Soil sampling and sensed ancillary data requirements for soil mapping in precision agriculture I. delineation of management zones to determine zone averages of soil properties

IF 5.4 2区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY
Ruth Kerry, Ben Ingram, Margaret Oliver, Zoë Frogbrook
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Abstract

Sensed and soil sample data are used in two main approaches for mapping soil properties in precision agriculture: management zones (MZs) and contour maps. This is the first of two papers that explores maps of MZs. Management zones based on variation in sensed data that are related to the more permanent soil properties assume that the zones are multi-purpose. Soil properties are then often sampled on a grid to provide the average values of each property per zone. This paper examines the plausibility of this approach by examining how the number of samples taken on a grid and the application of kriging affect mean soil property values for MZs. The suitability of MZs based on ancillary data for managing several agronomically important properties simultaneously is also considered. These concepts are examined with historic soil data from four field sites in southern UK with different scales of spatial variation. Results showed that when the grid sampling interval is large, there is less difference in the means of properties between MZs, but kriging the soil data increased the differences between zones when the sampling interval was large and sample small. Sensed data are used increasingly to aid the identification of MZs, but these could not be considered multi-purpose at all sites. The MZs produced were most useful for phosphorus (P), pH and volumetric water content (VWC) at the Wallingford site and useful for most properties at the Clays and Y215 sites. For the latter site this was true only when the most dense data were used to calculate MZ averages. The results show that sampling interval for MZ averages should relate to the scale of variation or the size of the MZs at a site. The sampling density could be based on the variogram range of ancillary data. This research suggests that there should be 6–8 samples per zone to obtain accurate averages of soil properties. Nutrient data for more than one year were examined at two sites and showed that patterns remained consistent in the short term unless variable-rate management was used, but also the range of values changed in the short term.

Abstract Image

I. 划定管理区以确定土壤特性的区平均值
在绘制精准农业的土壤特性地图时,有两种主要方法使用了传感数据和土壤样本数据:管理区(MZ)和等高线地图。本文是探讨管理区地图的两篇论文中的第一篇。管理区基于与较永久性土壤特性相关的传感数据变化,假定管理区是多用途的。然后,通常会在网格上对土壤特性进行采样,以提供每个区域中每种特性的平均值。本文通过研究网格取样数量和克里金法的应用如何影响多用途区的平均土壤属性值,来探讨这种方法的合理性。本文还考虑了基于辅助数据的多区是否适合同时管理几个重要的农艺属性。这些概念通过英国南部四个具有不同空间变化尺度的实地土壤历史数据进行了检验。结果表明,当网格采样间隔较大时,不同 MZ 之间的属性平均值差异较小,但当采样间隔较大而样本较小时,克里格法土壤数据会增加不同区域之间的差异。传感数据越来越多地用于帮助确定多区,但不能认为这些数据在所有地点都是多用途的。在沃灵福德站点,所产生的多级分区对磷(P)、pH 值和体积含水量(VWC)最有用,而在粘土和 Y215 站点,则对大多数属性都有用。对于 Y215 采样点,只有在使用最密集的数据计算 MZ 平均值时才会出现这种情况。结果表明,MZ 平均值的取样间隔应与站点的变化规模或 MZ 的大小有关。取样密度可根据辅助数据的变异图范围确定。这项研究表明,要获得准确的土壤特性平均值,每个区域应采集 6-8 个样本。对两个地点一年以上的养分数据进行了研究,结果表明,除非采用变率管理,否则短期内模式保持一致,但短期内数值范围也会发生变化。
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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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