Stabilizing Rainfed Wheat Yields with Supplemental Irrigation and Nitrogen in a Mediterranean Climate

Abstract

AbstractThe West Asia‐North Africa (WANA) region, with a Mediterranean‐type climate, has an increasing deficit in cereal production, especially bread wheat (Triticum aestivum L.). Rainfed cropping coincides with the relatively cool, rainy winter season, usually from October to May. Cereal yields are low and variable in response to inadequate and erratic seasonal rainfall and associated management factors, such as lack of N and late sowing. In an area where water is limited, small amounts of supplemental irrigation (SI) water can make up for the deficits in seasonal rain and potentially produce satisfactory yields. This field study (1992–1993 to 1995–1996) on a deep clay soil (a Calcixerollic Xerochrept) in northern Syria was conducted for four growing seasons to assess the effects of SI (rainfed, 1/3, 2/3, and full irrigation) combined with N rate (0, 50, 100, and 150 kg ha−1) and sowing date (early, normal, and late) on one traditional (Mexipak 65) and three improved bread wheat cultivars (Cham 4, Cham 6, Gomam). Yields of rainfed wheat varied with seasonal rainfall and its distribution, with all main factors having significant effects. A delay in the sowing date from November to January consistently reduced yields and the response to both SI and N. With irrigation, crop responses were generally significant up to 100 kg N ha−1, while optimum response for rainfed conditions was with 50 kg N ha−1. An addition of only limited irrigation (1/3 full irrigation) significantly increased yields, but near maximum yields were obtained by 2/3 of full irrigation. Responses to N and SI were greatest for the higher‐yielding cultivars. Use efficiency for both water and N was greatly increased by SI. Thus, with minimum irrigation during the winter growing season combined with appropriate management, inputs, and varieties, wheat output could be substantially and consistently increased in the semiarid Mediterranean zone. Production functions developed from this dataset can help predict the effects of changing any of these parameters in other locations in the region.