Requirement of heat unit and agrometeorological indices in selected wheat growing zones

Wheat growing ET -stations (viz., Jorhat, Varanasi, New Delhi, Ludhiana, Raipur, Jabalpur, Akola, Bellary, Banswara and Jodhpur) situated in arid to per humid climatic zones were selected. Heat unit and three agromeleorological indices, viz., ARI (agroclimatic rainfall index), YMI (yield moisture index) and AI (aridity index) were computed at various growth stages of wheat crop using latest available five years data for each of the stations. The study revealed that the crop degree days requirement varied from 1580 (at Jorhat) to 2350 (at Akola) with the maximum requirement at tillering and milk stages. All the stations (except Jorhat) recorded ARI values less than 25%, while for the stations in peninsular and western India, the values were even below 10%. Low cumulative YMI values were obtained in peninsular and western India, while high values were observed over eastern India. The wheat crop did not experience any aridity during tillering to flowering stages for all the stations (except Bellary and Banswara). High values of At were observed at early and late crop growth stages. Negative correlation was obtained between AI and ARI with the highest value (-0.89) observed at New Delhi. Depending upon this study, the wheat growing areas were divided into five zones.

Simple and Two-Level Hierarchical Bayesian Approaches for Parameter Estimation with One- and Two-Layer Evapotranspiration Models of Crop Fields

The two-layer Shuttleworth–Wallace (SW) evapotranspiration (ET) model has been widely used for predicting ET with good results. Since the SW model has a large number of specific parameters, these parameters have been estimated using a simple non-hierarchical Bayesian (SB) approach. To further improve the performance of the SW model, we aimed to assess parameter estimation using a two-level hierarchical Bayesian (HB) approach that takes into account the variation in observed conditions through the comparison with a traditional one-layer Penman–Monteith (PM) model. The difference between the SB and HB approaches were evaluated using a field-based ET dataset collected from five agricultural fields over three seasons in Myanmar. For a calibration period with large variation in environmental factors, the models with parameters calibrated by the HB approach showed better fitting to observed ET than that with parameters estimated using the SB approach, indicating the potential importance of accounting for seasonal fluctuations and variation in crop growth stages. The validation of parameter estimation showed that the ET estimation of the SW model with calibrated parameters was superior to that of the PM model, and the SW model provided acceptable estimations of ET, with little difference between the SB and HB approaches.

Crop specific requirement of growing degree days and agrometeorological indices in rice growing zones

The spatial and temporal distributions of heat unit and various agrometeorological indices for the rice crop, are studied in this paper. Eight ET – stations were selected from six rice growing zones, viz., Canning (in lower Gangetic plains), Bikramganj and Varanasi (in middle Gangetic plains), Ludhiana (in trans Gangetic plains), Ranchi, Shymakhunta (in eastern plateau and hills), Annamalai Nagar (in east coast plains and hill region) and Pattambi (in western plains and ghat region). Eleven crop growth stages were identified for this study, viz., germination, nursery seedling, transplanting, tillering, active tillering, lag phase, panicle initiation, flowering, grain formation, grain maturity and harvesting, the duration of each of the growth stages varied widely, station wise. Daily data were collected growth stagewise for latest available five years and the mean values were computed for the derived parameters, viz., the crop requirements of heat unit, agroclimatic rainfall index (ARI), yield moisture index (YMI), aridity index (AI). The study revealed that for rice crop the total degree days requirement varied from 1706 degree – days (at Ranchi) to 2815 degree – days (at Shymakhunta). It showed primary peak (with 16.7 % of total requirement) at active tillering stage. The ARI values were mostly higher than 100 per cent. The mean YMI values varied widely from 477 mm (at Bikramganj) to 1523 mm (at Pattambi). The values showed main peak at active tillering stage. The AI values showed moderate aridity at early growth stages, which increased at advanced crop growth stages.

