Precision Agriculture最新文献

{"title":"Detection of fusarium wilt-induced physiological impairment in strawberry plants using hyperspectral imaging and machine learning","authors":"P. Castro-Valdecantos, G. Egea, C. Borrero, M. Pérez-Ruiz, M. Avilés","doi":"10.1007/s11119-024-10173-6","DOIUrl":"https://doi.org/10.1007/s11119-024-10173-6","url":null,"abstract":"<p>Strawberry (<i>Fragraria x ananassa</i>) is a crop affected by various soil-borne fungal pathogens with mostly non-specific foliar symptoms and often requiring laboratory isolation for correct diagnosis. Moreover, these nonspecific foliar symptoms, appreciated by the human eye, appear after some time following infection by the pathogen. Early detection of plant diseases is one of the primary objectives in agriculture because it may contribute to identifying more tolerant cultivars in breeding programs and optimise pesticide use in agricultural production with earlier applications in emerging disease foci. New technologies, such as remote sensing and machine learning (ML) algorithms, have arisen as potential tools to improve the ability to detect and classify different crop diseases. The combined use of hyperspectral imagery and ML algorithms were investigated to detect and classify the physiological stress caused by early infections of Fusarium wilt in strawberry plants. Six ML models, namely artificial neural network, decision tree, K-nearest neighbour, support vector machine, multinomial logistic regression and Naïve Bayes were developed to estimate physiological stress associated with Fusarium wilt disease. The results showed that stomatal conductance (g_s) and photosynthesis (<i>A</i>) declined even without visual symptoms of the disease. Among the six ML models evaluated, the artificial neural network model showed the highest classification performance with an overall accuracy of 81%, regardless of the physiological parameter utilized for model training. Moreover, the artificial neural network accurately predicted the absolute values of both physiological parameters (g_s and A) based on the complete spectral signature from visually healthy foliar tissue, achieving coefficients of determination of 84% and 81%, respectively. Consequently, ML models utilizing physiological response data and hyperspectral imaging exhibited remarkable robustness, facilitating the estimation of Fusarium wilt severity in strawberry plants even without visual symptoms.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"55 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141755356","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}

{"title":"Farmers’ willingness to adopt precision agricultural technologies to reduce mycotoxin contamination in grain: evidence from grain farmers in Spain and Lithuania","authors":"Enoch Owusu-Sekyere, Assem Abu Hatab, Carl-Johan Lagerkvist, Manuel Pérez-Ruiz, Egidijus Šarauskis, Zita Kriaučiūnienė, Muhammad Baraa Almoujahed, Orly Enrique Apolo-Apolo, Abdul Mounem Mouazen","doi":"10.1007/s11119-024-10167-4","DOIUrl":"https://doi.org/10.1007/s11119-024-10167-4","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>This study examines the willingness of Spanish and Lithuanian grain farmers to adopt a combined approach of preventive site-specific spraying (PSSS) and selective harvesting (SH), two precision agricultural technologies (below referred to as PSSS-SH) aimed at mitigating the risk of mycotoxin contamination in barley and wheat.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>Data were collected from 190 commercial grain farmers using a choice experimental survey. The empirical analysis relied on the estimation of mixed logit and integrated latent class models.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>The surveyed farmers were heterogeneous in their preference for the PSSS-SH technology, with a majority (81%) reporting that they were willing to adopt and pay for the PSSS-SH technology. Furthermore, the farmers’ willingness to adopt PSSS-SH technology was influenced by the trade-offs between the potential production, economic and environmental changes.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>Profit maximization is not the only motivation for a farmer’s decision to adopt PSSS-SH, there are also important non-financial benefits that align with the observed choices. Furthermore, the perceived usefulness of the technology, the willingness and readiness to use the technology, and the farmer characteristics (e.g. cooperative membership, employment status, share of household income from grain production and past experience with precision farming technology) were positively associated with uptake of the PSSS-SH technology. Therefore, extension programmes should have a special focus on the perceived usefulness of the technology, the willingness and readiness of farmers to use it, and its unique characteristics.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"31 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141755352","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}

