b = 25 mm), and these two datasets performed the best at estimating monthly rainfall total (Table 5; Fig. 3). WeatherMan was the best at estimating annual total rainfall (Table 5).

94

Table 5. Statistical indicators of daily, decadal, monthly and annual time periods for rainfall (RF) (mm), maximum (Tmax) and minimum (Tmin) temperatures (oC) combined over the six stations for this study. r* ME Bias RMSE EF r ME Bias RMSE EF Dataset Daily rainfall Decadal rainfall WMan 0.12 0.02 1.01 9.23 -0.75 0.50 -0.21 0.99 34.34 -0.05 NASA 0.34 -0.29 0.91 7.32 -0.10 0.71 2.94 1.10 26.76 0.36 MS 0.09 0.22 1.08 10.39 -1.21 0.41 -2.19 0.93 41.48 -0.03 GYGA 0.33 -0.87 0.77 8.23 -0.39 0.74 8.84 1.30 28.45 0.46 Daily Tmax Decadal Tmax WMan 0.74 -0.01 1.00 2.33 0.50 0.88 0.01 1.00 1.46 0.75 NASA 0.28 2.76 1.11 4.92 -1.22 0.29 -2.76 0.90 4.63 -1.09 MS 0.55 0.08 1.00 3.79 -0.32 0.74 -0.09 1.00 2.38 0.54 GYGA 0.80 -0.09 1.00 1.99 0.71 0.88 0.08 1.00 1.48 0.64 Daily Tmin Decadal Tmin WMan 0.68 0.01 1.00 3.10 0.37 0.83 -0.01 1.00 2.02 0.65 NASA 0.18 -2.28 0.85 4.88 -0.56 0.18 2.29 1.18 4.49 -2.60 MS 0.56 0.11 1.01 4.12 -0.11 0.75 -0.11 0.99 2.63 0.55 GYGA 0.63 -0.34 0.97 3.06 0.38 0.71 0.35 1.03 2.51 0.02 Monthly rainfall Annual rainfall WMan 0.73 0.63 1.01 62.27 0.46 0.67 7.07 1.01 193 0.34 NASA 0.85 -8.83 0.91 46.61 0.69 0.77 -147 0.87 219 0.14 MS 0.61 6.56 1.08 82.21 0.05 0.47 55.8 1.06 288 -0.48 GYGA 0.87 -26.53 0.77 55.07 0.57 0.80 -310 0.76 358 -1.28 Monthly Tmax Annual Tmax WMan 0.94 -0.01 1.00 1.01 0.88 0.98 -0.01 1.00 0.45 0.96 NASA 0.27 2.75 1.11 4.55 -1.41 -0.39 2.45 1.10 4.33 -2.65 MS 0.85 0.09 1.00 1.67 0.67 0.97 0.10 1.00 0.54 0.94 GYGA 0.90 -0.09 1.00 1.29 0.80 0.98 -0.08 1.00 0.47 0.96 Monthly Tmin Annual Tmin WMan 0.91 0.01 1.00 1.41 0.82 0.97 0.00 1.00 0.56 0.95 NASA 0.16 -2.29 0.85 4.37 -0.71 -0.13 -2.05 0.87 3.71 -1.33 MS 0.86 0.11 1.01 1.81 0.71 0.96 0.10 1.01 0.68 0.92 GYGA 0.73 -0.35 0.97 2.31 0.52 0.94 -0.34 0.98 0.92 0.86 *WMan, WeatherMan; MS, MarkSim r, Pearson correlation coefficient; EF, Nash–Sutcliffe Efficiency coefficient; ME, mean error; RMSE, root mean square error.

95

Figure. 3. The 1:1 line and the degree of linear association between generated and measured total monthly rainfall over the six Global Yield Gap Atlas Technology Extrapolation Domains at meteorological stations in Ethiopia.

96 NASA underestimated daily Tmax by 2.76oC and overestimated daily Tmin by 2.28oC (Table 5). Except for NASA, the generated datasets had bias = 1.00 for Tmax (Table 5). GYGA resulted in the lowest RMSE for both daily Tmax and Tmin. Further, time series observed daily Tmax and Tmin were relatively well-estimated with GYGA (EF = 0.71 for Tmax; EF = 0.38 for Tmin). The RMSE from all the evaluated datasets were < 5oC for both daily Tmax and Tmin, with the lowest RMSE from GYGA dataset (Table 5). Considering all indices determined, WeatherMan, followed by GYGA, performed well in simulating decadal, monthly and annual values of Tmax (Table 5; Fig. 4). The Tmin was best simulated by WeatherMan and MarkSim for all time periods (Table 5; Fig. 4), whereas NASA performed the worst in simulating Tmax and Tmin for the decadal, monthly and annual time periods (Table 5; Fig.4).

97

Figure. 4. The 1:1 line and the degree of linear association between generated and measured mean monthly maximum (Tmax) and minimum (Tmin) temperatures over the six Global Yield Gap Atlas Technology Extrapolation Domains at meteorological stations in Ethiopia.

