评估小麦变速氮肥两种传感方法的效益

IF 5.4 2区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY
Rukayat Afolake Oladipupo, Ajit Borundia, Abdul Mounem Mouazen
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引用次数: 0

摘要

在当代农业中,在保护环境的同时实现可持续粮食生产至关重要。传统的匀速施氮常常导致农田氮肥过量或施用不足,对经济、农艺和环境造成负面影响。可变速率氮肥(VRNF)利用土壤和作物的变异来优化氮素施用,从而提高氮素利用效率,减少对环境的影响。本研究在比利时和法国的两个麦田中对两种VRNF解决方案进行了评价和比较。方法VRNF1利用离子选择电极(ISE)传感器现场测量土壤硝酸盐,VRNF2利用可见光和近红外光谱仪(vis-NIRS)和作物归一化植被指数(NDVI)融合在线测量的关键土壤特性。在VRNF1中,通过vis-NIRS-NDVI数据的聚类分析(与VRNF2一样),利用土壤硝酸盐值对管理区(MZs)的肥力水平进行排序,氮肥施用量调整为30-50%,在高肥力区施用较低的氮肥,在低肥力区施用较高的氮肥。在VRNF2中,通过在线测量pH、有机碳(OC)、水分含量(MC)、钾(K)、磷(P)、钙(Ca)和作物NDVI对MZ的肥力水平进行排序后,与VRNF1相似地调整氮肥施用量。结果成本效益分析显示,两种VRNF解决方案的毛利率都高于URNF, VRNF1和VRNF2的毛利率分别比URNF高289欧元和358欧元。与URNF相比,VRNF1将作物产量提高了8%,而VRNF2的产量提高了9.2%。然而,VRNF1在一个油田获得了轻微的经济优势(14欧元/公顷- 1),而VRNF2在另一个油田获得了69欧元/公顷- 1的利润。此外,VRNF2表现出了更优越的环境效益,比URNF减少14%的肥料用量,比VRNF1减少12%。结论总体而言,VRNF2比VRNF1和URNF具有更好的经济和环境效果。然而,在没有VRNF2的历史产量数据的情况下,将mz划分为不同肥力水平的主观性引起了人们的关注,呼吁在这种情况下,在未来的VRNF1方案中采用VRNF1。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Assessing benefits of two sensing approaches for variable rate nitrogen fertilization in wheat

Purpose

In contemporary agriculture, achieving sustainable food production while preserving the environment is crucial. Traditional uniform rate nitrogen fertilization (URNF) often leads to over- or under-applications of N in fields with negative economic, agronomic and environmental issues. Variable rate nitrogen fertilization (VRNF) has shown promise in optimizing N application by accounting for soil and crop variability, thus improving nitrogen use efficiency and reducing environmental impact. This study evaluates and compares two VRNF solutions in two wheat fields in Belgium and France.

Methods

The first, VRNF1 relied on onsite measurement of soil nitrate using ion-selective electrode (ISE) sensors, whereas the second, VRNF2, utilizes the fusion of on-line measured key soil properties using a visible and near-infrared spectrometer (vis-NIRS) and crop normalized difference vegetation index (NDVI). In VRNF1, soil nitrate values were used to rank the fertility level of management zones (MZs), delineated by the clustering analysis of vis-NIRS-NDVI data (like for VRNF2), with N fertilization rates adjusted by 30–50%, applying lower rates to high-fertility zones and higher rates to low-fertility zones. In VRNF2, after the fertility level of MZ was ranked by examining the on-line measurements of pH, organic carbon (OC), moisture content (MC), potassium (K), phosphorus (P), and calcium (Ca), and crop NDVI, N fertilizer rates were adjusted similarly to VRNF1.

Results

A cost-benefit analysis revealed that the gross margin of both VRNF solutions was larger than that of the URNF, with VRNF1 providing up to 289 EUR ha−1 and VRNF2 up to 358 EUR ha−1 more gross margin than URNF. VRNF1 increased crop yield by up to 8%, while VRNF2 resulted in a 9.2% yield increase compared to URNF. However, VRNF1 achieved a slight economic advantage (14 EUR ha−1) in one field, while VRNF2 was more profitable in the other field by 69 EUR ha−1. Additionally, VRNF2 demonstrated superior environmental benefits, using 14% less fertilizer than URNF and 12% less than VRNF1.

Conclusion

Overall, VRNF2 offered better economic and environmental outcomes than VRNF1 and URNF. However, the subjectivity of ranking MZs into different fertility levels in the absence of historical yield data for the VRNF2 raises concerns, calling in such a situation for VRNF1 to be adopted in future VRNF schemes.

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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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