Pub Date : 2025-12-22DOI: 10.1007/s11119-025-10301-w
Leon Weigelt, Matthias Wengert, Michael Wachendorf, Jayan Wijesingha
Accurate and timely forage yield prediction in alfalfa-grass mixtures (AGM) is essential for supporting precision agriculture management decisions. This study aimed to develop and evaluate UAV-borne remote sensing models to predict total dry matter yield (DMY) and legume dry matter yield (LY) across multiple harvests and field sites. UAV-borne high-resolution true-colour images were used to derive canopy height models via structure-from-motion. At the same time, multispectral imagery enabled the calculation of reflectance-based vegetation indices. Biomass was destructively sampled, and DMY and LY were determined through drying and botanical fractioning. A total of 276 biomass samples were collected over four harvests, including samples from three AGM fields. To predict DMY and LY, two machine learning regression models (random forest and extreme gradient boosting) were trained and validated using leave-spatial-temporal-group-out cross-validation to ensure robustness across locations and time. Random forest models using fused spectral and height data achieved the best performance, with median prediction errors of 0.51 t ha⁻¹ for DMY (median R² = 0.49) and 0.40 t ha⁻¹ for LY (median R² = 0.65), demonstrating good generalizability under varying agronomic conditions. The study highlights the potential of combining UAV-borne height and spectral data for high-resolution yield mapping in complex forage systems. Predictive maps of DMY and LY provide spatial insights that can inform management and support sustainable nitrogen cycling in crop rotations.
准确、及时地预测紫花苜蓿-草混合作物的饲料产量是支持精准农业经营决策的重要依据。本研究旨在开发和评估无人机遥感模型，以预测豆科作物在不同收获和不同场点的总干物质产量（DMY）和干物质产量（LY）。利用无人机携带的高分辨率真彩图像，通过运动结构推导出树冠高度模型。同时，多光谱影像可以计算基于反射率的植被指数。对生物量进行破坏性取样，并通过干燥和植物分馏测定DMY和LY。在四次收获中共采集了276个生物量样本，其中包括三个AGM田的样本。为了预测DMY和LY，我们对两个机器学习回归模型（随机森林和极端梯度增强）进行了训练，并使用leave-spatial-temporal-group-out交叉验证进行了验证，以确保跨地点和时间的鲁棒性。使用融合光谱和高度数据的随机森林模型取得了最好的效果，DMY的中位数预测误差为0.51 t ha⁻¹（中位数R²= 0.49），LY的中位数预测误差为0.40 t ha⁻¹（中位数R²= 0.65），在不同的农学条件下表现出良好的泛化性。该研究强调了将无人机运载的高度和光谱数据结合起来，在复杂的饲料系统中进行高分辨率产量测绘的潜力。DMY和LY的预测图提供了空间洞察力，可以为管理提供信息，并支持作物轮作中的可持续氮循环。
{"title":"Spatio-temporal prediction of total and legume dry matter yield using UAV-borne RGB and multispectral images in alfalfa-grass mixtures","authors":"Leon Weigelt, Matthias Wengert, Michael Wachendorf, Jayan Wijesingha","doi":"10.1007/s11119-025-10301-w","DOIUrl":"https://doi.org/10.1007/s11119-025-10301-w","url":null,"abstract":"Accurate and timely forage yield prediction in alfalfa-grass mixtures (AGM) is essential for supporting precision agriculture management decisions. This study aimed to develop and evaluate UAV-borne remote sensing models to predict total dry matter yield (DMY) and legume dry matter yield (LY) across multiple harvests and field sites. UAV-borne high-resolution true-colour images were used to derive canopy height models via structure-from-motion. At the same time, multispectral imagery enabled the calculation of reflectance-based vegetation indices. Biomass was destructively sampled, and DMY and LY were determined through drying and botanical fractioning. A total of 276 biomass samples were collected over four harvests, including samples from three AGM fields. To predict DMY and LY, two machine learning regression models (random forest and extreme gradient boosting) were trained and validated using leave-spatial-temporal-group-out cross-validation to ensure robustness across locations and time. Random forest models using fused spectral and height data achieved the best performance, with median prediction errors of 0.51 t ha⁻¹ for DMY (median R² = 0.49) and 0.40 t ha⁻¹ for LY (median R² = 0.65), demonstrating good generalizability under varying agronomic conditions. The study highlights the potential of combining UAV-borne height and spectral data for high-resolution yield mapping in complex forage systems. Predictive maps of DMY and LY provide spatial insights that can inform management and support sustainable nitrogen cycling in crop rotations.","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"2 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145807702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-10DOI: 10.1007/s11119-025-10295-5
Rodrigo Damasceno, Marcelo José Carrer, Larissa Gui Pagliuca, Marcela de Mello Brandão Vinholis, Hildo Meirelles de Souza Filho