98 4.7.3. Combined Weather Dataset The evaluation results for generated weather datasets generally showed good agreement between observed and GYGA rainfall and WeatherMan Tmax and Tmin for the daily and extended time periods. In addition, simulated grain yield with GYGA weather data showed the best agreement with simulations using observed weather data in this study. Hence, daily solar radiation and rainfall from GYGA dataset, and Tmax and Tmin from WeatherMan dataset were combined to develop a new weather dataset for use in running crop simulation models for sites that lack observed weather data in the complex topography of Ethiopia. This weather dataset is hereafter called “COMBINED” dataset. The COMBINED was evaluated by directly using in crop models to assess its performance and to compare with the other weather datasets.

4.7.4. Evaluation of Generated Weather Datasets Using Simulation Modeling Overall performance of the five weather datasets when directly used to run the three crop growth simulation models is illustrated in Table 6. There were no significant differences between grain yield simulated with observed weather data compared to simulations with either WeatherMan, GYGA or the combined weather datasets. The strongest correlation coefficients, lowest relative mean errors and normalized root mean square errors, and bias, model efficiency coefficients and index of agreements closer to unity indicated suitability of the GYGA, WeatherMan and the combined weather datasets to run CERES-Maize, CROPGRO-Dry bean and CROPGRO-Soybean models at locations that lack observed weather data in Ethiopia.

99 Table 6. Statistical indicators of simulated grain yield (Mg ha-1) with long-term measured and generated weather datasets (1999-2008) combined for maize cvBH546 and Melkassa-II, soybean cv Dhidhesa and dry bean cv Nassir at Bako and Melkassa locations in Ethiopia. WD* P(t) r ME rME Bias RMSE nRMSE EF d* WMan 0.91 0.96 -0.01 -0.23 1.00 0.60 13.23 0.90 0.99 NASA 0.03 0.87 0.34 7.59 0.92 1.04 23.22 0.71 0.99 MarkSim 0.00 0.65 1.45 32.25 0.66 2.24 49.78 -0.35 1.00 GYGA 0.11 0.97 -0.11 -2.53 1.03 0.46 10.12 0.94 0.99 Combined 0.72 0.98 0.02 0.51 0.99 0.41 9.11 0.95 0.99 *WD, Weather dataset; WMan, WeatherMan; d, index of agreement; EF, Nash–Sutcliffe Efficiency coefficient; ME, mean error; RMSE, root mean square error; nRMSE, normalized root mean square error; P (t), paired t test; r, Pearson correlation coefficient; rME, relative mean error.

Simulated grain yield using MarkSim followed by NASA datasets showed high interannual variability (Fig. 5). The median simulated maize grain yields at Bako and Melkassa ranged from 6.8 - 8.2 and 3.3 - 4.7 Mg ha-1, respectively (Fig. 5a and c). MarkSim resulted in the lowest median maize grain yield, whereas greatest median yield were simulated by NASA at Bako and by WeatherMan at Melkassa. The COMBINED compared with observed dataset resulted in the best agreement of median simulated maize grain yields and with the lowest interannual variability. Simulation with WeatherMan, followed by GYGA and COMBINED datasets, gave soybean and dry bean grain yields closest to median simulated grain yield with observed weather data (Fig. 5b and d). Whereas simulated soybean grain yield with the WeatherMan dataset showed high inter-annual variability, simulation with GYGA and the COMBINED datasets indicated low variability.

100

Figure. 5. The box plots of simulated grain yields using measured and generated weather datasets for the year 1999-2008. Lower and upper boundaries for each box are the 25th and 75th percentiles. The line inside each box indicates the median. Whiskers (error bars) above and below the box indicate the 90th and 10th percentiles, and the dot symbol (outliers) represent the minimum and maximum values. Simulations were with maize BH546 and soybean Dhidhesa at Bako and with maize Melkassa-II and dry bean Nassir at Melkassa in Ethiopia.

For Melkassa-II cultivar, 100, 90, and 70% of simulated maize grain yield, respectively, with COMBINED, WeatherMan and GYGA datasets, were within ±10% deviation of mean simulated maize grain yield with observed weather data (Fig. 6a). Simulated grain yields of BH546 fell within 100% of observed grain yield for GYGA and WeatherMan, 90% of observed grain yield for the COMBINED, and 55% of observed grain yield for the NASA and MarkSim datasets (Fig. 6b). Less than 45% of simulated soybean grain yield using all generated datasets fell within ±10% of simulation with observed weather data, and the best and the worst performances were with COMBINED and NASA datasets, respectively(Fig. 6c). Except for

