CONICET | Buscador de Institutos y Recursos Humanos

Yield gap (difference between the attainable yield of a crop in a given environment and the average yield achieved by farmers) assessment, and the identification of gap putative causes are important in the contexts of food security and research prioritization. The farmer's attainable yield gaps for all 8 sunflower producing regions of Argentina has been shown to exceed the expected floor value of 25% of average farmer yields, indicating a need for research aimed at narrowing the gap. Remote sensing allows the documentation of the dynamics of intermediate variables (pretaining to the crop and to its environment) that affect yield at geospatially specified sites. Here we report results obtained during the first year of a three-year project aimed at identifying putative causes of sunflower yield gaps using remote sensors. We compiled a geographic database combining information on crop management (dates of sowing & harvest, yield, previous crop, etc.), satellite data (NDVI, precipitation) for the previous crop, the inter-crop fallow and the target crop and weather stations (radiation, temperature max & min etc.) for geospatially defined fields of more than 30 ha in size (n =110) in two sunflower-growing regions (La Pampa [LP] and southwest of Buenos Aires [SWBA]). Frontier regression analysis was used to model the limiting relationship between yield and photosynthetically active radiation absorbed by the crops (APAR). APAR was estimated from normalized difference vegetation index (NDVI) and incident radiation data obtained from meteorological stations. Distances to the boundary function were calculated for each data point. Aditonally, a frontier regression analysis was also performed for the relationship between yield and seasonal rain using daily precipitation estimates derived from remotely sensed data. The yield vs. APAR relationship for the pooled data was significant and positive (p