Rukayat Afolake Oladipupo, Ajit Borundia, Abdul Mounem Mouazen
{"title":"评估小麦变速氮肥两种传感方法的效益","authors":"Rukayat Afolake Oladipupo, Ajit Borundia, Abdul Mounem Mouazen","doi":"10.1007/s11119-025-10241-5","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>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.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>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.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>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<sup>−1</sup> and VRNF2 up to 358 EUR ha<sup>−1</sup> 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<sup>−1</sup>) in one field, while VRNF2 was more profitable in the other field by 69 EUR ha<sup>−1</sup>. Additionally, VRNF2 demonstrated superior environmental benefits, using 14% less fertilizer than URNF and 12% less than VRNF1.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>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.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"8 1","pages":""},"PeriodicalIF":5.4000,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Assessing benefits of two sensing approaches for variable rate nitrogen fertilization in wheat\",\"authors\":\"Rukayat Afolake Oladipupo, Ajit Borundia, Abdul Mounem Mouazen\",\"doi\":\"10.1007/s11119-025-10241-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<h3 data-test=\\\"abstract-sub-heading\\\">Purpose</h3><p>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.</p><h3 data-test=\\\"abstract-sub-heading\\\">Methods</h3><p>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.</p><h3 data-test=\\\"abstract-sub-heading\\\">Results</h3><p>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<sup>−1</sup> and VRNF2 up to 358 EUR ha<sup>−1</sup> 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. 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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.
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.