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As a proximal soil sensing technique, laboratory visible and near-infrared (Vis-NIR) spectroscopy is a promising tool for the quantitative estimation of soil properties. However, there remain challenges for predicting soil phosphorus (P) content and availability, which requires a reliable model applicable for different land-use systems to upscale. Recently, a one-dimensional convolutional neural network (1D-CNN) corresponding to the spectral information of soil was developed to considerably improve the accuracy of soil property predictions. The present study investigated the predictive ability of a 1D-CNN model to estimate soil available P (oxalate-extractable P; Pox) content in soils by comparing it with partial least squares (PLS) and random forest (RF) regressions using soil samples (n = 318) collected from natural (forest and non-forest) and cultivated (upland and flooded rice fields) systems in Madagascar. Overall, the 1D-CNN model showed the best predictive accuracy (R2 = 0.878) with a highly accurate prediction ability (ratio of performance to the interquartile range = 2.492). Compared to the PLS model, the RF and 1D-CNN models indicated 4.37% and 23.77% relative improvement in root mean squared error values, respectively. Based on a sensitivity analysis, the important wavebands for predicting soil Pox were associated with iron (Fe) oxide, organic matter (OM), and water absorption, which were previously known wavelength regions for estimating P in soil. These results suggest that 1D-CNN corresponding spectral signatures can be expected to significantly improve the predictive ability for estimating soil available P (Pox) from Vis-NIR spectral data. Rapid and accurate estimation of available P content in soils using our results can be expected to contribute to effective fertilizer management in agriculture and the sustainable management of ecosystems. However, the 1D-CNN model will require a large dataset to extend its applicability to other regions of Madagascar. Thus, further updates should be tested in future studies using larger datasets from a wide range of ecosystems in the tropics.

Madagascar is known for its high erosion rates in the central highlands, yet the role of human disturbance versus natural processes is not well understood and is a topic of ongoing debate. At present the necessary quantitative data to couple vegetation dynamics and sediment fluxes over time in Madagascar is scarce. This study aims to provide more insight in vegetation changes and sediment transfers over the last millennia in the Lake Alaotra region, and specifically on the role of human disturbances and natural processes. Our vegetation reconstruction is based on pollen records from two lake sediment cores, covering the last 2600 years. Sediment accumulation rates were calculated from cores obtained from the floodplains, from wetlands surrounding the lake, and from Lake Alaotra itself. Our data show an early opening in the landscape, between 2050 and 1700 cal a BP, with a transition from a wooded grassland or woodland/grassland mosaic towards open grassland and an increase in charcoal accumulation rates. (Indirect) human impact is suggested as the main driver for these vegetation changes. Floodplain and wetland sediment accumulation rates only increase in the last 1000 years and peak in the last 400 years. This increased accumulation can mainly be linked to the increased anthropogenic pressure (grazing and farming activities) that triggered increased lavaka (gullies) activity. No changes in accumulation rate were observed in Lake Alaotra, indicating that most sediments are buffered in the floodplains and wetlands. Overall, our pollen and charcoal data suggest an indirect effect of human disturbance on vegetation shifts whilst strong evidence was found for a direct effect of human disturbance on sediment accumulation through intensified use of the grasslands.

Upland rice production is limited by the low phosphorus (P) availability of many highly weathered tropical soils and P deficiency is likely to become increasingly limiting in future drier climates because P mobility decreases sharply with soil moisture. Good seedling root development will be crucial to cope with the combined effects of low P and water availability. Upland rice genebank accession DJ123 was used as a donor for P efficiency and root vigor traits in a cross with inefficient local variety Nerica4 and a set of backcross lines were used to characterize the seedling stage response of upland rice to low P availability and to identify associated QTL in field trials in Japan and Madagascar. Ten QTL were detected for crown root number, root, shoot and total dry weight per plant in a highly P deficient field in Japan using the BC1F3 generation. Of these, qPef9 on chromosome 9 affected multiple traits, increasing root number, root weight and total biomass, whereas a neighboring QTL on chromosome 9 (qPef9-2) increased shoot biomass. Field trials with derived BC1F5 lines in a low-P field in Madagascar confirmed a highly influential region on chromosome 9. However, qPef9-2 appeared more influential than qPef9, as the shoot and root biomass contrast between lines carrying DJ123 or Nerica4 alleles at qPef9-2 was +23.8% and +13.5% compared to +19.2% and +14.4% at qPef9. This advantage increased further during the growing season, leading to 46% higher shoot biomass at the late vegetative stage. Results suggest an introgression between 8.0 and 12.9 Mb on chromosome 9 from P efficient donor DJ123 can improve plant performance under P-limited conditions. The QTL identified here have practical relevance because they were confirmed in the target genetic background of the local variety Nerica4 and can therefore be applied directly to improve its performance.

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