Quantifying Yield Gaps and Abiotic Stresses in Rain-fed Production Systems of Thailand: Global Theme on Agroecosystems Report no. 45

Quantifying potential yield and yield gap of crops for various growing conditions could provide valuable information for designing strategic crop management plans to increase crop yields. The farmers in the Phu Pha Man district of Khon Kaen province of Thailand commonly grow soybean and peanut under both rainfed and irrigated conditions and cultivate maize under rain-fed conditions. The farmers’ long-term average yields in the district are 1360 kg ha-1 for soybean, 1480 kg ha-1 for peanut and 2810 kg ha-1 for maize. The simulation results, using CSM-CROPGRO models for soybean, peanut and maize, showed that for the Phu Pha Man district, the yield potential of soybean ranged from 1130 to 3700 kg ha-1, maize ranged from 1370 to 7460 kg ha-1 and peanut ranged from 630 to 3880 kg ha-1 under rain-fed conditions. For the fully irrigated conditions in the dry season, the yield potential of soybean ranged from 1870 to 3150 kg ha-1 and peanut ranged from 1840 to 3010 kg ha-1. The yields were generally higher for early planting dates than for later plantings. These results indicated that farmers’ yields under rain-fed conditions in the Phu Pha Man district can be more than doubled with improved management practices. Yield gap analysis for Tad Fa watershed in Phu Pha Man district of Khon Kaen showed that under soil water and nitrogen nonlimiting conditions, the yield potential of soybean ranged from 2810 to 3630 kg ha-1 and for maize, it ranged from 4360 to 6130 kg ha-1. The yield reductions from the yield potential caused by water and nitrogen limitations ranged from 12% to 48% for soybean and 29% to 83% for maize. Low rates of nitrogen application and pests and diseases were the main factors causing yield gaps of soybean and maize in the Phu Pha Man district. Regional analysis of peanut yields showed that northeastern region of Thailand is more productive area for rain-fed conditions, whereas northern region is more suitable to produce peanut under well-irrigated conditions during the dry season

Application of the Cropping System Model (CSM)-CROPGROSoybean for Determining Optimum Management Strategies for Soybean in Tropical Environments

The determination of optimum crop management practices for increasing soybean production can provide valuable information for strategic planning in the tropics. However, this process is time consuming and expensive. The use of a dynamic crop simulation model can be an alternative option to help estimate yield levels under various growing conditions. The objectives of this study were to evaluate the performance of the Cropping System Model (CSM)-CROPGROSoybean and to determine optimum management practices for soybean for growing conditions in the Phu Pha Man district, Thailand. Data from two soybean experiments that were conducted in 1991 at Chiang Mai University and in 2003 at Khon Kaen University were used to determine the cultivar coefficients for the cultivars CM 60 and SJ 5. The CSM-CROPGRO-Soybean model was evaluated with data from two experiments that were conducted at Chiang Mai University. The observed data sets from farmers’ fields located in the Phu Pha Man district were also used for model evaluation. Simulations for different management scenarios were conducted with soil property information for seven different soil series and historical weather data for the period 1972–2003 to predict the optimum crop management practices for soybean production in the Phu Pha Man district. The results of this study indicated that the cultivar coefficients of the two soybean cultivars resulted in simulated growth and development parameters that were in good agreement with almost all observed parameters. Model evaluation showed a good agreement between simulated and observed data for phenology and growth of soybean, and demonstrated the potential of the CSM-CROPGROSoybean model to simulate growth and yield for local environments, including farmers’ fields, in Thailand. The CSM-CROPGRO-Soybean simulations indicated that the optimum planting dates from June 15 to July 15 produced maximum soybean yield in a rainfed environment. However, the planting date December 15 produced the highest yield under quality irrigation. Soybean yield was slightly improved by applying nitrogen at a rate of 30 kg N ha)1 at planting. Soybean yield also improved when the plant density was increased from 20 to 40 plants m)2. The results from this study suggest that the CSM-CROPGRO-Soybean model can be a valuable tool in assisting with determining optimum management practices for soybean cropping systems in the Phu Pha Man district and might be applicable to other agricultural production areas in Thailand and southeast Asia

Evaluation of the CSM-CROPGRO-Soybean model for dual-purpose soyabean in Kenya

Limited information is available on the potential performance of introduced dual purpose varieties across different Kenyan soils and agro-ecological environments and consistency across sites and seasons. Crop simulation modeling offers an opportunity to explore the potential of and select introduced cultivars for new areas before establishing costly and time-consuming field trials. Dual purpose soybeans were introduced due to their ability to improve soils and at the same time provide substantial grain yields. The objective of this study was to derive genetic coefficients of recently introduced dual purpose soybean varieties and to explore the reliability of the Cropping System Model (CSM)-CROPGRO-Soybean model in simulating phenology and yield of the dual purpose varieties under different environments. Field trials for seven varieties were conducted across three sites in two seasons and data on phenology and management, soil characteristics and weather was collected and used in the CROPGRO model. A stepwise procedure was used in the calibration of the model to derive the genetic coefficients. Two sets of data from Kakamega and Kitale were used in calibration process while 2006 data for Kakamega and Msabaha, were used for evaluation of the model. The derived genetic coefficients provided simulated values of various development and growth parameters that were in good agreement with their corresponding observed values for most parameters. Model evaluation with independent data sets gave similar results. The differences among the cultivars were also expressed through the differences in the derived genetic coefficients. CROPGRO was able to accurately predict growth, phenology and yield. The model predicted the first flowering dates to within 2¿3 days of the observed values, the first pod dates within 3 days of the observed values and yields within 5¿300 kg ha-1 of the observed yields. The genetic coefficients derived in CROPGRO model can, therefore, be used to predict soybean yield and phenology of the dual purpose soybean varieties across different agro-ecological zones. (Résumé d'auteur