Soil color originates mainly from organic matter, iron mineralogy and moisture content. We aimed to find a suitable method to measure soil color sensitively and to evaluate the extent to which the color parameters can be useful for statistically estimating total carbon (C), total nitrogen (N) and active iron (Fe) contents in Japanese agricultural soils. A soil color reader (SPAD-503) was applied to two sample sets: (1) 100 surface soils collected throughout a 0.5-ha paddy field (field scale) and (2) 147 surface soils collected from agricultural fields in Japan (national scale). For analysis with this instrument, about 2 g of air-dried, finely-ground samples were packed firmly in a plastic cell, and their colors as they appeared on windows in both sides of the cell were measured. A CIE 1976 (L*, a*, b*) color space was used for color description. For the field-scale samples, the values of the coefficient of variation were around 15% for total C, total N and acid oxalate extractable iron (Fe_o). The L* value (lightness) was negatively correlated with the content of total C and total N (R² = 0.18** and 0.26**, respectively), and the b* value (yellowness) was positively correlated with the Fe_o content (R² = 0.59**). For the national-scale samples, the values of the coefficient of variation were around 60% for total C, total N and Fe_o. The L* value was negatively correlated with the content of total C and total N (R² = 0.70** and 0.59**, respectively), but the b* value was not correlated with the content of Fe_o (R² = 0.00). When the analysis was limited to 65 samples frequently used for paddy fields, the b* value was positively correlated with the Fe_o content (R² = 0.52**). In conclusion, the proposed method enabled us to measure soil color sensitively with a small sample size. The L* and b* values obtained can be useful for rapid estimation of total C, total N and Fe_o contents in agricultural surface soils in Japan.

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.

Soil color has been conventionally measured using a Munsell soil color chart. Recently launched colorimeters can also measure the color of an object at a reasonable cost. This study was undertaken to evaluate to what extent such low-cost colorimeters (< 500 USD) can be useful for soil color analysis in the laboratory as compared with conventional colorimeters (> 3000 USD) and a Munsell soil color chart (about 200 USD). Sixty two air-dried soil samples collected from rice fields in Madagascar were subjected to two pretreatments for homogenization (2-mm sieving or additional hand-grinding) and instrumental analysis using four low-cost colorimeters (CS-10, Cube, Nix Pro, and Color Muse) and four conventional colorimeters (SPAD-503, CR-20, CR-400, and CR-410). The color of 2-mm sieved samples was also measured visually using a color chart. The effects of pretreatments and the analytical conditions were evaluated by the repeatability and stability of the measurement, the comparability of the soil color data obtained, and the time required for analysis. Overall, instrumental measurement was much more repeatable than visual observation. Both the repeatability and stability of the low-cost colorimeters tended to be lower than those of the conventional colorimeters. Among the low-cost colorimeters examined, soil color parameters (L*, a*, and b*) obtained with Nix Pro were most comparable (r > 0.97 for all parameters of 2-mm sieved samples) with those obtained with SPAD-503, which is an instrument designed specifically for soil color analysis. Additional hand-grinding pretreatment improved the repeatability of the instrumental analysis and reduced the subsample weight to two grams. However, this pretreatment also increased the L* value (lightness) of the samples, decreased the comparability with the data from 2-mm sieved samples, and prolonged the time required to complete the whole analysis. Among the various methods tested, 2-mm sieving of air-dried samples followed by the color measurement with Nix Pro several times per sample was considered the most cost-effective approach for measuring soil color in the laboratory.

To understand how soil color is influenced by soil components at the farm scale, we evaluated spatial variation in soil color and related soil properties in Japanese paddy fields. After harvest of rice, 246 surface soil samples were collected in 10-m grids from five contiguous irrigated paddy fields, each with an area of about 0.5 ha. The samples were analyzed to determine color parameters (L*, a*, and b*), and contents of total C, total N, Fe oxides, sand, and loss-on-ignition. The results obtained were modeled and mapped geostatistically. All color parameters indicated strong spatial dependence with long ranges (>85 m). In contrast, total C and N showed short ranges (about 40 m). The contents of Fe oxides, sand, and loss-on-ignition showed intermediate ranges (50–85 m). The ranges of these properties and their distribution patterns suggested that the contents of total C and N were influenced by long-term application of manure and that sand content was influenced by topography and past land consolidation. Further soil color analysis after removal of organic matter or silt plus clay particles revealed that soil organic matter, texture, and Fe oxides affected soil color parameters in a complex manner. Prediction of total C from soil darkness was hindered by the presence of silt plus clay particles containing Fe oxides. On the other hand, citrate-dithionite extractable Fe was estimated accurately from the b* value (yellowness), which can be useful for predicting the occurrence of akiochi (autumn decline) disease of rice at the farm scale.

The effect of soil heating on the dynamics of soil available nutrients in the rhizosphere was evaluated. A pot experiment was carried out by using a rhizobox; a pot which enables to sample soils and soil solutions not only temporally with plant growth but also spatially depending on the distance from the root-accumulating compartment. The experiment consisted of 4 treatments; soils with or without heating treatment (150°C, 3 h), each of which was either planted with maize (Zea mays L.) or not. During the 17-d experiment, soil solutions at 0–2 mm from the root-accumulating compartment were collected 5 times. Soils depending on the distance from the root-accumulating compartment and plants were also collected after the experiment. The ionic concentrations of the soil solutions and soil water extracts, and the nutrient contents of plants were analyzed. Immediately after soil heating, the concentrations of cations, SO₄ ^2-, CI^-, water-soluble P, and water-soluble organic carbon increased significantly. With plant growth, the total ionic concentration in the rhizosphere soil solution increased for heated soil, whereas it decreased for unheated soil. The increase of the concentrations of cations and SO₄ ^2- in the rhizosphere of heated soil was appreciable, suggesting that the movement of cations such as Ca²⁺ and Mg²⁺ by mass flow was regulated by that of SO₄ ^2-. Moreover soil heating inhibited nitrification, resulting in the supply of N mainly in the form of NH₄ ⁺ within 10 mm from the root-accumulating compartment. As a result, the soil pH decreased in the rhizosphere of heated soil. The amount of nutrients absorbed by plants, on the other hand, did not change significantly by soil heating except for an increase of P uptake. The increase of P uptake could be explained not only by the immediate increase of the water-soluble P concentration but also by the dissolution of Ca-bound P and the hydrolysis of water-soluble organic P in the rhizosphere.