Soil texture is a key soil property influencing many agronomic practices including fertilization and liming. Therefore, an accurate estimation of soil texture is essential for adopting sustainable soil management practices. In this study, we used different machine learning algorithms trained on vis–NIR spectra from existing soil spectral libraries (ICRAF and LUCAS) to predict soil textural fractions (sand–silt–clay %). In addition, we predicted the soil textural groups (G1: Fine, G2: Medium, and G3: Coarse) using routine chemical characteristics as auxiliary. With the ICRAF dataset, multilayer perceptron resulted in good predictions for sand and clay (R2 = 0.78 and 0.85, respectively) and categorical boosting outperformed the other algorithms (random forest, extreme gradient boosting, linear regression) for silt prediction (R2 = 0.81). For the LUCAS dataset, categorical boosting consistently showed a high performance for sand, silt, and clay predictions (R2 = 0.79, 0.76, and 0.85, respectively). Furthermore, the soil texture groups (G1, G2, and G3) were classified using the light gradient boosted machine algorithm with a high accuracy (83% and 84% for ICRAF and LUCAS, respectively). These results, using spectral data, are very promising for rapid diagnosis of soil texture and group in order to adjust agricultural practices.
Visible and near-infrared diffuse reflectance spectroscopy (VIS-NIR) has shown levels of accuracy comparable to conventional laboratory methods for estimating soil properties. Soil chemical and physical properties have been predicted by reflectance spectroscopy successfully on subtropical and temperate soils, whereas soils...
Sorghum crop is grown under tropical and temperate latitudes for several purposes including production of health promoting food from the kernel and forage and biofuels from aboveground biomass. One of the concerns of policy-makers and sorghum growers is to cost-effectively...
Grown in Jamaica since the days of slavery, food yams are major staples in local diets and a significant non-traditional export crop. The cultivation system used today is the same as 300 years ago, with alleged unsustainable practices. A new...
Se analizan los efectos de las interacciones, directas e indirectas, entre agricultores y otros actores relevantes en el intercambio de información y conocimiento para la innovación agrícola. Los datos se obtuvieron al preguntar a 120 agricultores «¿de quién aprende y/o...
This study explores the role of gender-based decision-making in the adoption of improved maize varieties. The primary data were collected in 2018 from 560 farm households in Dawuro Zone, Ethiopia, and were comparatively analyzed across gender categories of households: male...