These models are used for different purposes, such as crop management, yield gap analysis, crop-pest interactions, and climate change impact studies. Numerous simulation models, such as statistical, mechanistic, deterministic, stochastic, dynamic, and static, are designed with varying levels of complexity to simulate crop species and cultivars to predict growth and yield depending on the availability of the data and information. Crop modelling represents a better way of creating knowledge by summarizing data that helps to assess genotype performance across diverse target environments (G x E) and reduce the turnaround time in delivering the product. Modern plant breeding is data-driven and is based on statistical prediction models that leverage genomic and phenomic data to accelerate genetic improvement. The breeders can design and implement efficient and effective breeding strategies through such decision support systems. CSMs are computational tools used to mimic crops’ growth and developmental stages utilizing climatic and geographic data. Crop simulation models (CSM) are essential tools to characterize the agricultural system and predict the impact of climate changes on crop management practices and new breeding technologies. The optimization of breeding strategies is complex and is compounded by cost, time, and resources and addresses fewer traits than desired. Crop production requires the development of new genotypes that meet specific agronomic traits. However, by exploring farm diversity we established that, under certain conditions, the effect of the future climate might be as important as the effect of management changes in the current climate, hinting at the importance of locally-relevant management practices.Accelerating crop improvement programs is necessary to meet the growing demand due to population growth and mitigating biotic and abiotic stresses. We conclude that in the Sudano-Sahelian zone of West Africa sorghum, as it is cultivated today, appears moderately vulnerable to climate change, while doubling fertilizer inputs with an adjusted planting density, in the current climate, would more than double yields. Shifting to an improved cultivar had a marginal effect on grain yields, while increased fertilizer rates resulted in grain yield increases ranging of 20% and 153% forĭSSAT and APSIM, respectively, assuming the current climate. In Koutiala, sorghum yield changes for future climates ranged from −38 to +8% assuming current management. The addition of genetic improvement resulted in further yield increases (24%). In Navrongo, under current management, sorghum yields either decreased or increased compared to the baseline, depending on the crop models and the GCMs changes in management options induced a yield increase of up to 256%. Baseline climate data from observed weather (1980–2009) andįuture climates from five Global Circulation Models (GCMs: 2040–2069) in two Representative Concentration Pathways (RCP 4.5 and 8.5) were used as inputs for crop models.
#Apsim sorghum simulator
We applied the Decision Support Systems for Agro-Technological Transfer (DSSAT) Cropping Systems Model, and the Agricultural Production Systems sIMulator (APSIM), for a multiple-farm assessment (i.e.diverse types of management and soils) in Koutiala (Mali) and Navrongo (Ghana), which are representative sites for West African sorghum production systems. We assessed the sensitivity of current agricultural practices to climate change and to improved management practices: (i) increased fertilizer application combined with increased plant populations and (ii) use of improved sorghum varieties. The productivity of smallholder farming systems is held back by poor soil fertility, low input levels and erratic rainfall distribution in the sorghum-based cropping systems of the Sudano-Sahelian zone of West Africa.
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