Assessment of crop water requirement of field pea (Pisum sativum L.) in foothills valley areas of Manipur, North East India

The purpose of this study was to assess the evapotranspiration in field pea (Pisum sativum L.) in foothills valley areas of Manipur using the Hargreaves-Samani equation to predict the plant water demand. The crop coefficient (Kc) values ranged between 0.45 and 1.28 during the crop growth stages of field pea for the five crop seasons (2013-18). The average five-year effective rainfall was estimated to be 59.0 mm, with standard deviation (SD±) ranging between 4.4 to 35.1 mm. The average crop water requirement for field pea was estimated to be 221.0 mm and the average water demand for different crop growth stages of field pea was estimated to be 20.0 mm (initial stage), 52.0 mm (development stage), 100.0 mm (mid-season) and 49.0 mm (late season). Thus, the information generated may help in effective management of crop water requirements for sustainable crop production including field pea in the region.

Impact of Agro-met Conditions and Crop Growth Stages on the Progression of Brown Spot Disease in Basmati Rice

The present study was carried out at Crop Research Centre of SVPUAT Meerut, U.P during three cropping seasons i.e. 2018, 2019 and 2020 using basmati rice as test cultivar. The study was primarily focused upon the combined effect of weather parameters and crop growth stages of rice crop on the progression of brown spot disease. It was noticed that disease was first observed at late vegetative stage in every cropping season viz. 2018, 2019 and 2020 and reached its maximum towards maturity phase of the crop by obtaining total AUDPC’S of 1049.3, 1170.74 and 852.6 respectively. A significant negative correlation between weekly percent disease index (PDI) and T-max & T-min was obtained recording correlation coefficients (r) of (- 0.71 & - 0.98), (- 0.88 & - 0.98) and (- 0.63 & - 0.98) during 2018, 2019 and 2020 respectively indicating decline in maximum and minimum temperatures at the terminal stages of the crop can greatly favor disease progression. A non-significant positive correlation was obtained between weekly m-RH and PDI to the end of every crop season. During the year 2020, a highly significant negative correlation was obtained between weekly a-RH and PDI (r = - 0.803) in contrast with the years 2018 (r = - 0.55) and 2019 (r = -0.477) exhibiting non-significant negative correlation which might be the reason for low PDI during the year 2020 due to greater decline in relative humidity to the end of the crop season. Although, a non-significant negative correlation between weekly PDI and RF (rainfall) and partial positive correlation with weekly bright sunshine hours (BSS) was obtained during all three crop seasons, high intermittent rainfall from late vegetative to reproductive stage during 2018 and 2019 might be responsible for large amount of spore dispersal (high inoculum pressure) leading to greater disease progression. The regression model developed using 2018, 2019 and 2020 meteorological data, which was validated with disease severity data of 2019 yielded significant R2 value of 0.98 using observed and predicted values.

Construction of Saturated Designs for Mixture Experiments

Mixture Experiments are very common in real life experiments. Designing a mixture experiment involves selection of the proportion of the mixture components in a fashion such that a mathematical model can be fitted adequately and the parameters could be estimated. In agricultural experiments, the mixture components may be several sources of the input applied or input may be applied at different crop growth stages in splits such that total quantity applied to the crop is constant. Efficient designs for mixture experiments are useful when the response is assumed to depend on the relative proportions of the ingredients present in the mixture. A number of algorithms and heuristics are available in literature; however, a limited work has been done in the use of algorithms for mixture experiments. There is a need to develop designs for mixture experiments in smaller number of runs for a specific model for varying proportions using algorithmic approach. In this study we have developed algorithms to construct saturated designs fort mixture experiments. The algorithm provides a greater flexibility in design construction in comparison to the traditional approach in terms of models to be fitted; number of runs to be requited etc. These designs are very well suited in real life experiments. The use of algorithms in construction of designs for mixture experiments not only reduces the computational cost but also results in a more efficient search of the design in a continuous design space.

