The use of near infrared (NIR) spectroscopy to improve soil mapping at the farm scale

The creation of fine resolution soil maps is hampered by the increasing costs associated with conventional laboratory analyses of soil. In this study, near infrared (NIR) reflectance spectroscopy was used to reduce the number of conventional soil analyses required by the use of calibration models at the farm scale. Soil electrical conductivity and mid infrared (MIR) reflection from a satellite image were used and compared as ancillary data to guide the targeting of soil sampling. About 150 targeted samples were taken over a 97 hectare farm (approximately 1.5 samples per hectare) for each type of ancillary data. A sub-set of 25 samples was selected from each of the targeted data sets (150 points) to measure clay and soil organic matter (SOM) contents for calibration with NIR. For the remaining 125 samples only their NIR-spectra needed to be determined. The NIR calibration models for both SOM and clay contents resulted in predictions with small errors. Maps derived from the calibrated data were compared with a map based on 0.5 samples per hectare representing a conventional farm-scale soil map. The maps derived from the NIR-calibrated data are promising, and the potential for developing a cost-effective strategy to map soil from NIR-calibrated data at the farm-scale is considerable

Test av portabel röntgenfluorescens (PXRF) för bestämning av jordart, näringsämnen och tungmetaller direkt i fält

Vid markkartering tas matjordsprov som analyseras i laboratorium. Ett stort antal markvariabler kan vara aktuella att analysera. I det här projektet utvärderades möjligheten att använda ett handburet instrument som mäter röntgenfluorescens (PXRF – portable x-ray fluorescence) för att snabbt uppskatta flera sådana variabler direkt i fält. Ett stort antal mätningar med PXRF utfördes på gårdar i Västergötland, där även jordprovtagning med tillhörande kemiska jordanalyser utfördes. De markkarteringsvariabler som undersöktes var ler-, sand- och mullhalt, fosfor (P-AL), kalium (K-AL), calcium (Ca-AL), koppar (Cu-HCl), magnesium (Mg-AL), molybden (Mo-HNO3) och kadmium (Cd-HNO3). Jämförelser mellan insamlade PXRF-data – som gjordes både i fält på markytan och på upptagna jordprover, både före och efter torkning – visade att flera element som bestäms med PXRF var väl korrelerade till flera av markkarteringsvariablerna. Särskilt väl samvarierar textur, Cu, Ca, Mg och Cd med olika ämnen som bestäms med PXRF. Även vid mätning direkt i fält med PXRF finns möjlighet att få fram användbara data. Tekniken kan minska behovet av jordprovtagning och/eller fungera som en snabb metod för kompletterande datainsamling som kan göra det möjligt att kostnadseffektivt ta fram detaljkartor för aktuella markegenskaper

Remote sensing and on-farm experiments for determining in-season nitrogen rates in winter wheat – Options for implementation, model accuracy and remaining challenges

Optimised nitrogen (N) fertilisation can be used to increase farm profits, to realise the achievement of quality goals for produce, and to reduce environmental risks in the form of leaching and/or volatilisation of N compounds from the fields. This study examined options and challenges for remote sensing-based variable rate supplemental N fertilisation in winter wheat (Triticum aestivum L.). The models were based on data from ten field trials conducted in different regions across Sweden over three years. A two-step approach for modelling optimal N rates, suitable for practical implementation in precision agriculture, was developed and evaluated. The expected accuracies for new sites and years were assessed by leave-one-entire-trial-out cross-validation. In a first step, the average N rate was modelled from site-specific information, including data that can be obtained from on-farm experiments, i.e. N uptake in plots without N fertilisation (zero-plots) and N uptake in plots with non-limiting N supply (max-plots). In the second step, additions or subtractions from this average N rate was modelled based on vegetation indices (VIs) mapped by remote sensing. Mean absolute error of the best prediction was 14 kg N ha−1. In a practical application, however, there will be additional uncertainty from several sources, e.g. uncertainty in the assessment of yield potential. The best mean N rate model was based on geographical region, cultivar, N uptake in zero-plots and yield potential, while the best model of relative N rate within the field used a new multispectral index (d75r6), which was designed to give a standardized measure of the steepness of the red edge of reflectance of a crop canopy spectrum. Several other multispectral VIs also performed well but red-green-blue indices were less useful. We conclude that remote sensing (to capture within-field spatial variation patterns), on-farm experiments (to determine the field mean N rate), and the farmers’ experience and knowledge on local conditions (e.g. to assess the yield potential), is a useful combination of information sources in decision support systems for variable rate application of N. Options and remaining research needs for the setup of such a system are discussed