101 MarkSim, simulated dry bean yields were within ±20% of mean yield simulated using observed weather data (Fig. 6d).

Figure. 6. Inter-annual distribution of deviation of grain yield simulated with generated weather datasets from long-term average simulated grain yield with observed weather data for maize (BH546 and Melkassa-II), soybean (Dhidhesa) and dry bean (Nassir) cultivars. The simulations were conducted at Melkassa for Melkassa-II and Nassir, and at Bako for BH546 and Dhidhesa. 4.8. Discussion 4.8.1. Crop Model Performance The CERES-Maize, CROPGRO-Dry bean, and CROPGRO-Soybean adequately simulated measured values for the variables tested under the maize-dry bean or maize-soybean rotation and monoculture with conservation or conventional practices and with different N rate studies. For examples, grain and biomass yields and phenology were simulated with mean of normalized deviation closer to zero and with nRMSE < 15% using the three models. CERESMaize simulated measured data with more precision for the low (Melkassa) compared with the

102 high potential sites (Bako). CERES-Maize slightly underestimated LAI, especially with high N rates, though the predictions were acceptable. In addition, though acceptable, under-predictions of maize and dry bean grain yields with the rotation system under conservation agriculture was due to less sensitivity of the CERES-Maize and CROPGRO-Dry bean models to greater N immobilization associated with high residue retention in the fields as also reported in similar studies (Corbeels et al., 2014; Verhulst et al., 2011). In general, the results of model evaluation indicated CERES-Maize, CROPGROSoybean and CROPGRO-Dry bean models were able to simulate yields and the other variables considered in this study for the different N rates and components of conservation agriculture (crop rotation, tillage, and residue retention). This was in agreement with reported performance of CERES-Maize in simulating N rates (Thornton et al., 1995b; Matthews et al., 2002) and conservation agriculture practice (Ollenburger and Snapp, 2015) in other countries. Therefore, the models can be used to simulate conservation agriculture and N management practices in Ethiopia. 4.8.2. Suitability of Generated Weather Datasets 4.8.2.1. Evaluation Using Observed Weather Dataset The CERES-Maize, CROPGRO-Soybean and CROPGRO-Dry bean models operate on a daily-time step and evaluation of generated climatology variables at daily time scale is more important than the cumulative for longer time periods. The low correlation, poor agreement and bias far from unity for all the generated daily rainfall datasets compared with observed data could be due to complex topography of Ethiopia and problems with climatology variables assimilation model used to derive the daily rainfall data (White et al., 2008b). But overall, NASA, followed by GYGA dataset, showed relatively good performance in estimating

103 observed daily rainfall (Table 5). Nonetheless, NASA resulted in poor estimates of observed temperature variables. White et al. (2008a) also found biases between observed and NASA temperatures and speculated that these can be attributed to variation in elevation, landscape position, presence of large bodies of water, or problems with assimilation model used to derive the NASA temperature data. Variation in the sign and magnitude of the bias in NASA temperature data is highly unpredictable across the stations considered in this study in the complex topography of Ethiopia (See Appendix Table A1 and A2). Similarly, Van Wart (2015) found consistently weaker relationship between NASA and observed temperatures for locations in sub-Saharan Africa, and the weakest correlations occurred at sites with complex topography in Ethiopia and Kenya. The GYGA generated Tmax and Tmin data had the best agreement with observed temperature variables considering most of the performance indicators. The GYGA performance was better for Tmax compared with Tmin, and better for both temperature variables compared with daily rainfall. No dataset was superior to others on predicting observed data for all the weather variables together, but for some of the variables. Though GYGA daily rainfall and temperature variables and NASA daily rainfall were relatively good in performance compared with the other generated datasets, there is still a need to correct for the remaining bias of GYGA and NASA datasets to improve their performance in the complex topography of Ethiopia (Table 5). These could necessitate a more robust procedure to correct for the remaining error of GYGA propagated rainfall and temperature variables, especially for the minimum temperature. However NASA or GYGA daily rainfall, and either GYGA or WeatherMan daily Tmax and Tmin can be used to run crop simulation models in Ethiopia. Therefore, the COMBINED

104 dataset was developed from GYGA daily rainfall and WeatherMan daily Tmax and Tmin to use in crop simulation modeling in Ethiopia. 4.8.2.2. Evaluation Using Simulation Modeling Despite the poor agreement of generated with observed daily rainfall, evaluation with simulated maize, dry bean and soybean grain yields showed good performance of COMBINED, GYGA and WeatherMan datasets (Fig. 5; Fig. 6; Table 6) as found by Van Wart et al. (2013b) for yield gap analysis and Thornton et al. (1997) who used satellite and ground-based data for pearl millet growth simulation for famine early warning in Burkina Faso. Simulated grain yields of maize, dry bean and soybean using NASA and MarkSim datasets resulted in high interannual variability and poor agreement with simulation using observed weather data, though NASA daily rainfall showed relatively good agreement with observed data. Poor performance of NASA and MarkSim when used in simulation models is in agreement with results reported by Van Wart (2015) for sub-Saharan Africa. The COMBINED dataset followed by GYGA and WeatherMan showed low inter-annual variability (Fig. 5). Seventy percent (70%) of simulated grain yields with these datasets fall within ±10% of deviation from mean simulated grain yield with observed weather data and the median simulated grain yields were closer to simulation with observed weather data for the three crops. Over all, using COMBINED and GYGA, followed by WeatherMan, weather data had the best agreement with use of observed weather data for simulated grain yield. 4.9. Conclusion Results of calibration for genetic coefficients confirmed the importance of calibrating the CERES-Maize, CROPGRO-Dry bean and CROPGRO-Soybean models for their application in strategic decision making for the complex topography of Ethiopia. Evaluations of the models

