Influence of meteorological parameters on wheat yield under different sowing conditions

Field experiments were conducted on the research farm of IARI, New Delhi during Rabi 2016-17 and 2017-18. Three varieties of wheat (PBW-723, HD-2967 and HD-3086) were sown on three different dates for generating different weather condition during various phenological stages of crop. Results showed that during early crop growth stages soil moisture had higher value and soil temperature had lower value and with progress of crop growth stage, the moisture in the upper layer decreased and soil temperature increased significantly as compared to the bottom layers. During tillering and jointing stage, air temperature within canopy was more and relative humidity was less while during flowering and grain filling stage, air temperature within canopy was less and relative humidity was more in timely sown crop as compared to late and very late sown crop. Radiation use efficiency and relative leaf water content had significantly higher value while leaf water potential had lower value in timely sown crop followed by late and very late sown crop. Yield had higher value in HD-3086 followed by HD-2967 and PBW-723 in all weather conditions. Canopy air temperature difference had positive value in very late sown crop particularly during flowering and grain-filling stages. This reflects in the yield. Yield was more in timely sown crop as compared to late and very late sown crop.

Effect of projected climate scenarios on the yields of potato crop and agronomic adaptation options as evaluated by crop growth model

Potato is the most important non-cereal crop in the world and the most prominent winter season crop in India. Growth and yield of potato crop is very much sensitive to higher temperatures and the moisture stress. Hence, the anticipated increase of temperature due to global warming and climatic variability will have anadverse impact on potato production. Keeping this in view, a research work was carried out with the objectives to assess the impact of climate change on potato production and evaluating agronomic adaptation options through a crop growth simulation model (CGSM). Field experiments were carried out to prepare the minimum dataset for calibration and validation of one CGSM, namely InfoCrop. After validation, the model was used to predict the future tuber yield of ten selected stations situated under different agroclimatic regions of the State. In the future scenario 2050, the simulated yield for mid November planted crop likely to be about 11% less than the present level of mean yield. If the crop is planted in December, the percentage of yield reduction may be around 25%.The projected yield reduction, for the stations of higher latitude, is found to be negligible. Three possible agronomic adaptation options, viz., adjustment of date of planting, increase of seed rate and varying sprout length of seed tubers, have been tried as adaptation strategies to combat the adverse effects of climate change. It is concluded that the mid-November planting and longer sprout length will be the best adaptation option. However, the enhanced seed rate is not a viable adaptation option.

Evaluation of dynamic simulation model for wheat genotypes under diverse environments in India

Performance of dynamic crop growth simulation model (CERES -Wheat v3.5) has been evaluated for various wheat genotypes in wheat growing regions of India. The genetic coefficients were developed and sensitivity analysis was carried out for the genotypes under study. The simulated phenology and yield were found in agreement with observed ones suggesting that calibrated model may be operationally used with routinely observed soil, crop and weather parameters.

Mechanism of Intermittent Deep Tillage and Different Depths Improving Crop Growth From the Perspective of Rhizosphere Soil Nutrients, Root System Architectures, Bacterial Communities, and Functional Profiles

Long-term conventional shallow tillage reduced soil quality and limited the agriculture development. Intermittent deep tillage could effectively promote agricultural production, through optimizing soil structure, underground ecology system, and soil fertility. However, the microecological mechanism of intermittent deep tillage promoting agriculture production has never been reported, and the effect of tillage depth on crop growth has not been explored in detail. In this study, three levels of intermittent deep tillage (30, 40, and 50 cm) treatments were conducted in an experimental field site with over 10 years of conventional shallow tillage (20 cm). Our results indicated that intermittent deep tillage practices helped to improve plant physiological growth status, chlorophyll a, and resistance to diseases, and the crop yield and value of output were increased with the deeper tillage practices. Crop yield (18.59%) and value of output (37.03%) were highest in IDT-50. There were three mechanisms of intermittent deep tillage practices that improved crop growth: (1) Intermittent deep tillage practices increased soil nutrients and root system architecture traits, which improved the fertility and nutrient uptake of crop through root system. (2) Changing rhizosphere environments, especially for root length, root tips, pH, and available potassium contributed to dissimilarity of bacterial communities and enriched plant growth-promoting species. (3) Functions associated with stress tolerance, including signal transduction and biosynthesis of other secondary metabolites were increased significantly in intermittent deep tillage treatments. Moreover, IDT-30 only increased soil characters and root system architecture traits compared with CK, but deeper tillage could also change rhizosphere bacterial communities and functional profiles. Plant height and stem girth in IDT-40 and IDT-50 were higher compared with IDT-30, and infection rates of black shank and black root rot in IDT-50 were even lower in IDT-40. The study provided a comprehensive explanation into the effects of intermittent deep tillage in plant production and suggested an optimal depth.

