Evaluation of the PROMET model for yield estimation and N fertilization in on-farm research

IF 5.4 2区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY Precision Agriculture Pub Date : 2024-09-09 DOI:10.1007/s11119-024-10183-4

B. Brandenburg, Y. Reckleben, H. W. Griepentrog

{"title":"Evaluation of the PROMET model for yield estimation and N fertilization in on-farm research","authors":"B. Brandenburg, Y. Reckleben, H. W. Griepentrog","doi":"10.1007/s11119-024-10183-4","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Introduction</h3><p>Satellite-sourced data have become a valuable resource for precision agriculture because they provide crucial insights into various parameters that are essential for effective crop management. An array of practical agricultural tools provides comprehensive data for assessing crop biomass, soil conditions, and plant stress symptoms, predicting yields, and performing other functions. Satellite data, when combined with in situ data from different sources, can significantly enhance biomass and yield estimations.</p><h3 data-test=\"abstract-sub-heading\">Material and Methods</h3><p>The ability of the “PROcesses of radiation, Mass and Energy Transfer” (PROMET) model to predict crop biomass and grain yield and to optimize nitrogen fertilization during the vegetation period was investigated. Field trials were conducted to assess the accuracy and limitations of biomass and yield predictions.</p><h3 data-test=\"abstract-sub-heading\">Results and Conclusion</h3><p>The predicted yields were sufficiently accurate on a whole-field basis, and site-specific values showed strong correlations. In additional field trials with different fertilization strategies, the highest yield and nitrogen efficiency were observed for the PROMET-based strategy. Additional experiments with different crops and greater durations are needed to draw a more reliable conclusion.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":null,"pages":null},"PeriodicalIF":5.4000,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Precision Agriculture","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.1007/s11119-024-10183-4","RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}

引用次数: 0

Abstract

Introduction

Satellite-sourced data have become a valuable resource for precision agriculture because they provide crucial insights into various parameters that are essential for effective crop management. An array of practical agricultural tools provides comprehensive data for assessing crop biomass, soil conditions, and plant stress symptoms, predicting yields, and performing other functions. Satellite data, when combined with in situ data from different sources, can significantly enhance biomass and yield estimations.

Material and Methods

The ability of the “PROcesses of radiation, Mass and Energy Transfer” (PROMET) model to predict crop biomass and grain yield and to optimize nitrogen fertilization during the vegetation period was investigated. Field trials were conducted to assess the accuracy and limitations of biomass and yield predictions.

Results and Conclusion

The predicted yields were sufficiently accurate on a whole-field basis, and site-specific values showed strong correlations. In additional field trials with different fertilization strategies, the highest yield and nitrogen efficiency were observed for the PROMET-based strategy. Additional experiments with different crops and greater durations are needed to draw a more reliable conclusion.

Abstract Image

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

评估 PROMET 模型在农场研究中的产量估算和氮肥施用情况

导言卫星数据已成为精准农业的宝贵资源，因为它们提供了对有效作物管理至关重要的各种参数的重要见解。一系列实用的农业工具为评估作物生物量、土壤条件和植物胁迫症状、预测产量以及执行其他功能提供了全面的数据。材料与方法研究了 "辐射、质量和能量传递过程"（PROMET）模型预测作物生物量和谷物产量以及优化植被期氮肥施用的能力。进行了田间试验，以评估生物量和产量预测的准确性和局限性。在采用不同施肥策略的其他田间试验中，基于 PROMET 的策略产量和氮效率最高。要得出更可靠的结论，还需要对不同作物和更长的施肥期进行更多试验。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

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.