期刊
AGRONOMY JOURNAL
卷 108, 期 4, 页码 1726-1734出版社
WILEY
DOI: 10.2134/agronj2015.0606
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资金
- LTSDA-NRCS Conservation Innovation Program [69-3A75-10-157]
- New York Farm Viability Institute
- USDA-NIFA WQ grant [2013-51130-21490]
- Northern New York Agricultural Development Program, USDA-Sustainable Agriculture Research and Extension [LNE13-328]
- International Plant Nutrition Institute
- McKnight Foundation
- NIFA [2013-51130-21490, 687218] Funding Source: Federal RePORTER
Maize (Zea mays L.) production accounts for the largest share of crop land area in the United States and is the largest consumer of nitrogen (N) fertilizers. Routine application of N fertilizer in excess of crop demand has led to well-documented environmental problems and social costs. Current N rate recommendation tools are highly generalized over space and time and therefore do not allow for precision N management through adaptive and site-specific approaches. Adapt-N is a computational tool that combines soil, crop, and management information with near-real-time weather data to estimate optimum N application rates for maize. We evaluated this precision nutrient management tool during four growing seasons (2011 through 2014) with 113 on-farm strip trials in Iowa and New York. Each trial included yield results from replicated field-scale plots involving two sidedress N rate treatments: Adapt-N-estimated and grower-selected (conventional). Adapt-N rates were on average 53 and 31 kg ha(-1) lower than Grower rates for New York and Iowa, respectively (-34% overall), with no statistically significant difference in yields. On average, Adapt-N rates increased grower profits by $65 ha(-1) and reduced simulated environmental N losses by 28 kg ha(-1) (38%). Profits from Adapt-N rates were noticeably higher under wet early-season conditions when higher N rate recommendations than the Grower rates prevented yield losses from N deficiencies. In conclusion, Adapt-N recommendations resulted in both increased grower profits and decreased environmental N losses by accounting for variable site and weather conditions.
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