105 under different cropping conditions suggest their suitability for simulating N rates, maize-bean rotation, and conservation and conventional agriculture in Ethiopia. When only considering values of statistical indicators for evaluation of generated weather datasets with observed data, NASA daily rainfall, and daily Tmax and Tmin either generated using WeatherMan weather generator or GYGA propagated weather data are more reliable to run crop growth models in Ethiopia. But when the weather datasets were evaluated by directly using them in crop simulation models, grain yields closer to mean simulated yield and with low inter-annual variability were attained with the COMBINED, GYGA and WeatherMan datasets. Therefore, it is concluded that models calibrations and evaluations were satisfactory within the limits of test conditions, and that the models fitted with cultivar specific parameters can be used in simulation studies to optimize fertilizer N use and predict performance of conservation agriculture practice, and either COMBINED, GYGA, or WeatherMan datasets could be used to run the crop models at sites that lack observed weather data in Ethiopia. It should be noted that generated weather datasets were evaluated only using data measured at one station due to limited number of weather stations with quality data in each target TED. For robust evaluation of generated weather datasets, it is recommended that evaluation should be at more stations for each TED in countries with sufficient weather stations and with quality data. Satellite derived data are available and can be evaluated for Ethiopia to use in crop models. The satellite data can be evaluated by directly comparing with observed weather data and also by using in crop models.

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114 Appendices Table A1. Daily statistical indicators of rainfall for the four different weather datasets at the stations located in the six GYGA Technology Extrapolation Domains of Ethiopia. r* ME Bias RMSE EF r ME Bias RMSE EF Station WeatherMan NASA Ambo 0.15 0.05 1.02 7.63 -0.73 0.40 -0.95 0.74 6.43 -0.23 Arb. 0.03 0.05 1.02 9.38 -0.87 0.32 -0.58 0.81 7.27 -0.12 Bako 0.17 0.02 1.01 10.79 -0.72 0.30 -0.12 0.97 8.70 -0.12 Har. 0.07 -0.01 0.99 8.59 -0.81 0.34 0.02 1.01 6.62 -0.07 Jimma 0.11 0.03 1.01 10.45 -0.76 0.40 0.27 1.07 7.61 0.07 Mel. 0.10 -0.02 0.99 8.11 -0.74 0.26 -0.37 0.85 7.03 -0.31 MarkSim GYGA Ambo 0.11 0.01 1.00 9.35 -1.61 0.40 -0.93 0.74 7.18 -0.54 Arb. 0.08 0.30 1.14 9.10 -0.76 0.27 -0.88 0.74 7.70 -0.26 Bako 0.14 0.28 1.08 11.09 -0.82 0.30 -0.97 0.79 9.80 -0.42 Har. 0.05 0.10 1.05 9.14 -1.05 0.32 -1.12 0.66 7.67 -0.44 Jimma 0.07 0.53 1.15 12.40 -1.47 0.37 -0.99 0.81 9.39 -0.42 Mel. 0.06 0.07 1.04 10.80 -2.09 0.28 -0.35 0.86 7.25 -0.39 *r, Pearson correlation coefficient; EF, Nash–Sutcliffe Efficiency coefficient; ME, mean error; RMSE, root mean square error; Arb, Arbaminch; Hay, Haramaya; Mel, Melkassa.

