Yield Gap Analysis of Alfalfa Grown under Rainfed Condition in Kansas

The yield and production of alfalfa (Medicago sativa L.) have not been significantly improved in Kansas for the last 30 years even though farmers are using improved varieties. We have noted a significant yield difference between average alfalfa yield reported by farmers and researchers. The magnitude of yield gap in Kansas and its underlying factors are still unknown. Thus, understanding of potential yield is essential to meet the future forage demand with the limited production resources. The main objective of this study was, therefore, to quantify the current yield gap and identify the main yield-limiting factor for rainfed alfalfa grown in Kansas. To achieve this objective, we selected 24 counties in Kansas based on the rainfed production area and total production, and used county-level yield, daily temperature, and rainfall data from the past 30 yrs (1988–2017) of those selected counties. We applied four statistical approaches: (i) probability distribution function to delineate county-level alfalfa growing season, (ii) stochastic frontier yield function to estimate optimum growing season rainfall (GSR) and attainable yield, (iii) linear boundary function to estimate minimum water loss, water use efficiency, and water-limited potential yield, and (iv) conditional inference tree to identify the major yield contributing weather variables. The probability distribution function delineated the alfalfa growing season starting from mid-March to mid-November in Kansas. The frontier model estimated the attainable yield of 9.2 Mg ha−1 at an optimum GSR of 664 mm, generating a current yield gap of 18%. The linear boundary function estimated the water-limited potential yield of 15.5 Mg ha−1 at an existing GSR of 624 mm, generating a yield gap of 50%. The conditional inference tree revealed that 24% of the variation in rainfed alfalfa yield in Kansas was explained by weather variables, mainly due to GSR followed minimum temperature. However, we found only 7% GSR deficit in the study area, indicating that GSR is not the only cause for such a wide yield gap. Thus, further investigation of other yield-limiting management factors is essential to minimize the current yield gap. The statistical models used in this study might be particularly useful when yield estimation using remote sensing and crop simulation models are not applicable in terms of time, resources, facilities, and investments.