对基于作物预测的冬小麦季节性氮肥施用方法进行田间评估

IF 5.4 2区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY Precision Agriculture Pub Date : 2024-08-03 DOI:10.1007/s11119-024-10175-4
Palka M., Manschadi A.M.
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引用次数: 0

摘要

不适当的氮肥施用方法没有考虑到季节性多变的天气条件及其对作物产量潜力和氮需求的影响,导致作物氮利用效率降低。因此,作物生产系统的生态和经济可持续性都面临风险。本研究的目的是开发一种针对不同季节的作物预测方法,与经验性氮肥施用方法相比,这种方法可以在冬小麦上有针对性地施用氮肥,同时保持农业收入。本研究的作物预测是使用基于过程的作物模型 SSM,结合奥地利东部案例研究地区最先进的季节性集合天气预报(SEAS5)生成的。三项冬小麦田间试验的结果表明,采用基于作物预报的氮肥施用方法(-43.33 千克氮/公顷-1,-23.42%)与经验氮肥施用方法相比,氮肥施用量显著减少,且不会影响优质谷物的销售收入。这种减少氮肥施用量的方法所带来的收益通过施用氮肥的经济回报(ERAN)进行量化。在保持收益的同时,较低的氮施用量也带来了显著的收益,ERAN 为 + 30.22%(+ 2.20 欧元 kgN-1)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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On-farm evaluation of a crop forecast-based approach for season-specific nitrogen application in winter wheat

Inadequate nitrogen (N)-fertilisation practices, that fail to consider seasonally variable weather conditions and their impacts on crop yield potential and N-requirements, cause reduced crop N-use efficiency. As a result, both the ecological and economic sustainability of crop production systems are put at risk. The aim of this study was to develop a season-specific crop forecasting approach that allows for a targeted application of N in winter wheat while maintaining farm revenue compared to empirical N-fertilisation practices. The crop forecasts of this study were generated using the process-based crop model SSM in combination with state-of-the-art seasonal ensemble weather forecasts (SEAS5) for the case study region of Eastern Austria. Results from three winter wheat on-farm experiments showed a significant reduction in applied N when implementing a crop forecast-based N-application approach (-43.33 kgN ha-1, -23.42%) compared to empirical N-application approaches, without compromising revenue from high-quality grain sales. The benefit of this reduced N-application approach was quantified through the economic return to applied N (ERAN). While maintaining revenue, the lower amounts of applied N led to significant benefits of + 30.22% (+ 2.20 € kgN-1) in ERAN.

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来源期刊
Precision Agriculture
Precision Agriculture 农林科学-农业综合
CiteScore
12.30
自引率
8.10%
发文量
103
审稿时长
>24 weeks
期刊介绍: Precision Agriculture promotes the most innovative results coming from the research in the field of precision agriculture. It provides an effective forum for disseminating original and fundamental research and experience in the rapidly advancing area of precision farming. There are many topics in the field of precision agriculture; therefore, the topics that are addressed include, but are not limited to: Natural Resources Variability: Soil and landscape variability, digital elevation models, soil mapping, geostatistics, geographic information systems, microclimate, weather forecasting, remote sensing, management units, scale, etc. Managing Variability: Sampling techniques, site-specific nutrient and crop protection chemical recommendation, crop quality, tillage, seed density, seed variety, yield mapping, remote sensing, record keeping systems, data interpretation and use, crops (corn, wheat, sugar beets, potatoes, peanut, cotton, vegetables, etc.), management scale, etc. Engineering Technology: Computers, positioning systems, DGPS, machinery, tillage, planting, nutrient and crop protection implements, manure, irrigation, fertigation, yield monitor and mapping, soil physical and chemical characteristic sensors, weed/pest mapping, etc. Profitability: MEY, net returns, BMPs, optimum recommendations, crop quality, technology cost, sustainability, social impacts, marketing, cooperatives, farm scale, crop type, etc. Environment: Nutrient, crop protection chemicals, sediments, leaching, runoff, practices, field, watershed, on/off farm, artificial drainage, ground water, surface water, etc. Technology Transfer: Skill needs, education, training, outreach, methods, surveys, agri-business, producers, distance education, Internet, simulations models, decision support systems, expert systems, on-farm experimentation, partnerships, quality of rural life, etc.
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