{"title":"What drives the adoption of digital technology? An empirical assessment of multiple technology adoption by soybean farmers in São Paulo, Brazil","authors":"Rodrigo Damasceno, Marcelo José Carrer, Larissa Gui Pagliuca, Marcela de Mello Brandão Vinholis, Hildo Meirelles de Souza Filho","doi":"10.1007/s11119-025-10295-5","DOIUrl":"https://doi.org/10.1007/s11119-025-10295-5","url":null,"abstract":"","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"141 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145711458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-09DOI: 10.1007/s11119-025-10302-9
Anastasiia Safonova, Stefan Stiller, Momchil Yordanov, Masahiro Ryo
Purpose One of the most pervasive Artificial Intelligence (AI) methodologies utilized in the domain of agriculture for image-based classification purposes is Supervised Learning (SL). However, SL depends on a large amount of annotation effort and is susceptible to overfitting to the given prediction task. Self-Supervised Learning (SSL) is a novel training paradigm with the potential to address these issues, while its potential has not been investigated in the agriculture domain. This paper presents the initial experimental investigation and comparison of SL and SSL for the classification of agricultural images in the context of limited samples. Methods We used an agricultural subset of the Land Use and Cover Area Frame Survey (LUCAS) dataset serving as a case study. In total, it comprised 1,000 images for each of the 10 crops: common wheat, barley, oats, maize, potatoes, sugar beet, sunflower, rape and turnip rape, soya, and temporary grassland. For SL, we trained popular and frequently used Convolutional Neural Network (CNN) architectures such as VGG16, Inception, ResNet-18/50, SqueezeNet, ResNeXt-50, MobileNet-V2, ShuffleNet, EfficientNet-V2, and ConvNeXt Tiny with and without data augmentations. For SSL, the best-performing CNN architectures (ResNet-18, ResNet-50, and ResNeXt-50) were further tested. The architectures were pre-trained with the VICReg algorithm (Variance Invariance Covariance Regularization) and fine-tuned successively using supervision for crop type classification. Results Our results demonstrate that the SSL models can distinguish crop types (common wheat, barley, oats, maize, potatoes, sugar beet, sunflower, rape, soya, and grassland) even without labels based solely on morphological features and organize them into three semantically meaningful visual groups: cereal-like and grassland crops, upright broadleaf crops, and low-growing broadleaf crops. The fine-tuned models, particularly ResNeXt-50, achieved superior performance compared to any of the SLs. Notably, we show that the fine-tuned SSL models outperformed the best-performing SL models by using only 5% of the labeled training data for fine-tuning, corresponding to a small and balanced subset of the training split. Conclusion These findings highlight the potential of SSL for improving classification efficiency and generalization under limited data availability conditions in agriculture applications, providing a viable path toward more efficient agricultural monitoring systems.
{"title":"Self-supervised learning outperforms supervised learning for crop classification by annotating only 5% of images","authors":"Anastasiia Safonova, Stefan Stiller, Momchil Yordanov, Masahiro Ryo","doi":"10.1007/s11119-025-10302-9","DOIUrl":"https://doi.org/10.1007/s11119-025-10302-9","url":null,"abstract":"Purpose One of the most pervasive Artificial Intelligence (AI) methodologies utilized in the domain of agriculture for image-based classification purposes is Supervised Learning (SL). However, SL depends on a large amount of annotation effort and is susceptible to overfitting to the given prediction task. Self-Supervised Learning (SSL) is a novel training paradigm with the potential to address these issues, while its potential has not been investigated in the agriculture domain. This paper presents the initial experimental investigation and comparison of SL and SSL for the classification of agricultural images in the context of limited samples. Methods We used an agricultural subset of the Land Use and Cover Area Frame Survey (LUCAS) dataset serving as a case study. In total, it comprised 1,000 images for each of the 10 crops: common wheat, barley, oats, maize, potatoes, sugar beet, sunflower, rape and turnip rape, soya, and temporary grassland. For SL, we trained popular and frequently used Convolutional Neural Network (CNN) architectures such as VGG16, Inception, ResNet-18/50, SqueezeNet, ResNeXt-50, MobileNet-V2, ShuffleNet, EfficientNet-V2, and ConvNeXt Tiny with and without data augmentations. For SSL, the best-performing CNN architectures (ResNet-18, ResNet-50, and ResNeXt-50) were further tested. The architectures were pre-trained with the VICReg algorithm (Variance Invariance Covariance Regularization) and fine-tuned successively using supervision for crop type classification. Results Our results demonstrate that the SSL models can distinguish crop types (common wheat, barley, oats, maize, potatoes, sugar beet, sunflower, rape, soya, and grassland) even without labels based solely on morphological features and organize them into three semantically meaningful visual groups: cereal-like and grassland crops, upright broadleaf crops, and low-growing broadleaf crops. The fine-tuned models, particularly ResNeXt-50, achieved superior performance compared to any of the SLs. Notably, we show that the fine-tuned SSL models outperformed the best-performing SL models by using only 5% of the labeled training data for fine-tuning, corresponding to a small and balanced subset of the training split. Conclusion These findings highlight the potential of SSL for improving classification efficiency and generalization under limited data availability conditions in agriculture applications, providing a viable path toward more efficient agricultural monitoring systems.","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"30 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145703991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-09DOI: 10.1007/s11119-025-10303-8
J. Rakun, G. Popič
Purpose This study introduces an optimized algorithm for autonomous navigation of field robots, aiming to improve navigation accuracy, reduce crop damage and shorten execution times in agricultural environments. Methods The enhanced solution integrates advanced data filtering with sensor fusion techniques, combining LiDAR and IMU inputs to produce precise 3D point cloud representations for reliable navigation in structured crop rows. Both the legacy and improved algorithms were evaluated through simulation and physical trials on the FarmBeast robotic platform. Results The improved algorithm reduced traversal time by up to 33% on certain field sections and lowered crop damage by 25% compared to the previous version. Conclusions Results confirm the robustness and effectiveness of the enhanced navigation system in complex agricultural field conditions, demonstrating its potential for practical deployment within farming automation.
{"title":"Optimized autonomous navigation for field robots: extended results and practical deployment","authors":"J. Rakun, G. Popič","doi":"10.1007/s11119-025-10303-8","DOIUrl":"https://doi.org/10.1007/s11119-025-10303-8","url":null,"abstract":"Purpose This study introduces an optimized algorithm for autonomous navigation of field robots, aiming to improve navigation accuracy, reduce crop damage and shorten execution times in agricultural environments. Methods The enhanced solution integrates advanced data filtering with sensor fusion techniques, combining LiDAR and IMU inputs to produce precise 3D point cloud representations for reliable navigation in structured crop rows. Both the legacy and improved algorithms were evaluated through simulation and physical trials on the FarmBeast robotic platform. Results The improved algorithm reduced traversal time by up to 33% on certain field sections and lowered crop damage by 25% compared to the previous version. Conclusions Results confirm the robustness and effectiveness of the enhanced navigation system in complex agricultural field conditions, demonstrating its potential for practical deployment within farming automation.","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"138 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145703992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-05DOI: 10.1007/s11119-025-10304-7
Vinay Vijayakumar, Antonio de Oliveira Costa Neto, Yiannis Ampatzidis, John Schueller, Won Suk Lee, Tom Burks
{"title":"Design and evaluation of a PI-controlled robotic smart sprayer for precision herbicide applications with multi-nozzle integration","authors":"Vinay Vijayakumar, Antonio de Oliveira Costa Neto, Yiannis Ampatzidis, John Schueller, Won Suk Lee, Tom Burks","doi":"10.1007/s11119-025-10304-7","DOIUrl":"https://doi.org/10.1007/s11119-025-10304-7","url":null,"abstract":"","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"26 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145680380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-02DOI: 10.1007/s11119-025-10300-x
Muhammad Abdul Munnaf, Xun Liao, Paula Sangines, Maria Calera, Angela Guerrero, Abdul Mounem Mouazen
{"title":"Environmental life cycle assessment of precision nitrogen fertilization in multiple field crops","authors":"Muhammad Abdul Munnaf, Xun Liao, Paula Sangines, Maria Calera, Angela Guerrero, Abdul Mounem Mouazen","doi":"10.1007/s11119-025-10300-x","DOIUrl":"https://doi.org/10.1007/s11119-025-10300-x","url":null,"abstract":"","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"116 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145657695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-16DOI: 10.1007/s11119-025-10298-2