{"title":"Assessment of spray patterns and efficiency of an unmanned sprayer used in planar growing systems","authors":"Chenchen Kang, Long He, Heping Zhu","doi":"10.1007/s11119-024-10166-5","DOIUrl":"https://doi.org/10.1007/s11119-024-10166-5","url":null,"abstract":"<p>Automated technologies in precision agriculture enable unmanned systems to precisely target areas with chemicals through controlled nozzle movements. Quantitative assessment of these sprayers can enhance spraying strategies, catering to different canopy sizes, row spacing and coverage objectives. This research assessed an unmanned sprayer equipped with pan-tilt nozzles for targeted area control and spray coverage adjustment. The spray cloud path on the canopy, as the nozzles moved vertically and the sprayer advanced, was simulated mathematically. A model was developed to determine the swing angle based on orchard/vineyard geometrical parameters. This model was then applied in field tests in a vineyard and an apple orchard. Various nozzle-heading angles, driving speeds, and flow rates were experimented with, using average coverage and droplet density as the evaluation criterion. The findings showed that the developed model offered an effective method for determining the swing angles. Lowering driving speeds and increasing flow rates were found to notably enhance coverage. A 45º nozzle-heading angle proved more effective in vineyards, whereas a 90º angle yielded better results in apple orchards, reflecting the variations in canopy size and row spacing. The unmanned sprayer demonstrated great potential for autonomous spraying in vineyards and orchards.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"73 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141618339","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}

{"title":"Field validation of NDVI to identify crop phenological signatures","authors":"Muhammad Tousif Bhatti, Hammad Gilani, Muhammad Ashraf, Muhammad Shahid Iqbal, Sarfraz Munir","doi":"10.1007/s11119-024-10165-6","DOIUrl":"https://doi.org/10.1007/s11119-024-10165-6","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose and Methods</h3><p>Crop identification using remotely sensed imagery provides useful information to make management decisions about land use and crop health. This research used phonecams to acquire the Normalized Difference Vegetation Index (NDVI) of various crops for three crop seasons. NDVI time series from Sentinel (L121-L192) images was also acquired using Google Earth Engine (GEE) for the same period. The resolution of satellite data is low therefore gap filling and smoothening filters were applied to the time series data. The comparison of data from satellite images and phenocam provides useful insight into crop phenology. The results show that NDVI is generally underestimated when compared to phenocam data. The Savitzky-Golay (SG) and some other gap filling and smoothening methods are applied to NDVI time series based on satellite images. The smoothened NDVI curves are statistically compared with daily NDVI series based on phenocam images as a reference.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>The SG method has performed better than other methods like moving average. Furthermore, polynomial order has been found to be the most sensitive parameter in applying SG filter in GEE. Sentinel (L121-L192) image was used to identify wheat during the year 2022–2023 in Sargodha district where experimental fields were located. The Random Forest Machine Leaning algorithm was used in GEE as a classifier.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>The classification accuracy has been found 97% using this algorithm which suggests its usefulness in applying to other areas with similar agro-climatic characteristics.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"68 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141597668","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}

{"title":"Mechanized wet direct seeding for increased rice production efficiency and reduced carbon footprint","authors":"Nguyen Van Hung, Tran Ngoc Thach, Nguyen Ngoc Hoang, Nguyen Cao Quan Binh, Dang Minh Tâm, Tran Tan Hau, Duong Thi Tu Anh, Trinh Quang Khuong, Vo Thi Bich Chi, Truong Thi Kieu Lien, Martin Gummert, Tovohery Rakotoson, Kazuki Saito, Virender Kumar","doi":"10.1007/s11119-024-10163-8","DOIUrl":"https://doi.org/10.1007/s11119-024-10163-8","url":null,"abstract":"<p>Crop establishment is one of the major rice production operations that strongly affects rice production, productivity, and environmental impacts. This research introduced a new technology and provided scientific evidence for the benefits of mechanized wet direct seeding (mDSR) of rice as compared with the other crop establishment practices commonly applied by farmers for wet direct seeded rice in Mekong River Delta in Vietnam, such as seeding in line using drum-seeder (dDSR) and broadcast seeding (bDSR). The experiment was implemented across two consecutive rice cropping seasons that are Winter-Spring season and Summer-Autumn season in 2020–2021. Treatments included (1–3) mDSR with seeding rates of 30, 50, and 70 kg ha^− 1, (4) dDSR with 80 kg ha^− 1 seed rate, and (5) bDSR as current farmer practice with seeding rate of 180 kg ha^− 1. The fertilizer application was adjusted as per seeding rate with 80:40:30 kg ha^− 1 N: P₂O₅: K₂O with lower seed rate 30 and 50 kg ha^− 1 in mDSR; 90:40:30 kg ha^− 1 N: P₂O₅: K₂O with medium seed rate of 70 to 80 kg ha^− 1; and 115:55:40 kg ha^− 1 N: P₂O₅: K₂O with high seed rate of 180 kg ha^− 1 in bDSR. Mechanized wet direct seeding rice with a lower seed rate of 30 to 70 kg ha^− 1 and fertilizer rate by 22–30% reduced variation in seedling density by 40–80% and in yield by 0.1 to 0.3 t ha^− 1 and had similar yield to bDSR. In consequence, N productivity was 27 and 32% higher in mDSR as compared to bDSR during the Winter-Spring season and Summer-Autumn seasons, respectively. The use of lower seed rate and fertilizer in mDSR also led to higher income and lower carbon footprint (GHGe per kg of paddy grains) of rice production than the currently used practices of bDSR. Net income of mDSR was comparable to that of dDSR and higher by 145–220 and 171–248 $US than that of bDSR in Winter-Spring season and Summer-Autumn, respectively. The carbon footprint of mDSR rice production compared to bDSR was lower by 22–25% and 12–20% during the Winter-Spring and Summer-Autumn seasons, respectively. Given the above benefits of farming efficiency, higher income, and low emission, mDSR would be a technology package that strongly supports sustainable rice cultivation transformation for the Mekong River Delta of Vietnam.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"56 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141597655","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}

