Application of SP AW model parameters - optimum sowing date and water stress to explain wheat yield variability

The Soil-Plant-Atmosphere- Water (SPA W) model has been calibrated and validated using field experiment data from 1991-92 to 1993-94 for wheat crop at Varanasi district. Long-term (1973-74 to 1995-96) daily weather data were combined with general observation of wheat growth and soils to provide daily water budgets for 23 years. The model was calibrated with one year detailed crop growth characteristics and soil water observations and validated with another year soil water observations. The daily-integrated water stress index (WSI) values at the end of crop season correlated quite well with observed grain yield in this region. The water budget analysis shows a distinct optimum sowing period from 5th to 25th November and an optimum sowing date on 15th November with minimal water stress index. These results demonstrate the potential of SPA W model for planning irrigation scheduling and water management for wheat crop in this region.

Modifying the NRC weight gain model to estimate daily gain for stockers grazing bermudagrass in the southern United States

The energy requirements, feed intake, and performance of grazing animals vary daily due to changes in weather conditions, forage nutritive values, and plant and animal maturity throughout the grazing season. Hence, realistic simulations of daily animal performance can be made only by the models that can address these changes. Given the dearth of simple, user-friendly models of this kind, especially for pastures, we developed a daily gain model for large-frame stockers grazing bermudagrass [Cynodon dactylon (L.) Pers.], a widely-used warm-season perennial grass in the southern United States. For model development, we first assembled some of the classic works in forage-beef modeling in the last 50 years into the National Research Council (NRC, 1984) weight gain model. Then, we tested it using the average daily gain (ADG) data obtained from several locations in the southern United States. The evaluation results showed that the performance of the NRC model was poor as it consistently underpredicted ADG throughout the grazing season. To improve the predictive accuracy of the NRC model to make it perform under bermudagrass grazing conditions, we made an adjustment on the model by adding the daily departures of the modeled values from the data trendline. Subsequently, we tested the revised model against an independent set of ADG data obtained from eight research locations in the region involving about 4,800 animals, using 30 years (1991-2020) of daily weather data. The values of the various measures of fit used, namely the Willmott index of 0.92, the modeling efficiency of 0.75, the R 2 of 0.76, the root mean square error of 0.13 kg d -1, and the prediction error relative to the mean observed data of 24% demonstrated that the revised model mimicked the pattern of observed ADG data satisfactorily. Unlike the original model, the revised model predicted more closely the ADG value throughout the grazing season. The revised model may be useful to accurately reflect the impacts of daily weather conditions, forage nutritive values, seasonality, and plant and animal maturity on animal performance.

learnMET: an R package to apply machine learning methods for genomic prediction using multi-environment trial data

We introduce the R-package learnMET, developed as a flexible framework to enable a collection of analyses on multi-environment trial (MET) breeding data with machine learning-based models. learnMET allows the combination of genomic information with environmental data such as climate and/or soil characteristics. Notably, the package offers the possibility of incorporating weather data from field weather stations, or can retrieve global meteorological datasets from a NASA database. Daily weather data can be aggregated in daily windows based on naive (for instance, daily windows with a fixed number of days) or phenological approaches. Different machine learning methods for genomic prediction are implemented, including gradient boosted trees, random forests, stacked ensemble models, and multi-layer perceptrons. These prediction models can be evaluated via a collection of cross-validation schemes that mimic typical scenarios encountered by plant breeders working with MET experimental data in a user-friendly way. The package is fully open source and accessible on GitHub.

Calibration and Validation of DSSAT CROPGRO Peanut Model for Yield and Yield Attributing Characters of Groundnut Varieties in Northern Agro-Climatic Zone of Tamil Nadu

Aim: The research study was conducted to calibrate and validate the DSSAT CROPGRO peanut model for simulating the potential yield of groundnut to deciding the best possible management options at major growing areas of Northern Agro-Climatic zone of Tamil Nadu. Study Design: The experiment was conducted in Split plot Design with four Sowing dates and cultivars. Methodology: The DSSAT model requires layer wise soil data (physical and chemical), including soil texture and other soil properties. Daily weather data, including maximum and minimum air temperature (°C), solar radiation (MJ m−2 day−1), Relative Humidity (%) and precipitation (mm) were used as inputs. Data describing management practices and information of cultivar-specific genetic coefficients were used to calibrate the model. Validation of model were carried out using observed growth and yield attributes of TMV13 and G7 varieties using RMSE (Root Mean Square Error), NRMSE (Normalized Root Mean Square Error) and agreement per cent as test criteria for the evaluation. Results: The performance of DSSAT CROPGRO peanut model for simulated growth attributes were underestimated the growth attributes like days to anthesis, leaf area index, days to first pod and days to maturity than compared to observed growth attributes of TMV13 and G7 varieties. But the model performs better for G7 as compared to TMV13. Whereas, yield and yield attributes of CROPGRO peanut model were overestimated than the observed yield. Conclusion: The simulation model shows the low RMSE, NRMSE and high agreement per cent for growth and yield of groundnut which was more than 90 per cent, it shows the higher level of confidence on model simulation with observed characters.

