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Soil moisture stress during pod filling is a major constraint in production of chickpea (Cicer arietinum L.), a fundamentally dry land crop. We investigated effect of pre-sowing seed priming with static magnetic field (SMF) on alleviation of stress through improvement in radiation and water use efficiencies. Experiments were conducted under greenhouse and open field conditions with desi and kabuli genotypes. Seeds exposed to SMF (strength: 100 mT, exposure: 1 h) led to increase in root volume and surface area by 70% and 65%, respectively. This enabled the crop to utilize 60% higher moisture during the active growth period (78-118 days after sowing), when soil moisture became limiting. Both genotypes from treated seeds had better water utilization, biomass, and radiation use efficiencies (17%, 40%, and 26% over control). Seed pre-treatment with SMF could, therefore, be a viable option for chickpea to alleviate soil moisture stress in arid and semi-arid regions, helping in augmenting its production. It could be a viable option to improve growth and yield of chickpea under deficit soil moisture condition, as the selection and breeding program takes a decade before a tolerant variety is released. Bioelectromagnetics. 37:400-408, 2016. © 2016 Wiley Periodicals, Inc.

Even though research shows that aggregate stability and mean weight diameter (MWD) are critical components of soil health, it is not routinely measured. An alternative approach to the physical measurement is to calculate these values based on routinely measured soil parameters. Therefore, the objective was to compare two artificial intelligence (AI)-based machine learning approaches, that is, support vector machine (SVM) and artificial neural network (ANN) models in prediction of soil wet aggregate stability (quantified by MWD). Soil samples (120) from the Indo-Gangetic Alluvium major soil group, that are characterized as Ustifluvents were used in the study. These samples were analyzed for sand, silt, clay, bulk density (BD), organic carbon (OC), and MWD. The correlation coefficient (r) was highest in case of SVM model with a percentage increase of 16.92 and 2.70 when compared with MLR and ANN respectively. The SVM and ANN models showed 6.36 and 2.12% decrease in RMSE in training dataset while a 14.28% decrease was found for SVM in testing dataset when compared to the multi-linear regression (MLR) model. Results showed that ANN with two neurons (building blocks of ANN) in hidden layer had better performance in predicting MWD than MLR, whereas the radial basis Kernel function based SVM was found to be best for training and testing data of MWD. Soil texture, OC, and BD can be used to predict soil structural stability effectively using SVM. However, additional work is needed to confirm these findings with other soils.