{"title":"On-farm cereal rye biomass estimation using machine learning on images from an unmanned aerial system","authors":"Kushal KC, Matthew Romanko, Andrew Perrault, Sami Khanal","doi":"10.1007/s11119-024-10162-9","DOIUrl":"https://doi.org/10.1007/s11119-024-10162-9","url":null,"abstract":"<p>This study assesses the potential of using multispectral images collected by an unmanned aerial system (UAS) on machine learning (ML) frameworks to estimate cereal rye (<i>Secale cereal</i> L.) biomass. Multispectral images and ground-truth cereal rye biomass data were collected from 15 farmers’ fields up to three times between March and May in northwest Ohio. Images were processed to derive 13 vegetation indices (VIs). Out of 13 VIs, six optimal sets of VIs, including excess green (ExG), normalized green red difference index (NGRDI), soil adjusted vegetation index (SAVI), blue green ratio (B_G_ratio), red-edge triangular vegetation index (RTVI), and normalized difference red-edge (NDRE) were selected using the variance inflation factor (VIF) based feature selection approach. Six regression models including a multiple linear regression (MLR), elastic net (ENET), multivariate adaptive regression splines (MARS), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGB) were investigated for estimation of cereal rye biomass based on the VIs. For most of the models, the six selected VIs performed better than or similar to the full set of 13 VIs with R² ranging from 0.24 to 0.59 and RMSE ranging from 83.13 to 91.89 g/m² during 10-fold cross-validation. During independent accuracy assessment with the selected set of VIs, XGB exhibited the highest R² (0.67) and lowest RMSE (83.13 g/m²) and MAE (48.13 g/m²) followed by RF and ENET. For all the models, the agreement between observed and predicted biomass was high for biomass less than or equal to 200 g/m² but decreased for biomass greater than 200 g/m². When field-collected structural features were integrated with the selected VIs, the models showed improved performance, with R² and RMSE of the models reaching up to 0.82 and 61.67 g/m² respectively. Among the six VIs, SAVI showed the strongest impact on the model prediction for the best-performing RF and XGB regression models. The findings of this study demonstrate the potential of precisely estimating and mapping cereal rye biomass based on UAS-captured multispectral images. Timely information on cover crop growth can facilitate numerous decision-making processes, including planning the planting operations, and management of nutrients, weeds, and soil moisture to improve agronomic and environmental outcomes.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"28 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141553310","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}