Cold Plasma as a Potential Activator of Plant Biostimulants

Agriculture has become a sector with a huge impact on the natural environment. The interest of agriculture in the category of innovative bio-stimulants is due to the intensive search for preparations based on natural substances. This is not possible without developing and implementing innovative technologies, e.g., cold plasma, along with innovative technologies supporting farmers. Therefore, given the need to prevent environmental damage caused by intensive agriculture, plant production and protection must be targeted at merging the stimulation of crop growth and the elimination of threats to humans and the environment. The analysis of how cold plasma can influence the production of organic bio-stimulants seems to be an unavoidable step in future approaches to this topic. Since allelopathic plants represent a source of many chemical compounds promoting crop growth and development, the coupling of biologically-active compound extraction with plasma activation of allelopathic extracts has interesting potential in offering the most modern alternative to conventional agriculture. However, its implementation in practice will only be feasible after a comprehensive and thoughtful investigation of the mechanisms behind crops’ response to such bio-stimulants.

Estimation of Cotton Leaf Area Index (LAI) Based on Spectral Transformation and Vegetation Index

Unmanned aerial vehicles (UAV) has been increasingly applied to crop growth monitoring due to their advantages, such as their rapid and repetitive capture ability, high resolution, and low cost. LAI is an important parameter for evaluating crop canopy structure and growth without damage. Accurate monitoring of cotton LAI has guiding significance for nutritional diagnosis and the accurate fertilization of cotton. This study aimed to obtain hyperspectral images of the cotton canopy using a UAV carrying a hyperspectral sensor and to extract effective information to achieve cotton LAI monitoring. In this study, cotton field experiments with different nitrogen application levels and canopy spectral images of cotton at different growth stages were obtained using a UAV carrying hyperspectral sensors. Hyperspectral reflectance can directly reflect the characteristics of vegetation, and vegetation indices (VIs) can quantitatively describe the growth status of plants through the difference between vegetation in different band ranges and soil backgrounds. In this study, canopy spectral reflectance was extracted in order to reduce noise interference, separate overlapping samples, and highlight spectral features to perform spectral transformation; characteristic band screening was carried out; and VIs were constructed using a correlation coefficient matrix. Combined with canopy spectral reflectance and VIs, multiple stepwise regression (MSR) and extreme learning machine (ELM) were used to construct an LAI monitoring model of cotton during the whole growth period. The results show that, after spectral noise reduction, the bands screened by the successive projections algorithm (SPA) are too concentrated, while the sensitive bands screened by the shuffled frog leaping algorithm (SFLA) are evenly distributed. Secondly, the calculation of VIs after spectral noise reduction can improve the correlation between vegetation indices and LAI. The DVI (540,525) correlation was the largest after standard normal variable transformation (SNV) pretreatment, with a correlation coefficient of −0.7591. Thirdly, cotton LAI monitoring can be realized only based on spectral reflectance or VIs, and the ELM model constructed by calculating vegetation indices after SNV transformation had the best effect, with verification set R2 = 0.7408, RMSE = 1.5231, and rRMSE = 24.33%, Lastly, the ELM model based on SNV-SFLA-SNV-VIs had the best performance, with validation set R2 = 0.9066, RMSE = 0.9590, and rRMSE = 15.72%. The study results show that the UAV equipped with a hyperspectral sensor has broad prospects in the detection of crop growth index, and it can provide a theoretical basis for precise cotton field management and variable fertilization.