Modelling the impact of different irrigation regimes and mulching on strawberry crop growth and water use in the arsenic-contaminated Bengal basin.

Replacement of water-intensive winter rice with strawberry (Fragaria × ananassa Duch.) may restrict groundwater extraction and improve water productivity and sustainability of agricultural production in the arsenic-contaminated Bengal basin. The potential of strawberry cultivation in terms of yield obtained and water use efficiency need to be evaluated under predominant soil types with mulch applications. Water-driven model AquaCrop was used to predict the canopy cover, soil water storage and above-ground biomass of strawberry in an arsenic-contaminated area in the Bengal basin. After successful calibration and validation over three seasons, AquaCrop was used over a range of management scenarios (nine drip-irrigation × three soil types × four mulch materials) to identify the best irrigation options for a drip-irrigated strawberry crop. The most appropriate irrigation of 176 mm for clay loam soil in lowland and 189 mm for sandy clay loam in medium land rice areas and the use of organic mulch from locally available jute agrotextile improved 1.4 times higher yield and 1.7 times higher water productivity than that of without mulch. Strawberry can be introduced as an alternative crop replacing rice in non-traditional upland and medium land areas of the arsenic-contaminated Bengal basin with 88% lower groundwater extraction load and better economic return to farmers.

Critical weather limits for paddy rice under diverse ecosystems of India.

Rice yields are largely influenced by variability in weather. Here, we demonstrate the effect of weather variables viz., maximum and minimum temperatures, rainfall, morning and evening relative humidity, bright sunshine hours on the yield of rice cv. Swarna, grown across five rice ecologies of India through field experiments during kharif (wet) season (Jun-Sept.). Critical thresholds of weather elements were identified for achieving above average, average and below average yield for each ecology. The investigation could determine how different weather elements individually and collectively affect rice yield in different rice ecosystems of India. While a sudden increase in minimum temperature by 8-10 °C (> 30 °C) during reproductive period resulted in 40-50 per cent yield reduction at Mohanpur, a sudden decrease (< 20 °C) caused yield decline at Dapoli. The higher yields may be attributed to a significant difference in bright sunshine hours between reproductive phases of above-average and below-average yield years (ranging from 2.8 to 7.8 hours during P5 stages and 1.7 to 5.1 during P4 stages). Rice cultivar Swarna performed differently at various sowing dates in a location as well as across locations (6650 kg ha-1 at Dapoli to 1101 kg ha-1 at Samastipur). It was also found that across all locations, the above average yield could be associated with higher range of maximum temperature compared to that of below average yield. Principal component analysis explained 77 per cent of cumulative variance among the variables at first growth stage, whereas 70 per cent at second growth stage followed by 74 per cent and 66 per cent at subsequent growth stages. We found that coastal locations, in contrast to inland ones, could maximize the yield potential of the cultivar Swarna, due to the longer duration of days between panicle initiation to physiological maturity. We anticipate that the location-specific thresholds of weather factors will encourage rice production techniques that are climate resilient.

Machine Learning approach to Predict net radiation over crop surfaces from global solar radiation and canopy temperature data.

As the ground-based instruments for measuring net radiation are costly and need to be handled skillfully, the net radiation data at spatial and temporal scales over Indian subcontinent are scanty. Sometimes, it is necessary to use other meteorological parameters to estimate the value of net radiation, although the prediction may vary based on season, ground cover and estimation method. In this context, artificial intelligence can be used as a powerful tool for predicting the data considering past observed data. This paper proposes a novel method to predict the net radiation for five crop surfaces using global solar radiation and canopy temperature. This contribution includes the generation of real-time data for five crops grown in West Bengal state of India. After manual analysis and data preprocessing, data normalization has been done before applying machine learning approaches for training a robust model. We have presented the comparison in various machine learning algorithm such as ridge and spline regression, random forest, ensemble and deep neural networks. The result shows that the gradient boosting regression and ridge regression are outperforming other ML approaches. The estimated predictors enable to reduce the number of resources in terms of time, cost and manpower for proper net radiation estimation. Thus, the problem of predicting net radiation over various crop surfaces can be sorted out through ML algorithm.

Seed priming with selenium and zinc nanoparticles modifies germination, growth, and yield of direct-seeded rice (Oryza sativa L.).

Direct-seeded rice (DSR) seeds are often exposed to multiple environmental stresses in the field, leading to poor emergence, growth and productivity. Appropriate seed priming agents may help to overcome these challenges by ensuring uniform seed germination, and better seedling stand establishment. To examine the effectiveness of sodium selenite (Na-selenite), sodium selenate (Na-selenate), zinc oxide nanoparticles (ZnO-NPs), and their combinations as priming agents for DSR seeds, a controlled pot experiment followed by a field experiment over two consecutive years was conducted on a sandy clay loam soil (Inceptisol) in West Bengal, India. Priming with combinations of all priming agents had advantages over the hydro-priming treatment (control). All the combinations of the three priming agents resulted in the early emergence of seedlings with improved vigour. In the field experiment, all the combinations increased the plant chlorophyll, phenol and protein contents, leaf area index and duration, crop growth rate, uptake of nutrients (N, P, K, B, Zn and Si), and yield of DSR over the control. Our findings suggest that seed priming with the combination of ZnO-NPs, Na-selenite, and Na-selenate could be a viable option for the risk mitigation in DSR.

