191-10 Assessing Causes of Yield Gaps in Agricultural Areas with Diversity in Climate and Soils.
See more from this Division: ASA Section: Agronomic Production Systems
See more from this Session: Agronomic Production Systems General Oral
Tuesday, October 24, 2017: 10:50 AM
Tampa Convention Center, Room 3
Abstract:
Identification of causes for gaps between yield potential (Yp) and producer yields has been restricted to small geographic areas. We developed a novel approach for identifying causes of yield gaps over large agricultural areas with diversity in climate and soils. Survey data on yield and management were collected from 3568 producer fields over two crop seasons in the North-Central USA (NC USA) region, and grouped into 10 technology extrapolation domains (TED) depending upon their soil, climate, and water regime. Yp was estimated using a combination of crop modeling and boundary functions for water productivity. Yield gaps were calculated as the difference between Yp and average producer yield. Explanatory factors for yield gaps were investigated by identifying management factors that were concordantly associated with high- and low-yield fields. Across the 10 TEDs, soybean average Yp ranged from 3.3 to 5.3 Mg ha-1 for rainfed fields and from 5.3 to 5.6 Mg ha-1 for irrigated fields. Highest producer yields in each TED were similar to the estimated Yp. Yield gap, calculated as percentage of yield potential, was larger in rainfed (range:15-28%) than in irrigated (range:11-16%) soybean. Upscaled to NC USA region, Yp was 4.8 Mg ha-1 (rainfed) and 5.7 Mg ha-1 (irrigated), with a respective yield gap of 22 and 13% of Yp. Sowing date, tillage, and in-season foliar fungicide and/or insecticide were identified as explanatory causes for yield variation in half or more of the 10 TEDs. Analysis of in-season weather helped interpret management x TED interactions. For example, yield increase due to advances in sowing date was greater in TEDs with less water limitation during the pod setting phase. The present study highlights the strength of combining producer survey data with a biophysical spatial framework to measure yield gaps, identify management factors explaining these gaps, and understand the biophysical drivers influencing yield responses to field management.
See more from this Division: ASA Section: Agronomic Production Systems
See more from this Session: Agronomic Production Systems General Oral