Modeling adaptation of sorghum in Ethiopia with APSIM—opportunities with G×E×M

Abstract

Sorghum is an important food and feed crop in the dry lowland areas of Ethiopia. Farmers grow both early-sown long-duration landraces and late-sown short-duration improved varieties. Because timing and intensity of drought stress can vary in space and time, an understanding of major traits (G), environments (E), management (M), and their interactions (G×E×M) is needed to optimize grain and forage yield given the limited available resources. Crop simulation modeling can provide insights into these complex G×E×M interactions and be used to identify possible avenues for adaptation to prevalent drought patterns in Ethiopia. In a previous study predictive phenology models were developed for a range of Ethiopian germplasm. In this study, the aims were to (1) further parameterize and validate the APSIM-sorghum model for crop growth and yield of Ethiopian germplasm, and (2) quantify by simulation the productivity-risk trade-offs associated with early vs late sowing strategies in the dry lowlands of Ethiopia. Field experiments involving Ethiopian germplasm with contrasting phenology and height were conducted under well-watered (Melkassa) and water-limited (Miesso) conditions and crop development, growth and yield measured. Soil characterization and weather records at the experimental sites, combined with model parameterization, enabled testing of the APSIM-sorghum model, which showed good correspondence between simulated and observed data. The simulated productivity for the Ethiopian dry lowlands environments showed trade-offs between biomass and grain yield for early and late sowing strategies. The late sowing strategy tended to produce less biomass except in poor seasons, whereas it tended to produce greater grain yield except in very good seasons. This study exemplified the systems approach to identifying traits and management options needed to quantify the production-risk trade-offs associated with crop adaptation in the Ethiopian dry lowlands and further exemplifies the general robustness of the sorghum model in APSIM for this task.

Implementation and application of APSIM for crop modelling in Ethiopia: A comprehensive review

“…Among the wheat across different locally grown early, medium, and late-growing cultivars such as Hartog [ 18 ], HAR-2501 (Hawi) [ 19 ], Hawi, and Digelu cultivars [ 20 ] were used for APSIM wheat calibration and validation. For sorghum model calibration and validation, locally available cultivars Gigrite [ 21 ], Meko, and Jigurti [ 22 ] were used. Since maize is the most modeled crop in Ethiopia using the APSIM system, lots of locally grown cultivars are tested in different years.…”

“…In addition to the above-presented results in Table 1 , other additional specific study places were identified from Ref. [ 22 ] research such as Humera (Western Tigray), Axum (Central Tigray), Shiraro (North-Western Tigray), Debark (North Gonder), Shoa Robit (North Shewa), Kobo (North Wollo), Sirinka (North Wollo), Jijiga (Fafan), Babile (Eastern Hararge), Metehara (East Shewa), Konso (Konso), Gato (Konso), and Kayafer (South Omo) by growing the crops in Melkassa and Miesso. Among the different researchers, Chemura et al [ 26 ] try to predict the yield of maize in large study areas including Assosa, Guraghe, Hadiya, Illubabor, Jimma, Kemashi, Metekel, North Gonder, North Shewa, North Wollo, Sidama, South Gonder, West Gojam, and West Tigray.…”

“…While other researchers, Refs. [ 18 , [20] , [21] , [22] , 25 , 26 , 29 , 31 ] used these input data to investigate the climate change impact and to forecast the effect of climate change on the crop production. Furthermore, Refs.…”

“…[ 22 , 23 , 27 , 32 ] applied these input data to validate the APSIM model simulation results. Commonly used weather data input for the APSIM model includes daily weather data (highest air temperature, lowest air temperature, solar radiation, precipitation, and rainfall) [ 23 , 26 ], as well as soil water parameters (bulk density, air dryness, drained upper and lower limits, saturated soil water content, and root water extraction coefficient), were needed for the calibration of the APSIM model [ 22 ]. Soil data used as input for the APSIM model are soil carbon parameters including organic carbon, microbial biomass pool, and inert fraction, and soil nitrogen parameters including soil pH, nitrate, and ammonia [ 23 , 27 ].…”

Enhancing our understanding of optimal nitrogen fertilizer rates for maize: A synthesis of long-term experimentation and crop modeling

“…The APSIM model is well suited to investigate GxExM factors influencing cropping systems performance (Holzworth et al, 2014;Rotili et al, 2020;Winn et al, 2022;Tirfessa et al, 2023). The crop models within APSIM simulate grain yield and other aspects such as biomass production and partitioning, root growth, water use, and N uptake as a function of weather conditions and environmental stresses .…”