Spatial and temporal correlation between soil and rice relative yield in small-scale paddy fields and management zones

IF 5.4 2区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Precision Agriculture Pub Date : 2024-11-27 DOI:10.1007/s11119-024-10199-w

Zhihao Zhang, Jiaoyang He, Yanxi Zhao, Zhaopeng Fu, Weikang Wang, Jiayi Zhang, Xiaojun Liu, Qiang Cao, Yan Zhu, Weixing Cao, Yongchao Tian

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引用次数: 0

Abstract

Investigating soil properties and yield variability in farming systems is crucial for delineating Management Zones (MZs). The objectives of study were to investigate the spatiotemporal variability of soil properties, identify spatial and temporal yield-limiting factors of soil and delineate MZs based on these factors. This study was conducted at the Xinghua Rice Smart Farm (33.08°E, 119.98°N) in Jiangsu Province, China, and the experiment covered five consecutive years of soil and rice yield testing from 2017 to 2021, with 933 geo-referenced soil samples and 140 rice yield samples collected annually. Soil samples were analyzed for pH, soil organic matter (SOM), total nitrogen (TN), available phosphorus (AP), available potassium (AK), and apparent soil conductivity (ECa). Spatial and temporal variability of soil properties and RY were analyzed using statistical and geostatistical methods. Ordinary Kriging (OK) interpolation characterized these distributions, and the random forest (RF) algorithm identified key yield-limiting factors. Subsequently, the effectiveness of using all variables to delineate the MZ was compared against the approach of defining MZs based solely on the identified yield-limiting factors. The study also compared Fuzzy C Means (FCM) and Spatial Fuzzy C-Means (sFCM) clustering to evaluate MZs and their temporal stability. Results showed that the coefficients of variation for soil properties ranged from low to medium (7.7-77.4%), with semi-variational function analyses showing moderate to high spatial dependence for most properties. Temporally, soil nutrients and ECa exhibited a slow increase, whereas pH decreased, showing the highest temporal stability for pH and the lowest for AP. RF analysis identified SOM, TN, and ECa as primary influencers of spatial variability of RY, and SOM, pH, and TN as main contributors to its temporal variability. The integration of yield-limiting factors with the sFCM method improves performance of MZ delineation, maintaining stability over the five-year period.

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小规模稻田和管理区土壤与水稻相对产量之间的时空相关性

调查耕作系统中的土壤特性和产量变化对于划分管理区（MZ）至关重要。本研究的目的是调查土壤特性的时空变异性，确定土壤的时空产量限制因子，并根据这些因子划分管理区。本研究在中国江苏省兴化水稻智慧农场（33.08°E，119.98°N）进行，试验涵盖 2017 年至 2021 年连续五年的土壤和水稻产量测试，每年采集 933 个地理参照土壤样品和 140 个水稻产量样品。土壤样品分析了 pH 值、土壤有机质（SOM）、全氮（TN）、可利用磷（AP）、可利用钾（AK）和表观土壤电导率（ECa）。采用统计和地质统计方法分析了土壤特性和 RY 的时空变异性。普通克里金（OK）插值法描述了这些分布特征，随机森林（RF）算法确定了关键的产量限制因素。随后，比较了使用所有变量划定 MZ 与仅根据已确定的产量限制因素划定 MZ 的有效性。研究还比较了模糊 C-均值（FCM）和空间模糊 C-均值（sFCM）聚类法，以评估 MZ 及其时间稳定性。结果表明，土壤特性的变异系数从低到中（7.7%-77.4%）不等，半变异函数分析表明大多数特性具有中度到高度的空间依赖性。从时间上看，土壤养分和 ECa 呈缓慢上升趋势，而 pH 值下降，pH 值的时间稳定性最高，而 AP 值的时间稳定性最低。射频分析表明，SOM、TN 和 ECa 是 RY 空间变化的主要影响因素，而 SOM、pH 和 TN 则是 RY 时间变化的主要因素。将产量限制因子与 sFCM 方法相结合可提高 MZ 划分的性能，并在五年期间保持稳定。

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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.