115 Table A2. Daily statistical indicators of maximum temperature (Tmax) and minimum temperature (Tmin) for the four different weather datasets at the stations located in the six GYGA Technology Extrapolation Domains of Ethiopia. r* ME Bias RMSE EF r ME Bias RMSE EF Tmax Station WeatherMan NASA Ambo 0.66 0.06 1.00 2.25 0.32 0.71 2.87 1.12 3.64 -0.79 Arb. 0.54 -0.09 1.00 2.30 0.15 0.74 4.95 1.19 5.42 -3.73 Bako 0.66 0.06 1.00 2.77 0.37 0.67 4.58 1.19 5.31 -1.32 Har. 0.28 -0.07 1.00 2.34 -0.36 0.53 -4.44 0.84 5.14 -5.54 Jimma 0.56 0.01 1.00 2.32 0.15 0.65 4.30 1.18 4.92 -2.79 Mel. 0.55 -0.01 1.00 1.92 0.12 0.45 4.29 1.18 4.89 -4.67 MarkSim GYGA Ambo 0.43 0.00 1.00 3.96 -1.12 0.71 -0.19 0.99 1.93 0.50 Arb. 0.35 0.09 1.00 3.58 -1.07 0.74 0.08 1.00 1.70 0.54 Bako 0.55 0.07 1.00 3.85 -0.22 0.67 -0.04 1.00 2.61 0.44 Har. 0.10 0.05 1.00 3.96 -2.87 0.53 -0.07 1.00 1.71 0.28 Jimma 0.39 0.02 1.00 3.83 -1.30 0.65 -0.15 0.99 1.94 0.41 Mel. 0.24 0.28 1.01 3.57 -2.02 0.45 -0.17 0.99 1.93 0.11 Tmin WeatherMan NASA Ambo 0.17 0.10 1.01 2.63 -0.54 0.41 -2.17 0.84 3.28 -1.40 Arb. 0.24 -0.13 0.99 2.86 -0.46 0.20 0.61 1.04 2.73 -0.33 Bako 0.44 0.07 1.00 1.99 0.01 0.25 -0.17 0.99 2.74 -0.87 Har. 0.62 -0.03 1.00 4.18 0.26 0.71 -7.77 0.56 8.51 -2.07 Jimma 0.49 0.04 1.00 3.52 0.00 0.16 -3.43 0.77 5.03 -1.04 Mel. 0.58 0.00 1.00 2.95 0.23 -0.12 -0.77 0.95 4.42 -0.72 MarkSim GYGA Ambo 0.11 0.01 1.00 4.04 -2.65 0.41 -0.09 0.99 1.93 0.17 Arb. 0.15 0.14 1.01 3.55 -1.25 0.20 -0.29 0.98 2.33 0.03 Bako 0.28 0.08 1.01 3.61 -2.24 0.25 0.42 1.03 1.99 0.01 Har. 0.61 0.06 1.01 4.69 0.07 0.71 -1.02 0.91 3.57 0.46 Jimma 0.36 0.16 1.01 4.53 -0.65 0.16 -0.39 0.97 3.51 0.01 Mel. 0.45 0.20 1.02 4.16 -0.53 -0.12 -0.70 0.95 4.25 -0.59 *r, Pearson correlation coefficient; EF, Nash–Sutcliffe Efficiency coefficient; ME, mean error; RMSE, root mean square error; Arb, Arbaminch; Hay, Haramaya; Mel, Melkassa.

116 CHAPTER 5: GEOSPATIAL MODELING OF CONSERVATION AGRICULTURE AND NITROGEN MANAGEMENT STRATEGIES IN ETHIOPIA Abstract Field research on agronomic practices can be complemented by crop simulations to provide information for the diverse production areas of Ethiopia. Previously calibrated and evaluated DSSAT CERES-Maize and CROPGRO models were used to assess long-term effect of conservation agriculture and N management practices for seven technology extrapolation domains (TED). Three simulated experimentations were conducted to evaluate the effects of time of N application with different rates and tillage types; five crop management alternatives (CPm, CTm, CAr, CPr, and CAr+N); and maize response to N rates. Simulated maize (Zea mays L.) grain yield was greater with N split for three applications compared with less frequent application for all TED, and overall maize grain yield was 663 kg ha-1 more with three-split N application time compared to the local recommendation. Maize grain yield was 33% higher with CAr+N compared to CPm averaged across TED after 30 yr of simulation while stored soil organic C and N were 8543 and 594 kg ha-1 more, respectively, in the 2-m soil depth. Nitrogen leaching loss was 65% less with CAr strategy compared with CPr. Maize grain yield was slightly increased under CAr but reduced under CPm strategy over time. Simulated soil organic C and N declined over time, but the rate of decline was higher with CPm compared to CAr. First order stochastic dominance (FSD) analysis for net returns showed that maize-based rotations dominated maize monoculture. At all TED, yield with CAr dominated over CPm. Simulated maize responses to fertilizer N were significantly affected by the main effects of N rate and crop management strategy across TED. Hence, N response functions were generated separately for all TED. The economically optimum N rate (EOR)was on average higher with no-tillage (NT) compared to traditional ard tillage (CT), but the profit cost ratio was higher with CT compare to

117 NT for all TED. Yield response to applied N was greater with NT compared with CT at all TED. The simulation-based EOR were higher compared to EOR determined from field experiments with CT and were on average 6% higher for NT compared with CT.

Abbreviations: CPm, conventional practice with monoculture; CTm, conservation tillage with monoculture; CPr, conventional practice with rotation; CAr, conservation agriculture; CAr+N, conservation agriculture with high N.