Application of reflectance based vegetation index to derive dual crop co-efficient for summer sesame

Dual crop co-efficient approach was applied to estimate seasonal water requirement for summer sesame using reflectance based vegetation indices. Field experiment was conducted to collect the required various crop physiologic parameters and NDVI data for the study crop during 2018 and 2019. Basal crop co-efficients and soil evaporation co-efficients collected from FAO-56 for initial mid and end stages of summer sesame were adjusted for study area using local weather parameters. Spectrum® Field Scout CM 1000 NDVI Meter were used to collect the NDVI data at various stages of study crop. The NDVI was measured from crop canopy and soil surface at 7 days intervals between 12.00 to 13.00 clocks. NDVI Based Basal Crop Co-efficient and Soil evaporation co-efficient were derived using standard methods. FAO estimated crop co-efficients were compared with NDVI based crop co-efficients. The co-efficient of determination of the fitted regression equation was found to be 0.836 and 0.765 for drip irrigation and 0.783 and 0.867 for surface control irrigation system for summer sesame during 2018 and 2019, respectively. Crop growing stage wise water requirement per unit area was estimated for both treatments. Results indicates that among these two methods, NDVI method estimate lowest water requirement in both cases i.e. total water requirement and during all the crop growth stages for both irrigation systems while daily crop water requirement was lower for all growth stages in drip system as compare with control system.

Estimation of crop water requirement for rice- wheat and rice- maize cropping system using CROPWAT model for Pusa, Samastipur district, Bihar

This study is about estimation of crop water requirement for rice-wheat and rice-rabi maize cropping system for Pusa Region of Samastipur district of Bihar using CROPWAT model for year 2017-18.The effective rainfall was calculated using USDA S.C. Method. Reference crop evaporation was calculated using meteorological data viz temperature, relative humidity, wind speed and Sunshine using Penman Monteith equation. The meteorological data were collected from university observatory of R.P.C.A.U Pusa. Crop coefficient (Kc) value was taken according to crop growth stages. Effective rainfall and crop water requirement was used for determining net irrigation requirement. The annual crop water requirement of Rice- Wheat cropping system was found to be 904.1 mm whereas the crop-water requirement of Rice- Rabi Maize cropping system was 991.7 mm.

RNAi efficacy is enhanced by chronic dsRNA feeding in pollen beetle

Double-stranded RNAs (dsRNAs) represent a promising class of biosafe insecticidal compounds. We examined the ability to induce RNA interference (RNAi) in the pollen beetle Brassicogethes aeneus via anther feeding, and compared short-term (3 d) to chronic (17 d) feeding of various concentrations of dsRNA targeting αCOP (dsαCOP). In short-term dsαCOP feeding, only the highest concentration resulted in significant reductions in B. aeneus survival; whereas in chronic dsαCOP feeding, all three concentrations resulted in significant mortality. Chronic dsαCOP feeding also resulted in significantly greater mortality compared to short-term feeding of equivalent dsαCOP concentrations. Our results have implications for the economics and development of dsRNA spray approaches for managing crop pests, in that multiple lower-concentration dsRNA spray treatments across crop growth stages may result in greater pest management efficacy, compared to single treatments using higher dsRNA concentrations. Furthermore, our results highlight the need for research into the development of RNAi cultivars for oilseed rape protection, given the enhanced RNAi efficacy resulting from chronic, compared to short-term, dsRNA feeding in B. aeneus.

Mapping Crop Phenology in Near Real-Time Using Satellite Remote Sensing: Challenges and Opportunities

Crop phenology is critical for agricultural management, crop yield estimation, and agroecosystem assessment. Traditionally, crop growth stages are observed from the ground, which is time-consuming and lacks spatial variability. Remote sensing Vegetation Index (VI) time series has been used to map land surface phenology (LSP) and relate to crop growth stages mostly after the growing season. In recent years, high temporal and spatial resolution remote sensing data have allowed near-real-time mapping of crop phenology within the growing season. This paper summarizes two classes of near-real-time mapping methods, i.e., curve-based and trend-based approaches. The curve-based approaches combine the time series VIs and crop growth stages from historical years with the current observations to estimate crop growth stages. The curve-based approaches are capable of a short-term prediction. The trend-based approaches detect upward or downward trends from time series and confirm the trends using the increasing or decreasing momentum and VI thresholds. The trend-based approaches only use current observations. Both curve-based and trend-based approaches are promising in mapping crop growth stages timely. Nevertheless, mapping crop phenology near real-time is challenging since remote sensing observations are not always sensitive to crop growth stages. The accuracy of crop phenology detection depends on the frequency and availability of cloud-free observations within the growing season. Recent satellite datasets such as the harmonized Landsat and Sentinel-2 (HLS) are promising for mapping crop phenology within the season over large areas. Operational applications in the near future are feasible.