CropSAT – A decision support system for practical use of satellite images in precision agriculture

CropSAT is an interactive decision support system (DSS) that provides vegetation index (VI) maps from Sentinel-2 data all across the globe and lets users delineate fields, design variable-rate application of user specified inputs (mainly nitrogen, but also e.g. fungicides or growth regulators) based on the VI maps. The CropSAT DSS was initially developed in a research project at the Swedish University of Agricultural Sciences (SLU), and has since its launch in 2015 been continuously developed in a private-public-partnership between SLU, private companies and the Swedish Board of Agriculture. Now it has global coverage, is continuously updated with new satellite images, and is provided free-of-charge in multiple languages (including Arabic and French). The present study aims at describing the CropSAT systems, summarizing research results from the ongoing developmental process and pointing to opportunities for applications in precision agriculture, e.g. in Morocco and other countries in North Africa

Perspectives on validation in digital soil mapping of continuous attributes—A review

We performed a systematic mapping of validation methods used in digital soil mapping (DSM), in order to gain an overview of current practices and make recommendations for future publications on DSM studies. A systematic search and screening procedure, largely following the RepOrting standards for Systematic Evidence Syntheses (ROSES) protocol, was carried out. It yielded a database of 188 peer-reviewed DSM studies from the past two decades, all written in English and all presenting a raster map of a continuous soil property. Review of the full-texts showed that most publications (97%) included some type of map validation, while just over one-third (35%) estimated map uncertainty. Most commonly, a combination of multiple (existing) soil sampe datasets was used and the resulting maps were validated by single data-splitting or cross-validation. It was common for essential information to be lacking in method descriptions. This is unfortunate, as lack of information on sampling design (missing in 25% of 188 studies) and sample support (missing in 45% of 188 studies) makes it difficult to interpret what derived validation metrics represent, compromising their usefulness. Therefore, we present a list of method details that should be provided in DSM studies. We also provide a detailed summary of the 28 validation metrics used in published DSM studies, how to interpret the values obtained and whether the metrics can be compared between datasets or soil attributes

Digital soil mapping of copper in Sweden: Using the prediction and uncertainty as decision support in crop micronutrient management

Digital soil mapping (DSM) of topsoil copper (Cu) concentrations and prediction intervals covering 90% of agricultural land in Sweden was performed, in order to identify areas at risk of Cu deficiency. A total of 12,527 soil samples were used to calibrate the DSM model, using airborne gamma radiation data, climate data, topographical data and soil texture class data. Among the samples included, 11,093 had no laboratory-analysed Cu concentrations, so their Cu concentrations were predicted using portable X-ray fluorescence (PXRF) measurements. Cross-validation of the PXRF model resulted in Nash-Sutcliffe model efficiency coefficient (E) of 0.66 and mean absolute error (MAE) of 3.3 mg kg−1. Cross-validation of the DSM model showed somewhat lower performance (E = 0.57, MAE = 4.1 mg kg−1). Based on the lower bound of the prediction interval (5th percentile), 48% of agricultural soils in Sweden are most likely not at risk of Cu deficiency (>7 mg kg−1). The Cu map was also validated against concentrations in soil samples from five fields (25–47 ha in size; four samples per ha). The field means were predicted with a MAE of 1.0 mg kg−1 and within-field variation was reproduced with a field-wise squared Pearson correlation coefficient (r2) of 0–0.36. The classification metric ‘recall’ showed that the map of soil Cu concentrations might not predict all possible areas at risk of being Cu deficient, as observational data indicates that about 22% of soils in the mapped area should have Cu concentrations below the risk limit. However, the metric ‘precision’ showed that when the soil map predicted a concentration at or below 7 mg kg−1, it was generally correct. Increasing the limit resulted in the recall and precision increasing rapidly. The remaining 52% of agricultural soils at risk of being below the Cu concentration limit can be targeted by laboratory analysis or monitoring

