Agricultural Internet of Things and decision support for precision smart farming

Agricultural Internet of Things and Decision Support for Smart Farming reveals how a set of key enabling technologies (KET) related to agronomic management, remote and proximal sensing, data mining, decision-making and automation can be efficiently integrated in one system. Chapters cover how KETs enable real-time monitoring of soil conditions, determine real-time, site-specific requirements of crop systems, help develop a decision support system (DSS) aimed at maximizing the efficient use of resources, and provide planning for agronomic inputs differentiated in time and space. This book is ideal for researchers, academics, post-graduate students and practitioners who want to embrace new agricultural technologies

A comprehensive review of proximal electromagnetic sensors' accuracy and cost considerations for soil property prediction and mapping

Centennial Celebration and Congress of the International Union of Soil Sciences Florence - Italy May 19 - 21, 2024 ABSTRACT BOOKProximal soil sensors (PSS) are used to efficiently characterize soil properties from point to farm/field scale and reduce the need for cost- and labor-intensive soil sampling and laboratory analysis for creating highresolution maps. They enable rapid means for soil characterization and monitoring of soil properties, providing tools to make informed decisions aiming at the improvement of productivity, soil health conservation, and mitigation of environmental impacts. A framework for selecting the most suitable PSS method for mapping a specific soil property based on expected accuracy and associated costs is lacking. Within the ProbeField project, we are reviewing the accuracy of electromagnetic PSS in estimating specific soil properties and quantifying associated costs. Moreover, we discuss cost and accuracy variation when using multiple techniques simultaneously. The lack of information on costs in the literature caused us to perform a market analysis through questionnaires directed to companies, a unique aspect of this study. Our review hopes to be a guide for professionals, academics, and other end-users in PSS. We reviewed a total of 209 studies. The normalized root-mean-squared error (NRMSE) was used as a measure of accuracy in estimating soil properties. Among all, diffuse reflectance spectroscopy (DRS) and Xray fluorescence (XRF) techniques exhibit higher accuracy in estimating soil carbon and nutrients, however, require soil sample contact. Gamma-ray radiometry and electromagnetic induction (EMI) are the most common on-the-go sensor combinations, especially used to accurately estimate water content and soil texture. The Cost of mapping services ranges between a few hundred to several thousand euros per working day depending on the technique and type of sensor used. About 75% of mapping cost is attributed to fieldwork personnel, and data analysis and reporting, while the other 25% is to movement efforts and sample analysis. Several companies report extra charges attributed to fieldwork conditions. Results demonstrate that portable sensors offer accurate and cheaper point estimations, although on-the-go sensors offer better spatial estimations at the expense of accuracy

An approach for mapping Net Ecosystem Productivity (NEP) as a pragmatic indicator of soil ecosystem service greenhouse gas (GHG) regulation including carbon sequestration in EU Member States

Trabajo presentado en Annual Science Days - EJPSoil- European Joint Programme on Soil, celebrado en Vilnius (Lituania) del 10 al 14 de julio de 2024.Modelling the spatio-temporal distribution of Soil Ecosystem Services (SESs) can provide insights to identify their drivers (e.g., land use, agricultural management), improving our understanding of SESs and their relationships, and the implementation of environmental policies. The SES regulation of greenhouse gas (GHG) fluxes from agricultural soils in EU, would especially benefit from such spatio temporal modelling. Within SERENA project funded by EJP SOIL EU programme, to fill this gap, we are developing an approach to be included in a cookbook for the estimation of the net ecosystem productivity (NEP Gross primary production, GPP and Ecosystem respiration, Reco) as pragmatic indicator of the GHG regulation selected in the first stage of the project. The selection was based on the ranking of different types of GHG indicators from a literature review. Based on different criteria (scientific soundness, data availability, and ability to convey information), we were not able to select an ¿ideal¿ indicator which provided complete information (such as the sum of all GHG fluxes) for this SES, but instead selected NEP as a ¿pragmatic¿ GHG indicator. At the next stage, we realized that methods to estimate NEP based on the analysis of light-use efficiency models were impractical to be implemented by project partners. It was also suggested not to use mechanistic models for assessing NEP since methods should be easily applicable, even without scripting knowledge. Thus, we focused on a newly developed empirical model that could relate NEP to spatially exhaustive environmental covariates and be applicable with open GIS software. This was done by relating the well-known Fluxnet database of eddy covariance measures to spatially exhaustive covariates for agricultural areas (3600 8-day estimates of CO2 fluxes). The approach for mapping NEP in EU member states includes three main stages: 1) GPP estimation from Fluxnet stations that grow/have grown wheat in the EU (and one US station) were related to the MODIS 8-days GPP values, monthly average temperature (WorldClim), and a recent high temporal resolution database of daily soil volumetric moisture. 2) Reco estimates from the selected Fluxnet stations were fitted with a thermal performance model to monthly average temperature (WorldClim). 3) The NEP estimate is calculated as GPP-Reco, and after the calculation, there is an additional last step where its finer spatial distribution is made explicit with the EU-2018 crop layer at 10-m resolution, published by JRC, for locations recorded as wheat. Whereas the fitting quality for each independent component of NEP was relatively good, the overall fitting of the NEP indicator was not. Improvement could be obtained by applying other model fitting techniques (e.g. Gaussian Process Regression), using high-resolution environmental variables (with a weekly step), and trying to incorporate soil properties that have a much lower temporal variability (scales of several years) than the temporal scale of the main CO2 flux data (weekly, seasonal and yearly). However, such improvements most certainly would come with a cost in terms of cookbook applicability