{"title":"Downscaling crop production data to fine scale estimates with geostatistics and remote sensing: a case study in mapping cotton fibre quality","authors":"M. J. Tilse, P. Filippi, B. Whelan, T. F. A. Bishop","doi":"10.1007/s11119-024-10161-w","DOIUrl":"https://doi.org/10.1007/s11119-024-10161-w","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>A generalised approach to downscale areal observations of crop production data is illustrated using cotton yield and fibre quality (length and micronaire) data which is measured as a module (areal/block) average.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>Two features of the downscaling algorithm are; (i) to estimate spatial trends in yield and quality using regression with fine resolution predictors such as remote sensing imagery, and (ii) use area-to-point kriging (A2PK) to downscale either the observations in the absence of a useful spatial trend model or the residuals from the trend model (if useful) from areal averages.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>Correlations with remote sensing covariates were stronger for cotton fibre yield than for cotton fibre micronaire, and much stronger compared to those for cotton fibre length. Spatial trends in cotton fibre yield and micronaire could be estimated with good model quality using regression with remote sensing covariates with or without A2PK in almost all fields. Conversely, model quality was poorer for cotton fibre length and there was only a small difference in model performance between the null and trend models. When the downscaling approach was tested using fine-resolution yield observations, model performance was poorer at a fine-resolution compared to the module-resolution, which was to be expected.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>This approach enables the creation of high-resolution raster maps of variables of interest with a much finer spatial resolution compared to the areal observations, and can be applied for any areal averaged crop production data in a range of broadacre and horticultural industries (e.g. sugarcane, apples, citrus). The finer spatial resolution may allow growers or agronomists to better understand the drivers of variability within fields, assess management implications, and create management plans at a higher resolution.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"25 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141553464","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}

{"title":"Fertilization and soil management machine learning based sustainable agronomic prescriptions for durum wheat in Italy","authors":"Marco Fiorentini, Calogero Schillaci, Michele Denora, Stefano Zenobi, Paola A. Deligios, Rodolfo Santilocchi, Michele Perniola, Luigi Ledda, Roberto Orsini","doi":"10.1007/s11119-024-10153-w","DOIUrl":"https://doi.org/10.1007/s11119-024-10153-w","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>This research aims to develop a meta-machine learning model to optimize soil and nitrogen management for durum wheat in Italy. It addresses the challenges of increased food production on limited land amidst rising input costs, geopolitical changes, and climate change. The goal is to aid decision-makers in achieving maximum crop yield and income margins through effective agronomic strategies.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>The study developed a meta-machine learning model, integrating classification and regression models, and tested it at four sites in Marche and Basilicata, Italy, over several years. The model incorporated data from remote sensing, crop phenology, soil chemical properties, weather data, soil management, and nitrogen levels. A Random Forest model was used to classify crop phenology, while a Neural Network model predicted yield. Eleven nitrogen levels were compared across these sites.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>The Random Forest model achieved an accuracy of 0.98, kappa of 0.96, and recall of 0.98 for predicting crop phenology. The Neural Network model for yield prediction had an R squared of 0.90 and a Root Mean Square Error of 0.59 t ha-1. Key factors identified for model accuracy were temperature, precipitation, NDVI, and nitrogen input. Simulations of 30 soil management and fertilization combinations revealed that no-tillage management increased grain yield. The Marginal Fertilizer Yield Index determined optimal nitrogen application.</p><h3 data-test=\"abstract-sub-heading\">Conclusions</h3><p>The meta-machine learning model accurately predicted durum wheat yield and identified effective agronomic strategies, demonstrating the potential for broader application in field conditions. The model offers a promising approach to sustainable agriculture and climate change mitigation by utilising publicly available spatial datasets.</p><h3 data-test=\"abstract-sub-heading\">Graphical abstract</h3>\u0000","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"25 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141553309","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}

{"title":"Integrative approaches in modern agriculture: IoT, ML and AI for disease forecasting amidst climate change","authors":"Payam Delfani, Vishnukiran Thuraga, Bikram Banerjee, Aakash Chawade","doi":"10.1007/s11119-024-10164-7","DOIUrl":"https://doi.org/10.1007/s11119-024-10164-7","url":null,"abstract":"<p>Plant disease forecasting models, driven by concurrent data and advanced technologies, are reliable tools for accurate prediction of disease outbreaks in achieving sustainable and productive agricultural systems. Optimal integration of Internet of Things (IoTs), machine learning (ML) techniques and artificial intelligence (AI), further augment the capabilities of these models in empowering farmers with proactive disease control measures towards modern agriculture manifested by efficient resource management, reduced diseases and higher crop yields. This article summarizes the role of disease forecasting models in crop management, emphasizing the advancements and applications of AI and ML in disease prediction, challenges and future directions in the field via (a) The technological foundations and need for validation testing of models, (b) The advancements in disease forecasting with the importance of high-quality publicly available data and (c) The challenges and future directions for the development of transparent and interpretable open-source AI models. Further improvement of these models needs investment in continuous innovative research with collaboration and data sharing among agricultural stakeholders.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"59 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141462655","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}