Daniele Pinna, Elena Basso, Cristina Pornaro, Reddy Pullanagari, Stefano Macolino, Andrea Pezzuolo, Francesco Marinello
Context Accurate and regular estimation of above-ground biomass (AGB) in grassland ecosystems is essential for sustainable grazing management, feed planning, and carbon accounting. However, AGB mapping in heterogeneous grasslands remains challenging due to the spatial and temporal variability of vegetation and management practices. Aims This study explores the potential of Gaussian Process Regression (GPR) models combined with multispectral imagery from Sentinel-2 and PlanetScope to predict AGB across different grassland systems in Northern Italy. Methods Extensive field measurements (n = 954) were collected over 18 months across meadows, lowland pastures, and alpine grasslands, covering a range of altitudes, management regimes, and canopy structures. Spectral predictors from Sentinel-2 and PlanetScope were used to train independent GPR models and evaluate their predictive performance at both pixel and field scales. Key Results At the pixel level, GPR models achieved R 2 = 0.520 (Sentinel-2) and R 2 = 0.514 (PlanetScope) with mean absolute errors (MAE) of ~400 kg DM ha −1 , consistent with the high heterogeneity of grassland canopies. Aggregating predictions at the field scale markedly improved accuracy (R 2 = 0.972 and 0.968; MAE = 60–120 kg DM ha −1 , ≤10% relative error). These results are comparable to those of commercial pasture monitoring platforms. Conclusion The integration of high-resolution multispectral imagery and non-parametric GPR modeling allows robust AGB estimation in heterogeneous grasslands, reducing uncertainty through field-scale aggregation. Implications and Impacts This research provides a scalable and transferable framework for operational biomass monitoring, offering a practical tool for digital decision support systems (DSS) and a scientific basis for integration into carbon Measurement, Reporting, and Verification (MRV) protocols. The novelty of the study lies in demonstrating the combined use of Sentinel-2 and PlanetScope data within a unified GPR framework for multi-site grassland systems, validated through extensive field observations.
准确、规律地估算草地生态系统的地上生物量（AGB）对可持续放牧管理、饲料规划和碳核算至关重要。然而，由于植被和管理实践的时空变化，异质草原的AGB制图仍然具有挑战性。本研究探讨了高斯过程回归（GPR）模型结合Sentinel-2和PlanetScope的多光谱图像预测意大利北部不同草原系统AGB的潜力。方法在18个月的时间里，对草甸、低地牧场和高寒草原进行了广泛的野外测量（n = 954），涵盖了一系列海拔、管理制度和冠层结构。来自Sentinel-2和PlanetScope的光谱预测器用于训练独立的GPR模型，并在像素和场尺度上评估其预测性能。在像元水平上，GPR模型（Sentinel-2）和（PlanetScope）的平均绝对误差（MAE）分别为0.520和0.514，平均绝对误差为~400 kg DM ha - 1，与草地冠层的高度异质性相一致。在田间尺度上聚合预测显著提高了预测精度（r2 = 0.972和0.968;MAE = 60-120 kg DM ha - 1，相对误差≤10%）。这些结果与商业牧场监测平台的结果相当。结论高分辨率多光谱图像与非参数GPR模型的集成可以在非均匀草原上进行稳健的AGB估计，通过场尺度聚集减少不确定性。本研究为可操作的生物质监测提供了一个可扩展和可转移的框架，为数字决策支持系统（DSS）提供了实用工具，并为整合到碳测量、报告和验证（MRV）协议中提供了科学基础。该研究的新颖之处在于展示了在统一的GPR框架内结合使用Sentinel-2和PlanetScope数据用于多站点草地系统，并通过广泛的实地观测进行了验证。
{"title":"Optimising grassland Above-Ground biomass Estimation for managed grasslands: A Gaussian process regression approach for Sentinel-2 and Planet Scope in Northern Italy","authors":"Daniele Pinna, Elena Basso, Cristina Pornaro, Reddy Pullanagari, Stefano Macolino, Andrea Pezzuolo, Francesco Marinello","doi":"10.1007/s11119-025-10298-2","DOIUrl":"https://doi.org/10.1007/s11119-025-10298-2","url":null,"abstract":"Context Accurate and regular estimation of above-ground biomass (AGB) in grassland ecosystems is essential for sustainable grazing management, feed planning, and carbon accounting. However, AGB mapping in heterogeneous grasslands remains challenging due to the spatial and temporal variability of vegetation and management practices. Aims This study explores the potential of Gaussian Process Regression (GPR) models combined with multispectral imagery from Sentinel-2 and PlanetScope to predict AGB across different grassland systems in Northern Italy. Methods Extensive field measurements (n = 954) were collected over 18 months across meadows, lowland pastures, and alpine grasslands, covering a range of altitudes, management regimes, and canopy structures. Spectral predictors from Sentinel-2 and PlanetScope were used to train independent GPR models and evaluate their predictive performance at both pixel and field scales. Key Results At the pixel level, GPR models achieved R <jats:sup>2</jats:sup> = 0.520 (Sentinel-2) and R <jats:sup>2</jats:sup> = 0.514 (PlanetScope) with mean absolute errors (MAE) of ~400 kg DM ha <jats:sup>−1</jats:sup> , consistent with the high heterogeneity of grassland canopies. Aggregating predictions at the field scale markedly improved accuracy (R <jats:sup>2</jats:sup> = 0.972 and 0.968; MAE = 60–120 kg DM ha <jats:sup>−1</jats:sup> , ≤10% relative error). These