Do we speak one language on the way to sustainable soil management in Europe? A terminology check via an EU-wide survey

European soils are under increasing pressure, making it difficult to maintain the provision of soil ecosystem services (SESs). A better understanding of soil processes is needed to counteract soil threats (STs) and to promote sustainable soil management. The EJP SOIL programme of the EU provides a framework for the necessary research. However, different definitions of soil-related terms potentially lead to varied understandings of concepts. Furthermore, there are numerous indicators available to quantify STs or SESs. As unclear communication is a key barrier that hinders the implementation of research results into practice, this study aimed to answer the question about whether the terminology of large-scale initiatives is adequately understood within the soil-science community and non-research stakeholders. An online questionnaire was used to provide definitions for 33 soil-related terms in both scientific and plain language, as well as indicators for seven SESs and 11 STs. Participants were asked to rate their agreement with the definitions and indicators on a seven-grade Likert scale. The level of agreement was calculated as the percentage of ratings above 4, the neutral position. The survey was available from June to September 2023 and was distributed by a snowball approach. More than 260 stakeholders assessed the survey; 70% of respondents were researchers, and 15% were practitioners. Mean agreement levels for the definitions and indicators were generally high, at 85% and 78% respectively. However, it was apparent that the lowest agreement was found for terms that are relatively new, such as Ecosystem Services and Bundle, or unfamiliar for certain subgroups, such as ecological terms for stakeholders working at the farm scale. Due to their distinct majority, the results of this study primarily reflect the opinions of scientists. Thus, broad conclusions can only be drawn by comparing scientists with non-scientists. In this regard, the agreement was surprisingly high across all types of questions. The combined outcomes indicate that there is still a need to facilitate communication between stakeholders and to improve knowledge distribution strategies. Nevertheless, this study can support and be used by future projects and programmes, especially regarding the harmonization of terminology and methods.This research has been carried out within the framework of the SERENA project. SERENA (Soil Ecosystem seRvices and soil threats modElling aNd mApping) is an EJP SOIL internal project. EJP SOIL has received funding from the European Union's Horizon 2020 research and innovation programme: Grant agreement No 862695.Peer reviewe

The state of soils in Europe

This report delves into the intricate interplay between drivers, pressures and impacts on soil in the 32 Member States of the European Environment Agency (EEA), along with six cooperating countries from the West Balkans, Ukraine and UK, shedding light on the multifaceted challenges facing soil conservation efforts. Our analysis shows the complex interactions among various factors, both anthropogenic and natural, shaping soil degradation processes and their subsequent consequences. We highlight key findings, including the significant impacts of soil degradation on agriculture, ecosystem resilience, water quality, biodiversity, and human health, underscoring the urgent need for comprehensive soil management strategies. Moreover, our examination of citizen science initiatives underlines the importance of engaging the public in soil monitoring and conservation efforts. This work emphasises the policy relevance of promoting sustainable soil governance frameworks, supported by research, innovation, and robust soil monitoring schemes, to safeguard soil health and ensure the long-term resilience of ecosystems.JRC.D.3 - Land Resources and Supply Chain Assessment

Comparing Two Geostatistical Simulation Algorithms for Modelling the Spatial Uncertainty of Texture in Forest Soils