Key words: DSSAT, maize, nitrogen, conservation agriculture

5.1. Introduction Ethiopia is ecologically very diverse, ranging from tropical to temperate conditions. Technology extrapolation domains (TED) were determined with a crop suitability approach considering temperature and precipitation for Ethiopia (HarvestChoice, 2010; Van Wart et al., 2013a; GYGA, 2016). Sub-humid, humid, moist, and semi-arid climatic zones account for nearly all of the crop production (FAO, 1978), while about 51% of the country is in arid, semiarid and sub-moist zones (Tesfaye et al., 2015a). Crop production is largely managed by low input-output rainfed smallholder farmers and use of fertilizer remains low in the country. Only 30–40% of smallholders use fertilizer (Spielman et al., 2011). Many smallholders have financial constraints to fertilizer use and are concerned about risk of lack of fertilizer response and difficulty in repaying loans. Maize-based cropping systems are very important and are responsive to fertilizer N use in Ethiopia (Shiferaw et al., 2011; Tesfaye et al., 2015b) but production does not meet the demand. The mean maize yield was 3.4 Mg ha-1 for the 5 yr ending in 2016 (FAO, 2018) with yield constrained by soil water deficits and other abiotic, biotic and management constraints

118 (Sheferaw et al., 2011; Admassu et al., 2013; Tesfaye et al., 2015a). In comparison, the estimated rainfall-limited yield potential, without consideration of biotic and some abiotic constraints, has been estimated to be 12.4 Mg ha-1 (GYGA, 2018). Such constraints also limited response to applied nutrients (Tesfaye et al., 2015b). Optimization of fertilizer use, in the context of this study, connotes maximizing farmer profit resulting from fertilizer use, while not greatly adding to farmer risk (Kaizzi et al., 2017). This implies maximizing profit per hectare for farmers with adequate finance and net returns on small investments in fertilizer use made by financially constrained farmers. Estimation of profit from fertilizer use requires generating TED specific robust nutrient response functions for important annual food crops from field research results. Crop nutrient response functions are essential to efficiently apply economics to fertilizer use decisions. These were determined from results of field research data as asymptotic curvilinear-plateau functions taking the form of an exponential rise to a maximum or plateau yield (Kaizzi et al., 2012a, 2012b; Demissie and Bekele, 2017). In addition to generating response functions from fertilizer field research data, geospatial transfer of response functions among similar crop growing conditions is also possible (Kaizzi et al., 2017; Wortmann et al., 2017). Nitrogen nutrient is the most deficient nutrient in Ethiopian soils. Rate and timing of application are important for N fertilizer use optimization. Wrong N rate and untimely application can result in yield loss, low net returns, and environmental pollution due to N leaching. It is very common in Ethiopia to apply some N before or at planting as often there is a pop-up effect to stimulate early growth and root development. In cases of risk of poor crop establishment, N application may be more wisely done shortly after crop emergence and maybe with a rate adjustment according to establishment success. Application of appropriate N rate

119 identified based on N response functions specific to TED could improve maize productivity and reduce water and soil pollution. Some N application time should correspond to the beginning of very rapid N uptake by the crop, such as at the 8-leaf stage of maize, to reduce risk of N loss to leaching, especially beneficial on sandy soils and where much rainfall occurs during the early part of the crop season (Zingore et al., 2014). Improvement of N management is expected to increase in importance as the frequency of extreme weather events increases. Available N management good agronomic practices are not TED-specific or available for conservation agriculture (CA) in Ethiopia. Conservation agriculture is a management system characterized by zero or minimum tillage, permanent land cover with crop residue or plant growth, and crop rotation (FAO, 2018).Soil fertility and soil water management under erratic rainfall conditions may be improved with CA (Naudin et al., 2010; Scopel et al., 2013; Corbeels et al., 2014). Crop yields may be increased with CA in the short-term, especially where soil water deficits occur frequently, and over the long-term as a result of a gradual soil improvement (Thierfelder and Wall, 2009; 2012). Based on experimental evidence of increased water productivity under suboptimal rainfall conditions, CA has been attributed to mitigating negative effects from future climate change, when rainfall is projected to decrease and be more unreliable (e.g. Thierfelder and Wall, 2010). The improvement in soil water holding capacity under CA can increase N uptake by a crop in water limited regions. However, CA adaptation to local conditions is crucial for successful CA adoption by smallholder farmers. Long-term studies that report effects of CA, N rate and N application time specific to the heterogonous maize production zones in Ethiopia are lacking. Conducting field research to adapt and validate GAP for the wide variety of soil types and climatic conditions are time

120 consuming and expensive. Methodologies for GAP transfer for various TED is needed given resource limitations that researchers face. Geospatial modeling using crop simulation models is often considered useful to simulate different crop GAP under representative climatic scenarios for developing TED-specific adaptations (Jones et al., 2003; Rezzoug et al., 2008). In a recent comparison of potential and actual yield of maize across a range of cropping systems and environments, van Ittersum et al. (2013) concluded that use of crop simulation with a long-term weather database provides a more robust estimate than research plots because simulation better accounts for the impact of variations in temperature, solar radiation, and rainfall. Successful geospatial modeling of crop management strategies reduces research cost and shortens the time lag between development and transfer of GAP to farmers. Crop simulation models have become more useful with the incorporation of decision support systems that aid risk assessment and economic analyses of management strategies. Computer-based assessment of GAP under different soil and climatic scenarios enables TEDspecific adaptation, thereby enhancing the efficiency of the research process. In Africa, model simulation has provided a useful framework for designing field research for highly variable production environments, providing an opportunity for learning about new GAP and practices (Carberry et al., 2004) and for exploring options for sustainable intensification of production (Tittonell et al., 2009; Ollenburger et al., 2015). The Decision Support System for Agro-technology Transfer (DSSAT) connects several such models crop simulation models to the decision support system (Jones et al., 2003). DSSAT 4.6 has algorithms which can stimulate the influence of CA practices such as crop residue cover and tillage on soil surface properties and plant development (Hoogenboom et al., 2013). The other advantage of DSSAT 4.6 is that it has separate program drivers such as Rotational

