Potential to reduce the nitrate residue after harvest in maize fields without sacrificing yield through precision nitrogen management

IF 5.4 2区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY Precision Agriculture Pub Date : 2023-12-30 DOI:10.1007/s11119-023-10100-1

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Abstract

Site-specific nitrogen management has been proposed as a tool to increase crop yield while decreasing nutrient losses to the environment. Many reports can be found on sensing technologies to quantify the variability within a field and the definition of management zones based on the observed variability. However, fewer studies have been dedicated to the selection of the most suitable N fertilizer management scenario: should more or less nutrients be applied in the zones with a lower crop productivity potential? To address this knowledge gap, nine Flemish maize fields were selected as potential candidates for precision fertilization based on the soil maps and historical vegetation index patterns. Within each field, two management zones were identified based on historical vegetation index patterns and electrical conductivity maps, and different fertilization strategies were tested in each zone. The field trial results in terms of yield and soil residual nitrate showed that site-specific N management outperforms the conventional practice only in the fields with temporally stable management zones. In the fields having differences in the physical soil properties (e.g. presence of stones or clay particles), affecting water availability, lower fertilization in zones with a poor soil productivity potential could be recommended. In the fields where the performance of the management zones changes from year to year mainly due to annual variation in precipitation, a risk of incorrect implementation of the precision fertilization concept was identified. Historical NDVI time series serve a good basis to delineate the temporally stable management zones.

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通过精确氮管理减少玉米田收获后硝酸盐残留而不影响产量的潜力

摘要针对具体地点的氮肥管理被认为是提高作物产量同时减少环境养分损失的一种工具。许多报告介绍了用于量化田间变异性的传感技术，以及根据观测到的变异性确定管理区的方法。然而，专门针对如何选择最合适的氮肥管理方案的研究较少：在作物生产潜力较低的区域应该施用更多还是更少的养分？为了填补这一知识空白，我们根据土壤地图和历史植被指数模式，选择了九块佛兰德玉米田作为精准施肥的潜在候选地。在每块田中，根据历史植被指数模式和电导率图确定了两个管理区，并在每个管理区测试了不同的施肥策略。田间试验的产量和土壤残留硝酸盐结果表明，只有在具有时间稳定管理区的田块中，因地制宜的氮肥管理才优于常规做法。在土壤物理特性存在差异（如存在石块或粘粒）、影响水分供应的田块中，建议在土壤生产力潜力较低的区域减少施肥量。在一些田块，管理区的表现每年都会发生变化，主要是由于降水量的年际变化造成的。历史 NDVI 时间序列为划分时间稳定的管理区提供了良好的基础。

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来源期刊

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.