results are comparable to those of commercial pasture monitoring platforms. Conclusion The integration of high-resolution multispectral imagery and non-parametric GPR modeling allows robust AGB estimation in heterogeneous grasslands, reducing uncertainty through field-scale aggregation. Implications and Impacts This research provides a scalable and transferable framework for operational biomass monitoring, offering a practical tool for digital decision support systems (DSS) and a scientific basis for integration into carbon Measurement, Reporting, and Verification (MRV) protocols. The novelty of the study lies in demonstrating the combined use of Sentinel-2 and PlanetScope data within a unified GPR framework for multi-site grassland systems, validated through extensive field observations.","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"3 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145531529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-16DOI: 10.1007/s11119-025-10292-8
Kabindra Adhikari, Douglas R. Smith, Chad Hajda
Assessment of spatial variability of soil health indicators (SHI) from the root zone, not just the topsoil, is crucial for precise farm management decisions. We predicted the spatial distribution of soil organic carbon (SOC), inorganic carbon (SIC), total nitrogen (total-N), nitrate nitrogen (NO 3 -N), C: N ratio, phosphorus (PO 4 ), soil pH, and soil moisture (SM) from the root zone using soil samples from 0–15, 15–30, 30–60, 60–90 cm depths, apparent soil electrical conductivity (EC a ), topography, and a random forest (RF) model. The SHI and corn yield relationship was modeled and mapped, and the field was divided into soil health zones (SHZ) which were assessed for their agronomic significance. The RF model performed very well in predicting SM, pH, and SIC (R 2 up to 0.81), whereas PO 4 and total-N were weakly predicted (R 2 < 0.20) based on 30% test data. The EC a and terrain attributes (mrvbf, normht, sagawi, and fdem) were the most important predictors of SHI. The RF model was robust in quantifying the relationship between SHI and corn yield (R 2 = 0.64; RMSE = 0.80 Mt/ha) where SM appeared as the main predictor of yield variations followed by SIC, NO 3 -N, and pH. The field was divided into four SHZs, and yield responses from these zones were different. Results from this study can be useful for farm management decisions such as in soil health monitoring and variable-rate fertilization, and as a reference to future soil health and precision agriculture research.
{"title":"Digital mapping of selected soil health indicators from the root zone and their relationship with rainfed corn yield in Texas vertisols","authors":"Kabindra Adhikari, Douglas R. Smith, Chad Hajda","doi":"10.1007/s11119-025-10292-8","DOIUrl":"https://doi.org/10.1007/s11119-025-10292-8","url":null,"abstract":"Assessment of spatial variability of soil health indicators (SHI) from the root zone, not just the topsoil, is crucial for precise farm management decisions. We predicted the spatial distribution of soil organic carbon (SOC), inorganic carbon (SIC), total nitrogen (total-N), nitrate nitrogen (NO <jats:sub>3</jats:sub> -N), C: N ratio, phosphorus (PO <jats:sub>4</jats:sub> ), soil pH, and soil moisture (SM) from the root zone using soil samples from 0–15, 15–30, 30–60, 60–90 cm depths, apparent soil electrical conductivity (EC <jats:sub>a</jats:sub> ), topography, and a random forest (RF) model. The SHI and corn yield relationship was modeled and mapped, and the field was divided into soil health zones (SHZ) which were assessed for their agronomic significance. The RF model performed very well in predicting SM, pH, and SIC (R <jats:sup>2</jats:sup> up to 0.81), whereas PO <jats:sub>4</jats:sub> and total-N were weakly predicted (R <jats:sup>2</jats:sup> < 0.20) based on 30% test data. The EC <jats:sub>a</jats:sub> and terrain attributes (mrvbf, normht, sagawi, and fdem) were the most important predictors of SHI. The RF model was robust in quantifying the relationship between SHI and corn yield (R <jats:sup>2</jats:sup> = 0.64; RMSE = 0.80 Mt/ha) where SM appeared as the main predictor of yield variations followed by SIC, NO <jats:sub>3</jats:sub> -N, and pH. The field was divided into four SHZs, and yield responses from these zones were different. Results from this study can be useful for farm management decisions such as in soil health monitoring and variable-rate fertilization, and as a reference to future soil health and precision agriculture research.","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"154 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145532121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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