Går det att bestämma vattenhalten i fält med NIR för korrigering av andra sensormätningar?

En stor skillnad mellan sensormätningar gjorda på lab och mätningar gjorda direkt i fält är att fältmätningarna påverkas av vattenhalten i marken. Vattenhalten kommer att varierar mellan mätningstidpunkter men också beroende på var på fältet mätningen görs. Påverkan av vattenhalt och variationer i vattenhalt är några av anledningarna till att bestämningarna av t ex jordart eller tungmetaller från sensormätningar ofta blir något sämre med fältmätningar jämfört med mätningar på torkad jord på lab. Syftet med pilotprojektet var därför att undersöka möjligheten att göra vattenhaltsbestämningar direkt i fält med nära infraröd reflektans (NIR) spektroskopi. Vattenhaltsbestämningar från NIR-mätningar direkt i fält i samband med andra sensormätningar skulle kunna användas för att korrigera för vattenhalt i de andra sensormätningarna och därmed förbättra bestämningarna av jordart och tungmetaller utan att behöva ta in en mängd prov för analys på lab. Resultaten visar på möjligheten att använda NIR-mätningar för att bestämma vattenhalten i marken i samband med andra sensormätningar som t ex med ett PXRF (portabelt röntgenfluorescens) instrument. Men texturen har en stor inverkan på både vattenhalt och spektrum och det är viktigt att få med variationen i både vattenhalt och i textur i kalibreringen. Bäst modeller fick vi genom att använda ett fåtal provplatser, två, som täckte in ytterligheterna i textur och sedan bygga modeller på de två platserna där vi hade 10 olika vattenhalter per plats. Detta är möjligt i teorin, men inte i praktiken om modellerna visar sig vara så specifika som resultaten av pilotstudien antyder. Detta måste dock testas på fler platser. Mer framkomligt är det då att göra lokala kalibreringar i samband med varje sensormätning. Det skulle innebära att ett antal prov togs in för vattenhaltsbestämning genom torkning. Då de flesta andra sensorer också kräver någon form av kalibrering behöver detta inte nödvändigtvis innebära så mycket extrajobb. I pilotstudien gjordes sådana kalibreringar med 10 och 16 prov. Det var i minsta laget men då ska man komma ihåg att de tre fälten som ingick i pilotstudien har väldigt hög variation i textur. Med mindre texturvariationer borde det gå att hålla nere antalet kalibreringsprov för vattenhaltsbestämning

Digitala åkermarkskartan

Den digitala åkermarkskartan är en ny, allmänt tillgänglig, digital kartprodukt som ger information om matjordens egenskaper i en skala som avses vara relevant för åtgärder i lantbruket. Kartans upplösning är 50 m × 50 m och täcker i princip all åkermark upp till och med Gävleborgs län. De första kartlagren beskriver matjordens lerhalt respektive sandhalt. De beräknade värdena har en osäkerhet som varierar i olika regioner och i olika skalor. För hela kartan är medelfelet för lerhalt 5,6 % och r2 = 0.76. Motsvarande värden för sand var 11,3 % och r2 = 0,57. Lokalt kan osäkerheten förstås vara både större eller mindre