变速土壤残留除草剂施用的管理区划分

IF 5.4 2区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY Precision Agriculture Pub Date : 2024-03-11 DOI:10.1007/s11119-024-10130-3
Rose V Vagedes, Jason P Ackerson, William G Johnson, Bryan G Young
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引用次数: 0

摘要

人们建议使用土壤残留除草剂以及其他杂草管理策略多样化的做法,以改善杂草管理并阻止除草剂抗药性的发展。虽然土壤特性会影响这些除草剂的推荐施用量,但通常的做法是在土壤特性各异的田块中施用统一剂量的土壤残留除草剂。绘制田地土壤特性图以确定土壤残留除草剂的最佳剂量,可以提高这些除草剂的效率和效果,并改善环境管理。这项研究的目的是利用多种土壤数据源和土壤采样强度,开发并量化管理区分类的准确性,以适用于不同剂量的残留除草剂施用。地图由以下土壤数据绘制:(i) 土壤调查地理数据库 (SSURGO),(ii) 土壤样本 (SS),(iii) 根据土壤电导率 (EC) 测量值回归的土壤样本 (SSEC),(iv) 含有 SmartFirmer® (SF) 传感器提供的有机物 (OM) 数据的土壤样本 (SSSF),以及 (v) 根据 SmartFirmer® 传感器提供的 EC 测量值和 OM 数据回归的土壤样本 (SSECSF)。在印第安纳州的 10 块商业田地上使用了修改后的蒙特卡洛交叉验证法,生成了 3.6 万张地图,涵盖了所有空间土壤数据源、采样密度和三种代表性除草剂(吡蚜酮、甲草胺和灭草松)。与使用 SS 数据绘制的地图相比,使用 SSEC 数据绘制的地图管理区分类准确性最高。不过,在所有田块、除草剂和采样强度下绘制的地图中,SS 和 SSEC 地图同时具有 34% 的最高管理区划分准确率。与每 2 或 4 公顷采集一个土壤样本相比,每公顷采集一个土壤样本是生成除草剂施用管理区最可靠的采样强度。总之,土壤取样和 ECa 数据应用于确定变速(VR)残留除草剂施用管理区。
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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Management zone classification for variable-rate soil residual herbicide applications

The use of soil residual herbicides, along with other practices that diversify weed management strategies, have been recommended to improve weed management and deter the progression of herbicide resistance. Although soil characteristics influence recommended application rates for these herbicides, the common practice is to apply a uniform dose of soil residual herbicides across fields with variable soil characteristics. Mapping fields for soil characteristics that dictate the optimal dose of soil residual herbicides could improve the efficiency and effectiveness of these herbicides, as well as improve environmental stewardship. The objectives of this research were to develop and quantify the accuracy of management zone classifications for variable-rate residual herbicide applications using multiple soil data sources and soil sampling intensities. The maps were created from soil data that included (i) Soil Survey Geographic database (SSURGO), (ii) soil samples (SS), (iii) soil samples regressed onto soil electrical conductivity (EC) measurements (SSEC), (iv) soil samples with organic matter (OM) data from SmartFirmer® (SF) sensors (SSSF), and (v) soil samples regressed onto EC measurements plus OM data from SmartFirmer® sensor (SSECSF). A modified Monte Carlo cross validation method was used on ten commercial Indiana fields to generate 36,000 maps across all sources of spatial soil data, sampling density, and three representative herbicides (pyroxasulfone, s-metolachlor, and metribuzin). Maps developed from SSEC data were most frequently ranked with the highest management zone classification accuracy compared to maps developed from SS data. However, SS and SSEC maps concurrently had the highest management zone classification accuracy of 34% among maps developed across all fields, herbicides, and sampling intensities. One soil sample per hectare was the most reliable sampling intensity to generate herbicide application management zones compared to one soil sample for every 2 or 4 hectares. In conclusion, soil sampling with ECa data should be used for defining the management zones for variable-rate (VR) residual herbicide applications.

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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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