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

Micronutrient deficiencies in African soils and the human nutritional nexus: opportunities with staple crops

A synthesis of available agronomic datasets and peer-reviewed scientific literature was conducted to: (1) assess the status of micronutrients in sub-Saharan Africa (SSA) arable soils, (2) improve the understanding of the relations between soil quality/management and crop nutritional quality and (3) evaluate the potential profitability of application of secondary and micronutrients to key food crops in SSA, namely maize (Zea mays L.), beans (Phaseolus spp. and Vicia faba L.), wheat (Triticum aestivum L.) and rice (Oryza sativa L.). We found that there is evidence of widespread but varying micronutrient deficiencies in SSA arable soils and that simultaneous deficiencies of multiple elements (co-occurrence) are prevalent. Zinc (Zn) predominates the list of micronutrients that are deficient in SSA arable soils. Boron (B), iron (Fe), molybdenum (Mo) and copper (Cu) deficiencies are also common. Micronutrient fertilization/agronomic biofortification increases micronutrient concentrations in edible plant organs, and it was profitable to apply fertilizers containing micronutrient elements in 60–80% of the cases. However, both the plant nutritional quality and profit had large variations. Possible causes of this variation may be differences in crop species and cultivars, fertilizer type and application methods, climate and initial soil conditions, and soil chemistry effects on nutrient availability for crop uptake. Therefore, micronutrient use efficiency can be improved by adapting the rates and types of fertilizers to site-specific soil and management conditions. To make region-wide nutritional changes using agronomic biofortification, major policy interventions are needed

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

Skördeprognos med hjälp av YARA N-sensor

Syftet med projektet var att utreda möjligheten att i höstvete använda Yara N-sensorn för prognos av skördens storlek inför kompletteringsgödsling med kväve. Undersökningen består av två delar: 1) en genomgång av internationell vetenskaplig litteratur inom området och 2) sammanställning av data från 39 höstveteförsök (2012-2014) samt utvärdering av prognosmodeller för kärnskörd som baseras på SN-värdet från handburen Yara N-sensor i olika utvecklingsstadier (DC39-63). Försöksvis validering av modellerna de enskilda åren visade att skörden 2012 och 2013 predikterades bäst vid det senaste mättillfället, vilket var DC45-55 2012 och DC56-63 2013. Medelavvikelsen (RMSECV) för den validerade modellens skördeuppskattning jämfört med uppmätt skörd var då som lägst, 11 dt/ha. 2014 predikterades skörden bäst vid DC37-42, då medelavvikelsen för valideringen var 11 dt/ha (tabell 1), men även vid DC56-63 var medelavvikelsen låg, 12 dt/ha. Vid årsvis validering, d v s prediktion av skörd för ett år i taget utifrån en modell baserad på de andra två åren var medelavvikelsen från uppmätt skörd som lägst 18 dt/ha (RMSECV) vid DC37-42. Resultaten visar på goda möjligheter att prediktera skörden vid DC37-39 då kompletteringsgödsling vanligen rekommenderas, även om gödsling också i senare stadier kan ge skördeökningar vissa år. Ytterligare data från fler år och platser behövs för att kunna bygga en stabilare skördeprognosmodell som kan användas för att prediktera skörden ett kommande år, med så låg medelavvikelse som möjligt från den verkliga skörden. Det är viktigt att fortsätta göra N-sensormätningar i försök på flera platser i landet och fortsätta bygga upp databasen. Vi rekommenderar att man även går vidare och provar prova hur långt man kan komma med N-sensorns våglängdsband i multivariata modeller. Att kombinera grödmodeller med sensormätningar är en mer sofistikerad strategi. Det skulle kunna fungera bra men ett mer omfattande utvecklingsarbete krävs. Den enklaste strategin att börja med är förmodligen att ta fram relativa skördekartor inom fält baserat på tidigare års skördekartor, manuellt sätta skördenivån i de olika delarna utifrån erfarenhet, och lägga in dessa som bakgrunds information i N-sensorns styrning

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

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

Upscaling proximal sensor N-uptake predictions in winter wheat (Triticum aestivum L.) with Sentinel-2 satellite data for use in a decision support system

Total nitrogen (N) content in aboveground biomass (N-uptake) in winter wheat (Triticum aestivum L.) as measured in a national monitoring programme was scaled up to full spatial coverage using Sentinel-2 satellite data and implemented in a decision support system (DSS) for precision agriculture. Weekly field measurements of N-uptake had been carried out using a proximal canopy reflectance sensor (handheld Yara N-Sensor) during 2017 and 2018. Sentinel-2 satellite data from two processing levels (top-of-atmosphere reflectance, L1C, and bottom-of-atmosphere reflectance, L2A) were extracted and related to the proximal sensor data (n = 251). The utility of five vegetation indices for estimation of N-uptake was compared. A linear model based on the red-edge chlorophyll index (CI) provided the best N-uptake prediction (L1C data: r(2) = 0.74, mean absolute error; MAE = 14 kg ha(-1)) when models were applied on independent sites and dates. Use of L2A data, rather than L1C, did not improve the prediction models. The CI-based prediction model was applied on all fields in an area with intensive winter wheat production. Statistics on N-uptake at the end of the stem elongation growth stage were calculated for 4169 winter wheat fields > 5 ha. Within-field variation in predicted N-uptake was > 30 kg N ha(-1) in 62% of these fields. Predicted N-uptake was compared against N-uptake maps derived from tractor-borne Yara N-Sensor measurements in 13 fields (1.7-30 ha in size). The model based on satellite data generated similar information as the tractor-borne sensing data (r(2) = 0.81; MAE = 7 kg ha(-1)), and can therefore be valuable in a DSS for variable-rate N application

Predicting grain protein concentration in winter wheat (Triticum aestivum L.) based on unpiloted aerial vehicle multispectral optical remote sensing

Prediction models for crude protein concentration (CP) in winter wheat (Triticum aestivum L.) based on multispectral reflectance data from field trials in 2019 and 2020 in southern Sweden were developed and evaluated for independent trial sites. Reflectance data were collected using an unpiloted aerial vehicle (UAV)-borne camera with nine spectral bands having similar specification to nine bands of Sentinel-2 satellite data. Models were tested for application on near-real time Sentinel-2 imagery, on the prospect that CP prediction models can be made available in satellite-based decision support systems (DSS) for precision agriculture. Two different prediction methods were tested: linear regression and multivariate adaptive regression splines (MARS). Linear regression based on the best-performing vegetation index (the chlorophyll index) was found to be approximately as accurate as the best performing MARS model with multiple predictor variables in leave-one-trial-out cross-validation (R-2 = 0.71, R-2 = 0.70 and mean absolute error 0.64%, 0.60% CP respectively). Models applied on satellite data explained to a small degree between-field variations in CP (R-2 = 0.36), however did not reproduce within-field variation accurately. The results of the different methods presented here show the differences between methods used and their potential for application in a DSS