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.

Transpiration-use efficiency of young cactus pear plants (Opuntia ficus-indica L.)

Opuntia ficus-indica is a versatile crop that is resilient to drought, making it perfect for semiarid to arid zones. However, the lack of knowledge associated with its benefits and the lack of simple crop growth simulation models to determine its potential development, among others, has prevented its expansion. Transpiration-use efficiency (w) has been used to evaluate crop performance under different water supplies; however, the lack of consistency in w values under different environmental conditions has impeded its use as a transferable parameter. To overcome this problem, w is estimated through the normalized water-use efficiency (kDa) and the vapor pressure deficit (Da) as w = kDa Da-1, where kDa is a crop-dependent parameter. Therefore, the goals of this research were (i) to determine w and kDa in young plants of Opuntia ficus-indica and (ii) to compare the obtained parameters with values from other species. The w and kDa results were 18.57 (g kg-1) and 6.48 (g kPa kg-1), respectively. Here, w was more than two to six times the value for traditional cereals (maize, rice, wheat), while kDa was larger than that of most C3 crops and fell in the range for C4 and CAM crops. This is the first study that explicitly determines kDa for Opuntia ficus-indica; hence, more research should be carried out on its estimation, including under different agroclimatic conditions and in later stages of development. As a first approximation, the parameters obtained here can be used as a simple model to estimate yield projections of Opuntia ficus-indica.

SPATIALISATION OF RICE GROWTH AND YIELD MODEL USING OPTICAL AND SAR DERIVED BIOPHYSICAL PARAMETERS

<p><strong>Abstract.</strong> Integration of remote sensing derived biophysical parameters with process based crop growth simulation model is an emerging technology with diversified application for crop insurance as well as precision farming. Basically the crop growth simulation models are point based which simulate crop growth and yield as a function of soil, weather and crop management factors at a daily time scale. The temporal dimension of the crop growth model is supplemented by the spatial information on crop coverage and condition generated from remote sensing satellite derived biophysical parameters. In the present study an attempt is made to simulate pixel wise rice crop growth, condition and yield using Sentinel1 SAR, AWiFS/Landsat8 OLI and MODIS data with CERES rice growth simulation model on DSSAT platform. Temporal SAR data provides pixel wise start of season for rice crop at a spatial resolution of 10&amp;thinsp;m. Spectral indices derived from Landsat8/AWiFS and MODIS optical images are used for characterisation of rice growth environment and at the same time these indices are used to generate crop management information like irrigation and sowing dates. Bhadrak district of Odisha in the eastern coast of India is selected as the study area based on the prevalence of diversified rice growing environments. Sufficient in season field data are collected for checking the accuracy of rice map as well as for calibration and validation of the crop growth model. In season rice growth is initialised using SAR derived staggered sowing dates along with daily weather data and soil inputs. As CERES crop growth models are running well on DSSAT environment, rice crop growth simulation is carried out using multi date SAR images on DSSAT platform. The output of this study is maps depicting the spatial variability in rice area, staggered sowing dates and irrigation in the study region. These in season information are crucial for decision making particularly for crop insurance related activities.</p>

AUTOMATION OF THE DSSAT CROP GROWTH SIMULATION MODEL

<p><strong>Abstract.</strong> Crop Simulation Models (CSM) simulate the growth, development, and yield of crops using various inputs such as soil water, carbon and nitrogen processes, and management practices. DSSAT (Decision Support System for Agrotechnology Transfer) is a software program that comprises dynamic crop growth simulation models for over 42 crops. It incorporates modules for crop, soil, and weather to simulate long-term outcomes of crop management strategies. DSSAT-CSM requires various data for model operation. This includes data on the site where the model is to be operated, on the daily weather during the growth cycle, on the characteristics of the soil at the beginning of the growing cycle or crop sequence, and on the management of the crop. Acquisition of the data and providing the data to the DSSAT model is tedious and time-consuming as each individual value has to be manually entered. Additionally, crop simulation models can only be run for specific points and not for entire locations. Sometimes site-specific data especially weather data cannot be obtained. The output thus produced is difficult to analyze spatially at a large scale. The main purpose of this paper is to take the required dataset directly from spatial data. This is done by dividing locations into grids and taking the data from each grid. Python scripts are then used to convert this data into crop model format which is then run through DSSAT on an individual basis. The output thus obtained is be entered back into their respective grids as spatial data.</p>

Assimilating Remote Sensing Phenological Information into the WOFOST Model for Rice Growth Simulation

Precise simulation of crop growth is crucial to yield estimation, agricultural field management, and climate change. Although assimilation of crop model and remote sensing data has been applied in crop growth simulation, few studies have considered optimizing the crop model with respect to phenology. In this study, we assimilated phenological information obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) time series data into the World Food Study (WOFOST) model to improve the accuracy of rice growth simulation at the regional scale. The particle swarm optimization (PSO) algorithm was implemented to optimize the initial phenology development stage (IDVS) and transplanting date (TD) in the WOFOST model by minimizing the difference between simulated and observed phenology, including heading and maturity date. Assimilating phenology improved the accuracy of the rice growth simulation, with correlation coefficients (R) equal to 0.793, 0822, and 0.813 at three fieldwork dates. The performance of the proposed strategy is comparable with that of the enhanced vegetation index (EVI) time series assimilation strategy, with less computation time. Additionally, the result confirms that the proposed strategy could be applied with different spatial resolution images and the difference of simulated LAImean is less than 0.35 in three experimental areas. This study offers a novel assimilation strategy with regard to the phenology development process, which is efficient and scalable for crop growth simulation.