Uncertainty assessment is an essential part of modeling and mapping the spatial variability of key soil properties, such as texture. The study aimed to compare sequential Gaussian simulation (SGS) and turning bands simulation (TBS) for assessing the uncertainty in unknown values of the textural fractions accounting for their compositional nature. The study area was a forest catchment (1.39 km2) with soils classified as Typic Xerumbrepts and Ultic Haploxeralf. Samples were collected at 135 locations (0.20 m depth) according to a design developed using a spatial simulated annealing algorithm. Isometric log-ratio (ilr) was used to transform the three textural fractions into a two-dimensional real vector of coordinates ilr.1 and ilr.2, then 100 realizations were simulated using SGS and TBS. The realizations obtained by SGS and TBS showed a strong similarity in reproducing the distribution of ilr.1 and ilr.2 with minimal differences in average conditional variances of all grid nodes. The variograms of ilr.1 and ilr.2 coordinates were better reproduced by the realizations obtained by TBS. Similar results in reproducing the texture data statistics by both algorithms of simulation were obtained. The maps of expected values and standard deviations of the three soil textural fractions obtained by SGS and TBS showed no notable visual differences or visual artifacts. The realizations obtained by SGS and TBS showed a strong similarity in reproducing the distribution of isometric log-ratio coordinates (ilr.1 and ilr.2). Overall, their variograms and data were better reproduced by the realizations obtained by TBS

Vis-NIR Spectroscopy for Determining Physical and Chemical Soil Properties: An Application to an Area of Southern Italy

The development of rapid, accurate, cost effective methods to determine soil physical and chemical properties is important for sustainable land management. In the last two to three decades, the interest in using visible and near infrared (Vis-NIR) spectroscopy as an alternative method for determining soil properties has increased. To obtain reliable predictions of soil properties, multivariate calibration techniques such as Partial Least Squares Regression (PLSR) are commonly used to correlate the spectra with the chemical, physical and mineralogical properties of soils.The objective of the paper was to assess the potential of Vis-NIR spectroscopy coupled with PLSR to determine soil chemical and physical properties such as organic carbon (SOC), sand, silt, clay, and calcium carbonate (CaCO3) contents in a sample site of southern Italy.Spectral curves showed that the soils could be spectrally separable on the basis of chemical and physical properties. PLSR calibration models were derived for each of the soil properties and were validated with an independent data set. The optimum number of factors to be retained in the calibration models was determined by leave-one-out cross-validation. The accuracy of the calibration and validation models for the different soil properties was evaluated with the coefficient of determination (R2) and the root mean squared error (RMSE). The results showed that predictions were satisfactory for all soil properties analyzed with high values of R2 > 80.A combination of Vis-NIR spectroscopy and multivariate statistical techniques, therefore, can be used as a rapid, low cost and quantitative means of characterizing the soils of southern Italy

Insights into the Effects of Study Area Size and Soil Sampling Density in the Prediction of Soil Organic Carbon by Vis-NIR Diffuse Reflectance Spectroscopy in Two Forest Areas

Sustainable forest land management requires measuring and monitoring soil organic carbon. Visible and near-infrared diffuse reflectance spectroscopy (Vis-NIR, 350–2500 nm), although it has become an important method for predicting soil organic carbon (SOC), requires further studies and methods of analysis to realize its full potential. This study aimed to determine if the size of the study area and soil sampling density may affect the performance of Vis-NIR diffuse reflectance spectroscopy in the prediction of soil organic carbon. Two forest sites in the Calabria region (southern Italy), which differ in terms of area and soil sampling density, were used. The first one was Bonis catchment area (139 ha) with a cover consisting mainly of Calabrian pine, while the second was Mongiana forest area (33.2 ha) within the “Marchesale” Biogenetic Nature Reserve, which is covered by beech. The two study areas are relatively homogeneous regarding parent material and soil type, while they have very different soil sampling density. In particular, Bonis catchment has a lower sampling density (135 samples out of 139 ha) than Mongiana area (231 samples out of 33.2 ha). Three multivariate calibration methods (principal component regression (PCR), partial least square regression (PLSR), and support vector machine regression (SVMR)) were combined with different pretreatment techniques of diffuse reflectance spectra (absorbance, ABS, standard normal variate, SNV, and Savitzky–Golay filtering with first derivative (SG 1st D). All soil samples (0–20 cm) were analyzed in the laboratory for SOC concentration and for measurements of diffuse reflectance spectra in the Vis-NIR region. The set of samples from each study area was randomly divided into a calibration set (70%) and a validation set (30%). The assessment of the goodness for the different calibration models and the following SOC predictions using the validation sets was based on three parameters: the coefficient of determination (R2), the root mean square error (RMSE), and the interquartile range (RPIQ). The results showed that for the two study areas, different levels of goodness of the prediction models depended both on the type of pretreatment and the multivariate method used. Overall, the prediction models obtained with PLSR and SVMR performed better than those of PCR. The best performance was obtained with the SVMR method combined with ABS + SNV + SG 1st D pretreatment (R2 ≥ 0.77 and RPIQ > 2.30). However, there is no result that can absolutely provide definitive indications of either the effects of the study area size and soil sampling density in the prediction of SOC by vis-NIR spectroscopy, but this study fostered the need for future investigations in areas and datasets of different sizes from those in this study and including also different soil landscapes