Aims Phosphorus (P) deficiency is a major constraint for rice production in the tropics. Field-specific P management is key for resource-limited farmers to increase yields with minimal inputs. We used soil P fractionation analysis to identify the relevant factors controlling P uptake and the responses to P fertilization of rice in flooded and highly weathered soils. Methods Phytometric pot-based experiments and a modified Hedley fractionation analysis were repeated for soils from extensive regions and from geographically adjacent fields in Madagascar. Results Large field-to-field variations in indigenous P supply from soils (total P uptake of rice when P is omitted) and fertilizer-P recovery efficiencies (increased P uptake when P is applied) were observed not only for soils with various geological backgrounds but also for soils from adjacent fields. Regression models indicated that the indigenous P supply in soils was largely controlled by readily available inorganic and organic P pools (r 2 = 0.72), whereas fertilizer-P recovery efficiencies were controlled by the abundance of oxalateextractable aluminum and iron in soils (r 2 = 0.81). Conclusions Spatial heterogeneity even within adjacent fields leads to benefits from field-specific fertilizer management based on indigenous P supply from soils and fertilizer-P recovery efficiencies evaluated by different soil properties.
As a laboratory proximal sensing technique, the capability of visible and near-infrared (Vis-NIR) diffused reflectance spectroscopy with partial least squares (PLS) regression to determine soil properties has previously been demonstrated. However, the evaluation of the soil phosphorus (P) content—a major nutrient constraint for crop production in the tropics—is still a challenging task. PLS regression with waveband selection can improve the predictive ability of a calibration model, and a genetic algorithm (GA) has been widely applied as a suitable method for selecting wavebands in laboratory calibrations. To develop a laboratory-based proximal sensing method, this study investigated the potential to use GA-PLS regression analyses to estimate oxalate-extractable P in upland and lowland soils from laboratory Vis-NIR reflectance data. In terms of predictive ability, GA-PLS regression was compared with iterative stepwise elimination PLS (ISE-PLS) regression and standard full-spectrum PLS (FS-PLS) regression using soil samples collected in 2015 and 2016 from the surface of upland and lowland rice fields in Madagascar (n = 103). Overall, the GA-PLS model using first derivative reflectance (FDR) had the best predictive accuracy (R2 = 0.796) with a good prediction ability (residual predictive deviation (RPD) = 2.211). Selected wavebands in the GA-PLS model did not perfectly match wavelengths of previously known absorption features of soil nutrients, but in most cases, the selected wavebands were within 20 nm of previously known wavelength regions. Bootstrap procedures (N = 10,000 times) using selected wavebands also confirmed the improvements in accuracy and robustness of the GA-PLS model compared to those of the ISE-PLS and FS-PLS models. These results suggest that soil oxalate-extractable P can be predicted from Vis-NIR spectroscopy and that GA-PLS regression has the advantage of tuning optimum bands for PLS regression, contributing to a better predictive ability.
Water erosion is one of the main concerns driving land degradation in mountainous areas throughout the world, and its characteristics change widely with soil and climate conditions in different locations. To investigate the effects of soil and rainfall properties on surface runoff and soil loss, we installed runoff plots (width 0·8 m × slope length 2·4 m) enclosed by corrugated iron sheets, and evaluated water budgets for rainfall, surface runoff and soil moisture for every rainfall event over a rainy season at four sites (designated NY, TA, SO and MA) in the Uluguru Mountains of Tanzania. Two of the sites, NY and TA, were located in mountainous areas and had steep slopes and higher rainfall amounts, while the two foothill sites, SO and MA, were flatter and with coarser textures. Runoff amount was related to rainfall amount, but also to infiltration capacity. Runoff amounts in mountainous areas were higher than in foothill sites while runoff ratio was low at sites with high permeability in the surface layer, TA and MA. Sediment concentrations and soil loss were basically enhanced by high rainfall amount and intensity at mountainous sites. Soil texture also controlled the erodibility of soils; MA had twice the soil loss of SO owing to sandy soil despite similar rainfall amounts at MA and SO. Our results show that the high rainfall amounts in the mountainous areas and high susceptibility of sandy soils to erosion enhanced soil loss by water erosion in this study.
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.
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