Considering the importance of organic farming and growing demand for organically produced foods, field studies were conducted for 5 years (2004-05 to 2009-10) on a black clayey vertisol soil at the Directorate of Rice Research, Hyderabad, to study the influence of organic and conventional farming systems on productivity, grain quality, soil health and economic returns of super fine rice varieties. Two main plot treatments, with and without plant protection, and four sub plot treatments viz., Control; 100% inorganics; 100% organics; and 50% inorganics+50% organics (integrated nutrient management, INM) were imposed. During wet season, grain yields under 100% inorganics and INM were near stable (4.7-5.5 t/ha) and superior to organics by 15-20% during the first two years, which improved with organics (4.8-5.2 t/ha) in the later years to comparable levels with inorganics, while it had taken five years during dry season. Moderate improvement in nutritional quality was recorded with organics, especially in brown rice. There was a significant improvement in soil physical, fertility and biological properties with organics, which resulted in further improvement in soil quality indices. The sustainability index of the soil was maximum with organics (1.63) compared to inorganics (1.33), after five years of study. The soil organic carbon (SOC) stocks were higher with organics by 44 and 35%, compared to conventional system during wet and dry seasons, respectively, after five years of study. The carbon sequestration rate was also positive with organics (0.97 and 0.57 t/ha/yr during wet and dry seasons, respectively), compared to conventional system that recorded negative SOC sequestration rate (-0.21 and-0.33 t/ha/yr during wet and dry seasons, respectively). Benefit cost ratio was less with organics in the initial years and improved later over inorganics by fifth year.

Plant growth promoting rhizobacteria are key to soil and plant health maintenance. In the present study, two PGPR strains which were identified as spp. (accession number KX650178 and KX650179) with nanozeolite (50 ppm) were applied to the seeds in different combinations and tested on growth profile of maize crop. Various growth related parameters, including plant height, leaf area, number of leaves chlorophyll and total protein were positively increased up to twofold by the nanocompound treatment. GC-MS results reveal increase in total phenolic and acid ester compounds after the treatment of nanozeolite and PGPR, which are responsible for stress tolerance mechanism. Soil physicochemical parameters (organic carbon, phosphorous, potassium, ammoniacal nitrogen and nitrate nitrogen) were assessed qualitatively and a shift towards higher amount was observed. Various biochemical parameters of soil health like dehydrogenase, fluorescein diacetate hydrolysis and alkaline phosphatase activity were significantly enhanced up to threefold with the application of different treatments. The results, for the first time, demonstrate successful use of nanozeolite in enhancing growth of, under controlled conditions and present a viable alternative to GM crop for ensuring food security.

In the present study, estimating pan evaporation (Epan) was evaluated based on different input parameters: maximum and minimum temperatures, relative humidity, wind speed, and bright sunshine hours. The techniques used for estimating Epan were the artificial neural network (ANN), wavelet-based ANN (WANN), radial function-based support vector machine (SVM-RF), linear function-based SVM (SVM-LF), and multi-linear regression (MLR) models. The proposed models were trained and tested in three different scenarios (Scenario 1, Scenario 2, and Scenario 3) utilizing different percentages of data points. Scenario 1 includes 60%: 40%, Scenario 2 includes 70%: 30%, and Scenario 3 includes 80%: 20% accounting for the training and testing dataset, respectively. The various statistical tools such as Pearson’s correlation coefficient (PCC), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), and Willmott Index (WI) were used to evaluate the performance of the models. The graphical representation, such as a line diagram, scatter plot, and the Taylor diagram, were also used to evaluate the proposed model’s performance. The model results showed that the SVM-RF model’s performance is superior to other proposed models in all three scenarios. The most accurate values of PCC, RMSE, NSE, and WI were found to be 0.607, 1.349, 0.183, and 0.749, respectively, for the SVM-RF model during Scenario 1 (60%: 40% training: testing) among all scenarios. This showed that with an increase in the sample set for training, the testing data would show a less accurate modeled result. Thus, the evolved models produce comparatively better outcomes and foster decision-making for water managers and planners.

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Status of water stress in IndiaIN India rainfed agro-ecologies contribute 60% of the net sown area, 100% of the forest and 66% of the livestock. About 84-87% of pulses and minor millets, 80% of horticulture, 77% of oilseeds, 66% of cotton and 50% of cereals are cultivated under this region 1 . The area under dryland condition is 85 m ha (60% of total cultivated area), which receives average annual rainfall less than 1150 mm. Also, more than 30% of total geographical area of the country comes under low rainfall (less than 750 mm). About 84 districts in India fall in the category of low rainfall area.India ranks 41st among 181 countries with regard to water stress, with average score of 4.2 on the 0-5 scale system. (Water stress measures how much water is withdrawn every year from rivers, streams and shallow aquifers for domestic, agricultural and industrial uses. Scores above 4 on a scale of 0-5 indicate that, for the average water user, more than 80% of the water available is withdrawn annually.) The 4.2 score indicates that India is in the high risk zone with regard to water stress 2 .