Evaluating and Adapting Climate Change Impacts on Rice Production in Indonesia: A Case Study of the Keduang Subwatershed, Central Java

Predicting the effect of climate change on rice yield is crucial as global food demand rapidly increases with the human population. This study combined simulated daily weather data (MarkSim) and the CERES-Rice crop model from the Decision Support System for Agrotechnology Transfer (DSSAT) software to predict rice production for three planting seasons under four climate change scenarios (RCPs 2.6, 4.5, 6.0, and 8.5) for the years 2021 to 2050 in the Keduang subwatershed, Wonogiri Regency, Central Java, Indonesia. The CERES-Rice model was calibrated and validated for the local rice cultivar (Ciherang) with historical data using GenCalc software. The model evaluation indicated good performance with both calibration (coefficient of determination (R2) = 0.89, Nash–Sutcliffe efficiency (NSE) = 0.88) and validation (R2 = 0.87, NSE = 0.76). Our results suggest that the predicted changing rainfall patterns, rising temperature, and intensifying solar radiation under climate change can reduce the rice yield in all three growing seasons. Under RCP 8.5, the impact on rice yield in the second dry season may decrease by up to 11.77% in the 2050s. Relevant strategies associated with policies based on the results were provided for decision makers. Furthermore, to adapt the impact of climate change on rice production, a dynamic cropping calendar, modernization of irrigation systems, and integrated plant nutrient management should be developed for farming practices based on our results in the study area. Our study is not only the first assessment of the impact of climate change on the study site but also provides solutions under projected rice shortages that threaten regional food security.

Potential yield and yield gap analysis of sugarcane (Saccharum officinarum) using the DSSAT-CANEGRO model in different districts of Uttar Pradesh, India

DSSAT-CANEGRO model have been used to determine crop potential yield over eight districts (viz; Muzaffarnagar, Shahjahanpur, Agra, Lucknow, Basti, Faizabad, Allahabad and Jhansi) representing different agroclimatic conditions & environmentof Uttar Pradesh state in India. The thirty six years (1980-2016) daily weather data of above districts were used to simulate seasonal yield potentials under the various management conditions and compared with the respective district reported yield. The simulated mean potential yield by the CANEGRO model over different district of the state varied between 77.8 t ha-1 in Muzaffarnagar and 97.8 t ha-1 in Agra, while mean reported yield (fresh stalk mass) varied between 40.1 t ha-1 in Jhansi and 62.8 t ha-1 in Muzaffarnagar within the state. Similarly, the attainable yield by the model was simulated lowest of 65.1 t ha-1 in Shahjahanpur and the highest of 73.6 t ha-1 in Faizabad district. The management yield gap was between 9.0 to 30.0 t ha-1 while sowing yield gap was between 7.0 to 26.0 t ha-1 in different districts under study. Further it is not only interesting & surprising but also encouraging to growers that the trends in total yield gap at all the above districts in various agro-climatic zones were found decreasing (narrowed down) at the rate of 138.8 – 801.2 kg ha–1 year–1. Delayed planting by about 30 days in some of the districts resulted into a decrease in sugarcane yield to the tune of 106.7 to 146.7, 103.3 to 143.3 and 80.0 to 133.0 kg ha–1 day–1, respectively. Findings reveal that DSSAT crop simulation model can be an effective tool to aid in decision support system. Yield gap estimates using the past crop data and subsequent adjustment in planting window may help to achieve close to the potential yields.