Foliar Spray of Micronutrients Alleviates Heat and Moisture Stress in Lentil (Lens culinaris Medik) Grown Under Rainfed Field Conditions.

The simultaneous occurrence of high temperature and moisture stress during the reproductive stage of lentil (Lens culinaris Medik) constrains yield potential by disrupting the plant defense system. We studied the detrimental outcomes of heat and moisture stress on rainfed lentils under residual moisture in a field experiment conducted on clay loam soil (Aeric Haplaquept) in eastern India from 2018 to 2019 and from 2019 to 2020 in winter seasons. Lentil was sown on two dates (November and December) to expose the later sowing to higher temperatures and moisture stress. Foliar sprays of boron (0.2% B), zinc (0.5% Zn), and iron (0.5% Fe) were applied individually or in combination at the pre-flowering and pod development stages. High temperatures increased malondialdehyde (MDA) content due to membrane degradation and reduced leaf chlorophyll content, net photosynthetic rate, stomatal conductance, water potential, and yield (kg ha-1). The nutrient treatments affected the growth and physiology of stressed lentil plants. The B+Fe treatment outperformed the other nutrient treatments for both sowing dates, increasing peroxidase (POX) and ascorbate peroxidase (APX) activities, chlorophyll content, net photosynthetic rate, stomatal conductance, relative leaf water content (RLWC), seed filling duration, seed growth rate, and yield per hectare. The B+Fe treatment increased seed yield by 35-38% in late-sown lentils (December). In addition, the micronutrient treatments positively impacted physiological responses under heat and moisture stress with B+Fe and B+Fe+Zn alleviating heat and moisture stress-induced perturbations. Moreover, the exogenous nutrients helped in improving physiochemical attributes, such as chlorophyll content, net photosynthetic rate, stomatal conductance, water potential, seed filling duration, and seed growth rate.

Evaluating the long-term impact of projected climate on rice-lentil-groundnut cropping system in Lower Gangetic Plain of India using crop simulation modelling.

Most simulations of food production in response to various climates to date have used simulations of the same crop over multiple years. This study evaluated the impact of projected climate on performance of rice-lentil-groundnut cropping sequence in New Alluvial Zone of West Bengal, India, using DSSAT model. The study period consisted of baseline (1980-2010), mid-century (2040-2069) and end-century (2070-2099). Advancement in days to anthesis (2-13 days) was simulated for rice during the future periods. For lentil and groundnut, average advancement in days to anthesis was 1 day. Days to maturity were shortened by 3-16 days for rice and 0-7 days for lentil. Nevertheless, for groundnut, the days to maturity were simulated to increase by 1-9 days. The impact on final biomass and yield was simulated with and without CO2 fertilization, and the positive impact of CO2 fertilization was prominent for all the three crops. When CO2 fertilization effect was considered, the yield of rice was projected to increase by 11-32%. On the other hand, yield of lentil and groundnut was estimated to change by - 31 to - 12% and - 33 to + 8%, respectively. Enhanced CO2 could mitigate the magnitude of yield reduction due to enhanced temperature. Rice was benefited due to the carryover effect of residue from preceding groundnut and, hence, could sustain the yield on a long term. The study could also quantify the uncertainty in simulation of yield due to selection of GCMs.

The Response of Lentil (Lens culinaris Medik.) to Soil Moisture and Heat Stress Under Different Dates of Sowing and Foliar Application of Micronutrients.

Soil moisture and air temperature stress are the two major abiotic factors limiting lentil (Lens culinaris Medik.) growth and productivity in the humid tropics. Field experiments were conducted during winter seasons (November to March) of 2018-2019 and 2019-2020 on clay loam soil (AericHaplaquept) of Eastern India to cultivate rainfed lentil, with residual moisture. The objective was to study the effect of different time of sowing and foliar spray of micronutrients in ameliorating the effect of heat and moisture stress lentil crop experience in its reproductive stage. The study was conducted with two different dates of sowing, November and December, as main plot treatment and micronutrients foliar spray of boron, iron, and zinc either alone or in combination as subplot treatment. No foliar spray treatment was considered as a control. The soil moisture content is depleted from 38 to 18% (sowing to harvest) during November sowing; however, in December sowing, the depletion is from 30 to 15%. The foliar spray of micronutrients helped to have a better canopy cover and thus reduced soil evaporation during the later stages of crop growth when the temperature was beyond the threshold temperature of the crop. Crop growth rate (CGR) and biomass were significantly higher (p ≤ 0.05) for November sown crop and with foliar spray of boron and iron (FSB + FE) micronutrients. In the later stages of the crop when the soil moisture started depleting with no precipitation, the canopy temperature increased compared with air temperature, leading to positive values of Stress Degree Days (SDD) index. Delay in sowing reduced the duration by 11.4 days (113.5 vs. 102.1 days), resulting in varied accumulated Growing Degree Days (GDD). FSB + FE resulted in the highest yield in both years (1,436 and 1,439 kg ha-1). The results of the study concluded that the optimum time of sowing and foliar spray of micronutrients may be helpful to alleviate the soil moisture and heat stress for the sustainability of lentil production in the subtropical region.