{"title":"Spaceborne imaging spectroscopy enables carbon trait estimation in cover crop and cash crop residues","authors":"Jyoti S. Jennewein, W. Hively, Brian T. Lamb, Craig S. T. Daughtry, Resham Thapa, Alison Thieme, Chris Reberg-Horton, Steven Mirsky","doi":"10.1007/s11119-024-10159-4","DOIUrl":"https://doi.org/10.1007/s11119-024-10159-4","url":null,"abstract":"&lt;h3 data-test=\"abstract-sub-heading\"&gt;Purpose&lt;/h3&gt;&lt;p&gt;Cover crops and reduced tillage are two key climate smart agricultural practices that can provide agroecosystem services including improved soil health, increased soil carbon sequestration, and reduced fertilizer needs. Crop residue carbon traits (i.e., lignin, holocellulose, non-structural carbohydrates) and nitrogen concentrations largely mediate decomposition rates and amount of plant-available nitrogen accessible to cash crops and determine soil carbon residence time. Non-destructive approaches to quantify these important traits are possible using spectroscopy.&lt;/p&gt;&lt;h3 data-test=\"abstract-sub-heading\"&gt;Methods&lt;/h3&gt;&lt;p&gt;he objective of this study was to evaluate the efficacy of spectroscopy instruments to quantify crop residue biochemical traits in cover crop agriculture systems using partial least squares regression models and a combination of (1) the band equivalent reflectance (BER) of the &lt;i&gt;PRecursore IperSpettrale della Missione Applicativa&lt;/i&gt; (PRISMA) imaging spectroscopy sensor derived from laboratory collected Analytical Spectral Devices (ASD) spectra (&lt;i&gt;n&lt;/i&gt; = 296) of 11 cover crop species and three cash crop species, and (2) spaceborne PRISMA imagery that coincided with destructive crop residue collections in the spring of 2022 (&lt;i&gt;n&lt;/i&gt; = 65). Spectral range was constrained to 1200 to 2400 nm to reduce the likelihood of confounding relationships in wavelengths sensitive to plant pigments or those related to canopy structure for both analytical approaches.&lt;/p&gt;&lt;h3 data-test=\"abstract-sub-heading\"&gt;Results&lt;/h3&gt;&lt;p&gt;Models using laboratory BER of PRISMA all demonstrated high accuracies and low errors for estimation of nitrogen and carbon traits (adj. &lt;i&gt;R&lt;/i&gt;&lt;sup&gt;&lt;i&gt;2&lt;/i&gt;&lt;/sup&gt; = 0.86 − 0.98; RMSE = 0.24 − 4.25%) and results indicate that a single model may be used for a given trait across all species. Models using spaceborne imaging spectroscopy demonstrated that crop residue carbon traits can be successfully estimated using PRISMA imagery (adj. &lt;i&gt;R&lt;/i&gt;&lt;sup&gt;&lt;i&gt;2&lt;/i&gt;&lt;/sup&gt; = 0.65 − 0.75; RMSE = 2.71 − 4.16%). We found moderate relationships between nitrogen concentration and PRISMA imagery (adj. &lt;i&gt;R&lt;/i&gt;&lt;sup&gt;&lt;i&gt;2&lt;/i&gt;&lt;/sup&gt; = 0.52; RMSE = 0.25%), which is partly related to the range of nitrogen in these senesced crop residues (0.38–1.85%). PRISMA imagery models were also influenced by atmospheric absorption, variability in surface moisture content, and some presence of green vegetation.&lt;/p&gt;&lt;h3 data-test=\"abstract-sub-heading\"&gt;Conclusion&lt;/h3&gt;&lt;p&gt;As spaceborne imaging spectroscopy data become more widely available from upcoming missions, crop residue trait estimates could be regularly generated and integrated into decision support tools to calculate decomposition rates and associated nitrogen credits to inform precision field management, as well as to enable measurement, monitoring, reporting, and verification of net carbon benefits from climate smart agricultural practice adoption in an eme","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"2015 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141462547","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}