Water Supply Reliability of Agricultural Reservoirs under Varying Climate and Rice Farming Practices

Technological development and climate change dictate farming practices, which can directly affect irrigation water requirement and supply. In this article, the water supply reliability (WSR) of 62 major Korean agricultural reservoirs was comprehensively evaluated for varying climate and farming practices. Field surveys identified the recent divergence from standard rice farming practices and a 45-year daily weather data set (1973–2017) was examined to understand the phenomenon of climate change. Effective rainfall increments mitigated the imminent surges in rice irrigation water requirements driven by warming-led accelerated crop evapotranspiration rates; therefore, climate change marginally influenced the WSR of selected reservoirs. The transplanting period and associated water consumption were the primary deviations from standard rice farming practices. A significantly prolonged transplanting period seriously compromised the WSR of agricultural reservoirs and the maximum number of unsafe reservoirs was detected for a 24-day increase in the transplanting period. A watershed/irrigated area ratio of less than 2.5 was the lower threshold below which all the reservoirs had unsafe WSR regardless of the climate change and/or farming practices. Recent variations in farming practices were the primary cause of reservoir failure in maintaining the WSR.

Suitability of Earth Engine Evaporation Flux (EEFlux) Estimation of Evapotranspiration in Rainfed Crops

This study evaluated evapotranspiration (ET) estimated using the Earth Engine Evapotranspiration Flux (EEFlux), an automated version of the widely used Mapping Evapotranspiration at High Spatial Resolution with Internalized Calibration (METRIC) model, via comparison with ET measured using eddy covariance flux towers at two U.S. sites (St. John, WA, USA and Genesee, ID, USA) and for two years (2018 and 2019). Crops included spring wheat, winter pea, and winter wheat, all grown under rainfed conditions. The performance indices for daily EEFlux ET estimations combined for all sites and years dramatically improved when the cold pixel alfalfa reference ET fraction (ETrF) in METRIC was reduced from 1.05 (typically used for irrigated crops) to 0.85, with further improvement when the periods of early growth and canopy senescence were excluded. Large EEFlux ET overestimation during crop senescence was consistent in all sites and years. The seasonal absolute departure error was 51% (cold pixel ETrF = 1.05) and 23% (cold pixel ETrF = 0.85), the latter reduced to 12% when the early growth and canopy senescence periods were excluded. Departures of 10% are a reasonable expectation for methods of ET estimation, which EEFlux could achieve with more frequent satellite images, better daily weather data sources, automated adjustment of daily ETrF values during crop senescence, and a better understanding of the selection of adequate cold pixel ETrF values for rainfed crops.

Influence of multi-decadal meteorological variability on the reference evpotranspirtion in hot and humid coastal town of Annamalainagar in Tamil Nadu state

Reference evapotranspiration (ETo) is a key pointer of atmospheric evaporation demand and has been extensively used to describe the hydrological change. In this study, the reference evapotranspiration over the hot and humid town, Annamalainagar, very near to the east coast in Tamilnadu State, India, have been estimated employing the FAO Penman-Monteith (PM) method and the observed daily weather data during 1977-2016. The objective of the present study is two-fold: (i) To identify the multi-decadal trend of the various measured meteorological parameters namely, mean air temperature (Tmean), vapour pressure deficit (VPD), actual sunshine hours (SSH), net radiation (Rn) and wind speed (WS) at the study location and (ii) To identify the main contributing meteorological parameter for the detected decreasing trend in ETo over the multi-decadal period.

Development of high spatial resolution weather data using daily meteorological observations over Indian region

Finer spatial resolution interpolated weather data is essential to enable utilization of satellite-based images in studies related to crop growth dynamics, etc. Satellite data are available daily at 1 × 1 km or at the most within 5 × 5 km grid. To make the weather data timely available at the same spatial scale, the procedure has been developed to generate the spatially interpolated weather data product over India. Daily weather data (minimum & maximum temperature and rainfall) available at point scale on India Meteorology Department web site have been used in this study. A semi-automated user interactive Graphical User Interface (GUI) has been developed which quality checks the temperature data sets by filling the missing data sets as well as providing a platform to correct erroneous data which have been identified using statistical methods taking spatial as well as temporal incompatibility into account. Daily spatially interpolated product is generated in image form using thin plate spline interpolation technique that uses the quality checked weather data as well as elevation information from CARTODEM data in order to account for effect of elevation on temperature. The validation was performed using “Jack-knife testing method” for three different seasons i.e., monsoon, summer and winter. The mean absolute errors for decadal averaged products were found to vary within 1.2-1.5 °C for maximum temperature, 1.1-1.7 °C for minimum temperature, 1.0-7.0 mm for rainfall considering all seasons with higher error observed in monsoon for maximum temperature and rainfall and in winter for minimum temperature. It was found that errors were close to 1 °C for stations with elevation less than 550 m whereas in central portion of India, mean absolute errors were found to be less than 1 °C.