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

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

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

Asunto-osakeyhtiöiden purkavan uusrakentamisen helpottaminen turvallisesti, ennakoitavasti ja tehokkaasti. Lausuntotiivistelmä

Oikeusministeriön työryhmä ehdotti asunto-osakeyhtiölain muuttamista purkavan uusrakentamisen helpottamiseksi siten, että samalla turvataan kunkin osakkaan asuminen, asuntovarallisuuden arvo ja muut asunto-osakkeiden tuottamat oikeudet (OMML 16/2018). Ehdotuksen mukaan yhtiökokous voi päättää 4/5 määräenemmistöllä uusrakentamisesta, jossa osakkaat saavat uudesta rakennuksesta uudet huoneistot yhdenvertaisuusperiaatteen mukaisesti ja muuten tietyin ehdoin. Toisena keinona työryhmä ehdotti mahdollisuutta lunastaa vähemmistöosakkeet käypään hintaan, kun lunastajalla on yli 90% yhtiön osakkeista ja äänistä. Lisäksi työryhmä ehdotti, että yhtiökokous voisi 4/5 määräenemmistöllä päättää korjauskelvottoman rakennuksen ja kiinteistön luovuttamisesta ja selvitystilasta siten, että yhtiön netto-omaisuus jaettaisiin saman tien osakkaille. Lähes kaikki lausunnonantajat kannattivat yleisesti esitettyä asunto-osakeyhtiölain muutosta. Osa lausunnonantajista ehdotti riippumattoman asiantuntijan pätevyysvaatimusten säätämistä suoraan laissa. Huomattava osa lausunnonantajista ehdotti intressiristiriitatilanteiden sääntelyn erillistä jatkovalmistelua tai nykyistä esteellisyyssääntelyä ehdotetun lähipiirisääntelyn sijasta. Osa lausunnonantajista katsoi, ettei verotuskysymyksiä, asuntokauppalain soveltamista uusien asuntojen myyntiin vanhoille osakkaille, suhdetta maankäyttö- ja rakennuslakiin, aiemmin osakkaille myönnettyjen avustusten takaisinperintään ja huoneenvuokrasuhteiden sääntelyyn ole riittävällä tavalla käsitelty

Kansainvälinen selvitys velkakonversion ulottuvuuksista yritysjärjestelyiden ja maksukyvyttömyystilanteiden näkökulmasta. Lausuntotiivistelmä

Asianajotoimisto Borenius laati valtioneuvoston kanslian toimeksiannosta vuonna 2017 kansainvälisen vertailun maksuvaikeuksissa olevan yrityksen velkojen muuntamisesta osakkeiksi ja osakepääomaksi (VN-TEAS: VNK/817/48/2017). Selvityksen tavoitteena oli selvittää Suomen lainsäädännön kannalta keskeisten vertailuvaltioiden lainsäädännön sekä lainvalmisteluaineiston pohjalta velkajärjestelyn ja erityisesti velkakonversion mahdollista käyttöä maksuvaikeuksiin ajautuneen tai ajautumassa olevan yhtiön osalta. Selvityksen loppuraportti (https://tietokayttoon.fi/julkaisu?pubid=24204) julkaistiin tammikuussa 2018, minkä jälkeen oikeusministeriö järjesti siitä keväällä 2018 laajan lausuntokierroksen. Lausunnonantajia pyydettiin arvioimaan sekä selvityksen tuloksia että selvityksen aiheeseen liittyvän Suomen maksukyvyttömyys- ja yhtiölainsäädännön toimivuutta ja muutostarpeita. Enemmistö lausunnonantajista piti Suomen oikeustilaa puutteellisena velkakonversion ja maksunsaantijärjestyksen osalta, ja kannatti yrityssaneerausta ja yritysjärjestelyjä koskevien säännösten muutostarpeen tarkempaa arviointia. Moni lausunnonantaja esitti muun muassa, että kansainvälisistä käytännöistä poikkeava osakkeenomistajien etusija velkojiin nähden vääristää suomalaista insolvenssijärjestelmää. Suuri osa lausunnonantajista oli sitä mieltä, että velkakonversiota koskevan lainsäädännön muuttaminen on toteutettava kiireellisesti. Useat lausunnonantajat pitivät sääntelyn tarkistamista tarpeellisena EU:ssa valmisteltavana olevan insolvenssidirektiivin johdosta. Toisaalta eräät lausunnonantajat pitivät nykytilaa riittävänä, eivätkä nähneet muutostarvetta. Lausunnonantajilla oli vaihtelevia käsityksiä mahdollisen sääntelyn tarkemmasta sisällöstä ja velkakonversion toteuttamistavasta

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

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

Potential for soil organic carbon sequestration in grasslands in East African countries: A review

Grasslands occupy almost half of the world's land area. Soil organic carbon (SOC) is a key indicator of soil fertility and grassland productivity. Increasing SOC stocks (so‐called SOC sequestration) improves soil fertility and contributes to climate change mitigation by binding atmospheric carbon dioxide (CO2). Grasslands constitute about 70% of all agricultural land, but their potential for SOC sequestration is largely unknown. This review paper quantitatively summarizes observation‐based studies on the SOC sequestration potential of grasslands in six East African countries (Burundi, Ethiopia, Kenya, Rwanda, Tanzania and Uganda) and seeks to identify knowledge gaps related to SOC sequestration potential in the region. In the studies reviewed, SOC stocks in grasslands range from 3 to 93 Mg C/ha in the upper 0.3 m of the soil profile, while SOC sequestration rate ranges from 0.1 to 3.1 Mg C ha‐1 year‐1 under different management strategies. Grazing management is reported to have a considerable impact on SOC sequestration rates, and grassland regeneration and protection are recommended as options to stimulate SOC sequestration. However, a very limited number of relevant studies are available (n = 23) and there is a need for fundamental information on SOC sequestration potential in the region. The effectiveness of potential incentive mechanisms, such as payments for environmental services, to foster uptake of SOC‐enhancing practices should also be assessed

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