121 Analysis and Seasonal Analysis, which has the ability to analyze and compare different management options biophysically and economically to guide choice of the most efficient management options (Hoogenboom et al., 2013). The models have been extensively calibrated and evaluated across diverse environments in sub-Saharan Africa (Thornton et al., 1995) and used for making decisions in crop management under different environments and simulating the effects of conservation agriculture on crop yields and soil properties in sub-Saharan Africa (Jones et al., 2003; Thornton et al., 2011; Ngwira et al., 2014). The objectives of this study in Ethiopia were to: (1) simulate the effects of N application time and CA on maize yield and to identify the most suitable GAP for seven TED; (2) simulate effect of N rate on maize grain yield and to generate N response functions for seven TED; and (3) to understand long-term effects of crop management strategies on crop productivity and soil properties under smallholder maize-food legume systems.

5.2. Materials and Methods 5.2.1. Site Description and Environmental Characterization Seven non-contiguous Global Yield Gap Atlas (GYGA) TED were selected with each accounting for >15,000 ha yr-1 of maize production (www.gyga.org) with at least two sites per TED for a total of 16 sites accounting for about 251,700 ha yr-1 of maize production in Ethiopia. Point simulations were conducted for CA alternatives, N application time and N response functions (Table 1). The seasonal rainfall amounts and its inter-seasonal variability differed by site and TED (Fig. 1) with relatively low rainfall and inter-seasonal variability for TED 7201 compared with other TED. Therefore, TED 7201 was considered to have low potential for maize-legume

122 production while the rest of TED could be considered as high potential areas for the maizelegume production (Table 1; Fig. 1). Table 1. Global Yield Gap Atlas Technology Extrapolation Domains (TED), targeted sites, their geographic locations, and total land areas represented by the selected sites in Ethiopia. TED Study site Soil texture Long. Lat. Alt. (m) Area (ha) 5501 Ambo Loam 37.84 8.96 2100 17295 5501 Kulumsa Loam 39.15 8.00 2200 9172 6301 Haramaya Sandy clay loam 42.03 9.40 1980 3680 6301 Arsi Negele Loam 38.68 7.35 1578 10533 6501 Bako Clay loam 37.03 9.07 1650 10272 6501 Waliso Clay 37.97 8.55 2060 17696 6501 Walkite Clay 37.78 8.27 1880 27492 6601 Bahirdar Clay loam 37.38 11.58 1790 20428 6601 Debremarkos Clay 37.74 10.33 2470 14541 6801 Jimma Clay loam 36.43 7.84 1750 39956 6801 Nekemte Clay loam 36.54 9.09 2110 18046 7201 Harar Sandy clay loam 42.10 9.31 1840 7878 7201 Melkassa Loam 39.33 8.40 1550 26626 7201 Shire Endasillase Sandy clay loam 38.33 14.10 1920 10065 7401 Areka Clay loam 37.45 7.04 1801 10706 7401 Gelemso Clay loam 40.53 8.81 1810 7337 Total 251,723

123

Figure 1. Inter-annual variability (a) and mean seasonal monthly distribution (b) of rainfall at the sixteen study sites in Ethiopia. The inter-annual variability and mean monthly distribution were based on 10 years rainfall data (1998-2007). Negele and Shire stands, respectively, for Arsi Negele and Shire Endasillase sites.

5.2.2. Model Description In Decision Support System for Agrotechnology Transfer (DSSAT), DSSAT v. 4.6, all crop models were combined into Cropping System Model (CSM), which is based on a modular modeling approach (Hoogenboom et al., 2013). The CSM simulate the effects of weather, soil, and management on crop growth, development and yield. The CSM uses one set of code for simulating soil water, N and C dynamics, while crop growth and development are, for example, simulated with the Crop Environment Resource Synthesis (CERES) and CROPGRO module (Hoogenboom et al., 2013). The CERES-Maizehas set a high standard and is widely used (Jones

