iSDAsoil: The first continent-scale soil property map at 30 m resolution provides a soil information revolution for Africa.

Open access, high-resolution soil property maps have been created for Africa at 30 m resolution, using machine learning trained on over 100,000 analysed soil samples. Combined with other field-level information, iSDAsoil enables the possibility of site-specific agronomy advisory for smallholder farmers.

African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning.

Soil property and class maps for the continent of Africa were so far only available at very generalised scales, with many countries not mapped at all. Thanks to an increasing quantity and availability of soil samples collected at field point locations by various government and/or NGO funded projects, it is now possible to produce detailed pan-African maps of soil nutrients, including micro-nutrients at fine spatial resolutions. In this paper we describe production of a 30 m resolution Soil Information System of the African continent using, to date, the most comprehensive compilation of soil samples ([Formula: see text]) and Earth Observation data. We produced predictions for soil pH, organic carbon (C) and total nitrogen (N), total carbon, effective Cation Exchange Capacity (eCEC), extractable-phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), sodium (Na), iron (Fe), zinc (Zn)-silt, clay and sand, stone content, bulk density and depth to bedrock, at three depths (0, 20 and 50 cm) and using 2-scale 3D Ensemble Machine Learning framework implemented in the mlr (Machine Learning in R) package. As covariate layers we used 250 m resolution (MODIS, PROBA-V and SM2RAIN products), and 30 m resolution (Sentinel-2, Landsat and DTM derivatives) images. Our fivefold spatial Cross-Validation results showed varying accuracy levels ranging from the best performing soil pH (CCC = 0.900) to more poorly predictable extractable phosphorus (CCC = 0.654) and sulphur (CCC = 0.708) and depth to bedrock. Sentinel-2 bands SWIR (B11, B12), NIR (B09, B8A), Landsat SWIR bands, and vertical depth derived from 30 m resolution DTM, were the overall most important 30 m resolution covariates. Climatic data images-SM2RAIN, bioclimatic variables and MODIS Land Surface Temperature-however, remained as the overall most important variables for predicting soil chemical variables at continental scale. This publicly available 30-m Soil Information System of Africa aims at supporting numerous applications, including soil and fertilizer policies and investments, agronomic advice to close yield gaps, environmental programs, or targeting of nutrition interventions.

Metabolic flux from the chloroplast provides signals controlling photosynthetic acclimation to cold in Arabidopsis thaliana.

Photosynthesis is especially sensitive to environmental conditions, and the composition of the photosynthetic apparatus can be modulated in response to environmental change, a process termed photosynthetic acclimation. Previously, we identified a role for a cytosolic fumarase, FUM2 in acclimation to low temperature in Arabidopsis thaliana. Mutant lines lacking FUM2 were unable to acclimate their photosynthetic apparatus to cold. Here, using gas exchange measurements and metabolite assays of acclimating and non-acclimating plants, we show that acclimation to low temperature results in a change in the distribution of photosynthetically fixed carbon to different storage pools during the day. Proteomic analysis of wild-type Col-0 Arabidopsis and of a fum2 mutant, which was unable to acclimate to cold, indicates that extensive changes occurring in response to cold are affected in the mutant. Metabolic and proteomic data were used to parameterize metabolic models. Using an approach called flux sampling, we show how the relative export of triose phosphate and 3-phosphoglycerate provides a signal of the chloroplast redox state that could underlie photosynthetic acclimation to cold.

Dynamic Acclimation to High Light in Arabidopsis thaliana Involves Widespread Reengineering of the Leaf Proteome.

Leaves of Arabidopsis thaliana transferred from low to high light increase their capacity for photosynthesis, a process of dynamic acclimation. A mutant, gpt2, lacking a chloroplast glucose-6-phosphate/phosphate translocator, is deficient in its ability to acclimate to increased light. Here, we have used a label-free proteomics approach, to perform relative quantitation of 1993 proteins from Arabidopsis wild type and gpt2 leaves exposed to increased light. Data are available via ProteomeXchange with identifier PXD006598. Acclimation to light is shown to involve increases in electron transport and carbon metabolism but no change in the abundance of photosynthetic reaction centers. The gpt2 mutant shows a similar increase in total protein content to wild type but differences in the extent of change of certain proteins, including in the relative abundance of the cytochrome b6f complex and plastocyanin, the thylakoid ATPase and selected Benson-Calvin cycle enzymes. Changes in leaf metabolite content as plants acclimate can be explained by changes in the abundance of enzymes involved in metabolism, which were reduced in gpt2 in some cases. Plants of gpt2 invest more in stress-related proteins, suggesting that their reduced ability to acclimate photosynthetic capacity results in increased stress.

FUM2, a Cytosolic Fumarase, Is Essential for Acclimation to Low Temperature in Arabidopsis thaliana.

Although cold acclimation is a key process in plants from temperate climates, the mechanisms sensing low temperature remain obscure. Here, we show that the accumulation of the organic acid fumaric acid, mediated by the cytosolic fumarase FUM2, is essential for cold acclimation of metabolism in the cold-tolerant model species Arabidopsis (Arabidopsis thaliana). A nontargeted metabolomic approach, using gas chromatography-mass spectrometry, identifies fumarate as a key component of the cold response in this species. Plants of T-DNA insertion mutants, lacking FUM2, show marked differences in their response to cold, with contrasting responses both in terms of metabolite concentrations and gene expression. The fum2 plants accumulated higher concentrations of phosphorylated sugar intermediates and of starch and malate. Transcripts for proteins involved in photosynthesis were markedly down-regulated in fum2.2 but not in wild-type Columbia-0. Plants of fum2 show a complete loss of the ability to acclimate photosynthesis to low temperature. We conclude that fumarate accumulation plays an essential role in low temperature sensing in Arabidopsis, either indirectly modulating metabolic or redox signals or possibly being itself directly involved in cold sensing.