The performance of any crop depends on biophysical traits of the farm environment, biotic and abiotic stresses, inputs, and human activity. Most smallholder farmers in sub-Saharan Africa cannot afford or access inorganic fertilizers, irrigation, and genetically improved crop varieties. Without adequate resources and technology, agricultural production is inefficient and yields fall short of their potential.

At HarvestChoice we explore a range of possibilities aimed at boosting yields by using cropping system growth models to answer “what if…?” questions and to evaluate possible crop responses to different technologies, practices, and climate scenarios.

Potential Yields and Crop Growth Models

The difference between actual and potential crop yield under best practices provides a valuable, spatially explicit layer of information. Yield-gap analysis is based on the notion that there are a range of factors that determine the difference between the potential yield of a crop at any given location and the yield actually realized under a specific set of crop management conditions.

Traditionally, cropping system models simulate crop growth in a single field. HarvestChoice is scaling up the application of crop models across much broader geographic extents, such as sub-Saharan Africa and the globe. At present, HarvestChoice uses three aggregations to characterize production systems: subsistence/market-orientated; rainfed/irrigated; and low-med/med-high input use (e.g., fertilizers, pesticides). For each of the three systems, we use crop models to simulate estimated yield potential with existing crop varieties under best management practices using climate, soil, crop biology and input/management data layers. Currently this does not include the impacts of biotic constraints, but we will incorporate this as our pest and disease mapping work proceeds.

Individual models have their strengths and weaknesses, so HarvestChoice uses four different crop models to answer different types of questions at different scales: DSSAT, APSIM, ORYZA, and WOFOST. The relevance of individual models is conditioned by the original goal, conceptual design and operational versatility of each model, as well as by the experience and creativity of the analyst. Some models perform better than others in specific contexts (e.g., particular climates, crops, cropping patterns/rotations, soil quality indicators, and potential management interventions).

Crop Nutrient Responses

FAO FERTIBASE is a compilation of crop nutrient response data managed by the FAO Fertilizer Programme. The aim of this database is " allow for the extraction of yield data per agro-ecological zone for the main food crops in a specific country." As an example to a specific query, one output map shows the location of FAO maize trial locations and reported yield response to different types/amount of fertilizers.

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