124 and Kiniry, 1986). The CROPGRO crop template module in DSSAT-CSM is as described by Boote et al. (1998), but with its components fitted to a modular structure. The CROPGRO plant growth and development model simulates seven grain legumes including soybean and dry bean. The CERES-Maize, CROPGRO-Dry bean, and CROPGRO-Soybean models (DSSAT 4.6) were previously calibrated for the maize cv. BH546 and Melkassa-II, soybean cv Dhidhesa, and dry bean cv. Nassir under different N rates with pre-plant application and for CA compared with CP (Chapter 4, this dissertation). The calibrations were for rainfed conditions at Melkassa and Bako Agricultural Research Centers which, respectively, represent low and high potential areas for maize in Ethiopia (Table 1; Fig.1). Therefore, the calibrated and evaluated models were used to simulate N application time, maize N response, and CA versus CP. Input requirements for the models include data for weather, soil, cultivar, and management. CERESMaize was used to simulate maize response to time and rate of N application for the 16 sites. CERES-Maize and CROPGRO-Dry bean, CERES-Maize and CROPGRO-Soybean models were, respectively, used to extrapolate practices for the 16 sites. Corbeels et al. (2016) found DSSAT to be sensitive to changes in crop residue retained on the soil surface, bulk density due to tillage, and soil organic C when coupled with the CENTURY (Porter et al., 2010) soil model for CA simulations. In addition, unlike the default CERES soil model, CENTURY accounts for crop residue decomposition effect on soil organic C and nutrient dynamics, crop residue retention effect on runoff, surface albedo and shading by crop residue effect on soil surface evaporation, surface roughness, and soil texture on soil organic C turnover and nutrient mineralization (Porter et al., 2010). Therefore, the CENTURY soil model within DSSAT was used to simulate soil C and nutrient dynamics for modeling of conservation agriculture and conventional practices scenarios.

125 5.2.3. Soil and Weather Data Sources This study required long-term daily rainfall, minimum and maximum temperatures and solar radiation, and soil profile information for the 16 study sites to perform the simulations but the data was insufficient for most study sites. The soil profile descriptions were obtained from the “Global High-Resolution Soil Profile Database for Crop Modeling Applications” formed by combining SoilGrids and ISRIC-AfSIS at 1km resolution to develop a set of DSSAT compatible soil profiles on 5 arc-minute grid for the globe (HarvestChoice, 2015). The soil profile for each of the 16 study sites was selected according to their geographic grid. The gridded soil profile variables were compared with observed soil profile variables from Ambo, Bako, Bahirdar, Melkassa, and Jimma study sites and it was seen that the HarvestChoice interpolated soil organic C for Ethiopian soils were much overestimated. Because of this, the gridded soil organic C was modified based on available soil profile data and researcher expertise for all 16 sites. Depending on the degree of weather variability among years, at least 10–20 yr of daily weather data are needed for reliable assessment of the effect of a management practice on mean yield potential in an agroecological zone (Van Wart et al., 2013a). Due to inadequate weather data availability, the suitability of GYGA generated and WeatherMan weather generator generated daily weather data were evaluated, validated and used for the CSM simulations (Chapter 4, this dissertation). Consecutive 30 years of daily rainfall, maximum and minimum temperature, and solar radiation were generated based on 5 years daily observed weather data of the 16 study sites using WeatherMan weather generator. These data were used to run the crop models as GYGA generated weather data are only for 15 years, though with better quality.

126 5.2.4. Long Term Simulation Design Three 30-yr simulation experimentswere conducted at the 16 study sites of the seven TED using CSM to determine effects of N application time, N rate and CA on crop yield, soil organic C and N, and N leaching. All studies were with one crop per year and the simulations were for the main crop season at the study sites.

5.2.4.1. Nitrogen Application Time Three N application times, two tillage practices and four N rates were evaluated in a complete factorial of 24 treatments to explore the main effect of N application time and its interactions with N rate and tillage on maize grain yield and N leaching under maize monoculture with no crop during the dry season for the seven TED. Thus, 24 treatments were developed by combining different levels of N application time, tillage and N rate. The N application time levels were: (1) application of 50% N at planting (AP) and 50% at 40 days after planting (AP/40-DAP); (2) application of 50% N AP and 50% at 60 DAP (AP/60-DAP); and (3) application of 33.3% N AP, 33.3 % at 40 DAP and 33.3% at 60 DAP (AP/40-60-DAP). The N rates were 50, 75, 100, and 150 kg N ha-1. Urea (46-0-0) was the N fertilizer. The tillage systems included: (1) three passes with an oxen-drawn ard plow with 50 kg ha-1 for TED-7201, >75 kg ha-1for TED-5501 and 6501, and > 100 kg ha-1for the rest of TED resulted in very little added yield for both NT and CT strategies.

143

Figure 6. Cumulative probability (CP) function plots of maize grain yield for the five management strategies for 30 yr. DSSAT simulations at the seven TEDs in Ethiopia. The results were when the rotation crop was maize. Strategy 1, 2, 3, 4, and 5 were, respectively, CPm, CTm, CPr, CAr, and CAr+N.

144

Figure 7. Curvilinear to plateau nature of DSSAT simulated maize response to applied N under no-tillage (NT) and conventional tillage (CT) for the seven technology extrapolation domains in Ethiopia. CT: three passes with animal drawn ard implement without residue retention (