Spatial Yield Assessment of Sorghum (Sorghum Bicolor) Using DSSAT – CERES –Sorghum Model in Solapur, Maharashtra, India

Accurate estimation of crop yield is crucial for ensuring food security and effective policy making. This study focuses on the estimation of sorghum yield in the Solapur region of Maharashtra (India), employing the Decision Support System for Agrotechnology Transfer (DSSAT) model. Sorghum is the fifth largely produced staple crop of the world which also plays a vital role in the food security produced by India. Maharashtra has the largest area under sorghum crop, and Solapur has the most area under rabi sorghum with an area of 4.6 lakh ha, accounting for 23% of the total area under rabi sorghum in the state. Although productivity is lower in Maharashtra than in other states, these studies will help us to get a preharvest estimate of the crop. Crop Cutting Experiments(CCE) were conducted for rabi sorghum and the model was validated for the simulated yields; which have a range of grain yield from 611 to 1525 kgs ha-1 and showed error with less than 14% and it was evaluated with statistical models such R2, Nash-Sutcliffe efficiency (NSE) and Normalized Root Mean Square Error (NRMSE) and results show as 84%, 0.84 and 0.07. This model can be used further used for the yield gap analysis, and climate change studies for the locations.

Studies on Genetic Variability, Correlation Analysis in Mithipagal (Momordica charantia var. muricata) Genotypes

The present investigation was carried out in Tamil Nadu Agricultural University, Coimbatore to study the “Genetic variability and correlation analysis in Mithipagal (Momordica charantia var. muricata) genotypes”. Observations on vine length, days to male flower inflorescence, days to female flower inflorescence, node of first male flower appearance, node of first female flower appearance, sex ratio, number of fruits per vine, fruit weight, fruit length, fruit girth, number of seeds per fruit, yield per vine, ascorbic acid, protein content, iron content and total soluble solids were recorded. Variability and correlation analysis among genotypes were examined. The results, showed that yield per plant had high positive and high significant correlation with fruit weight, fruit girth, number of seeds per fruit, vine length and fruit length. High genotypic co-efficients of variation (GCV) were found for fruit yield per vine, fruit weight, TSS, fruit length, vine length, fruit girth, node of first female flower, number of fruits per vine, number of seeds per fruit, iron, protein content when genetic characteristics were taken into account, However low GCV was found for days to the first male and female flowering. Phenotypic variants were always greater than genotypic variances. For traits such asyield per vine, fruit weight, TSS, fruit length, vine length, fruit girth, number of fruits per vine, number of. seeds per fruit, node of first male flower appearance, node of first female flower appearance, sex ratio, vitamin C, protein, iron content high heritability was found together with high genetic advance in percent of mean, indicating that these features are under additive gene control and hence selection for genetic improvement would be successful. Node of first male flower appearance showed low heritability combined with low genetic advance as a percentage of the mean indicating that non-additive gene effects were involved in the expression of this trait and hence selection for such a trait could not be beneficial. The knowledge of these statistical factors would be useful in identifying genotypes with greater yield potential that might be used in the improvement of mithipagal.

Mapping Rice Area in the Cauvery Delta Zone of Tamil Nadu Using Sentinel 1A Synthetic Aperture Radar (SAR) Data

Since remote sensing based crop inventory provides accurate and timely information as compared to the conventional survey methods of estimating area, Multi-temporal Sentinel 1A Synthetic Aperture Radar data was used for the estimation of rice area during Samba season 2022 in the Cauvery delta zone comprising Thanjavur, Thiruvaru, Mayiladuthurai, and Nagapatnam districts of Tamil Nadu. SAR data was preferred over optical satellite data due to excess cloud cover during cropping the major season in Tamil Nadu. Temporal back-scatter (dB) signature of rice crop was generated from the multi-temporal processed SAR data utilizing the modules of a fully automated MAPscape software aiding the discriminating of the crop from others. The signatures revealed that the dB levels to be the lowest during agronomic floods, reached the highest during maximum tillering stage and started declining thereafter. Multi-temporal feature extraction module of Mapscape was used to estimate rice area and validated for accuracy using ground truth data collected during survey. A total of 3.05 lakh ha of rice area was estimated with an overall accuracy of 90.8 % and 0.82 kappa coefficient. Largest area of 1.12 lakh ha was recorded in Thanjavur followed by Thiruvarur and Mayiladuthurai with 0.95 and 0.51 lakh ha respectively.

Assessment of Climate Change Impact and Adaptation Strategies for Rainfed Maize (Zea mays L.)

The study was performed to assess the impact of climate change on spatiotemporal changes in rainfed maize yield. Climate projections data of MIROC-ESM-CHEM model from CMIP6 was used for future climatic scenarios in the maize growing areas of Dindigul and Perambalur districts of Tamil Nadu. The DSSAT model was used to simulate maize yield and evaluate adaptation strategies for base period (1991-2020), the mid (2040-2069) and end centuries (2070-2099) under SSP245 and SSP585 scenarios. The simulation finding shows that, in all scenarios maize yield declined in both Dindigul (7 to 9% and 11 to 12%) and Perambalur (6 to 9% and 11 to 13%) during mid and end centuries respectively from the base period (1991-2020). Following the adaptation strategies such as delayed sowing, the yield was increased in both Dindigul (5 to 6% and 4 to 5%) and Perambalur (4 to 5% and 5 to 6%) with respect to normal sowing date. The results of this study would help in developing adaptation strategies for minimizing the adverse effects of the projected climate in maize-growing districts of Tamil Nadu.

Crop Diversification Assessment in Tank Ayacut Areas of Lower Palar Sub-Basin of Chengalpattu District, Tamil Nadu, India Using Geo-Spatial Techniques

For the assessment of crop diversification in the major tank Ayacut area of the Lower Palar sub-basin in Chengalpattu district of Tamil Nadu, research works were carried out using Sentinel 2 optical data by relating with ground truth data, to identify the crops in pixel-based classification and further classified the crops using Random Forest machine learning algorithms. The total area estimated under crop classification was 15767.97 and 28818.17 ha respectively for the summer seasons of 2018 and 2021. Since, the summer season experiences high crop diversification. The water spread area and water volume of tanks estimated were 612.31 and 1177.89 ha and 6,39,248 and 14,06,056 m3 respectively for 2018 and 2021. The accuracy assessment of ground truth points by confusion matrix reveals an overall classification accuracy of 96.8% (2018) and 94.9 % (2021) with kappa scores of 0.96 and 0.94 respectively. The crop diversification assessments were estimated using the Simpson Index of Diversity and values of 0.63 and 0.68 were accounted for in 2018 and 2021 respectively. The diversified pattern of crops is significantly correlated with tank water availability which increased the cropping area in 2021 as confirmed by the Crop Diversification factor.