Constraints Faced by Stakeholders under Agriculture Technology Management Agency (ATMA)

Agriculture Technology Management Agency (ATMA) is a registered society in India with key stakeholders enmeshed with various agricultural activities for sustainable agricultural development in the state, with focus at district level. It is a hotbed for integrating research, extension and marketing activities and decentralizing day-to-day management of the public Agricultural Technology Development and Dissemination System. The present study was carried out in Andhra Pradesh state to explore the constraints faced by the extension functionaries at each level of decentralized management. Moreover, constraints perceived by the farmers with the support of ATMA in realizing their needs were also studied

Studies on the relationship of weather on Fall armyworm damage in maize (Zea mays L.) under different growing environments

Fall armyworm is a recently occurring invasive pest in India, the most important defoliator causing drastic damage to maize production. Hence, the present study aimed to understand the temporal infestation level of Fall armyworms on maize (Zea mays L.) with weather patterns. Field experiments were conducted during Summer (February-May) and Rainy seasons, 2022 (August-December) at Agro Climate Research Centre, Tamil Nadu Agricultural University, Coimbatore. Three different growing environments (GE1, GE2 and GE3) were created by providing staggered sowing. Regression models were developed for per cent leaf damage against three-days lagged (LT3) and seven-day lagged (LT7) weather variables. Results showed that irrespective of growing environments, weather variables showed negative correlation (Tmax: r = -0.57, -0.81*, -0.31; SSH: -0.30, -0.48, -0.39; Tmean: -0.49, -0.23, -0.30; and SR: -0.48, -0.94*, -0.40) during summer season whereas same variables (i.e Tmax =0.62*, 0.41, 0.33; SSH = 0.09, 0.68*, 0.24; Tmean = 0.29, 0.32, 0.44; and SR=0.13, 0 .67*, 0.26 ) showed a positive correlation with PLD. Rainfall exhibits positive relation (0.06, 0.54, 0.53) and negative correlation (-0.64*, -0.10, -0.02) during summer and rainy season, respectively. Among the regression models, LT7 model had higher R2 (0.65 and 0.76) than LT3 (0.57 and 0.68) during summer and rainy seasons, respectively. These models had good regression values of 0.56 and 0.70 during Rainy and Summer, respectively. It was concluded that Tmax (32.9 °C), Tmin (23.7 °C), Tmean (28.3 °C), RH-I (85.6%), RH-II (56.4%), SSH (4.1), SR (274.6 cal cm-2 m-2), afternoon cloud cover (4.8 okta) and weekly total rainfall (10.2 mm) were very conducive for the greater leaf damage

Modelling of wetting patterns for surface drip irrigation in dense clay soil

The proportion of agricultural water consumption is continuously decreasing due to increased competition for water resources by urban, industrial, and agricultural users. Drip irrigation is more efficient in terms of water and energy utilization. These considerations are critical in view of the ongoing struggle for water resources among various consumers due to water scarcity. Some of the most critical criteria in the effective design and maintenance of drip irrigation systems are the shape and size of the volume of wet soil beneath the emitter. Hence several statistical models were constructed in this research to estimate the dimensions of wetting patterns, which are critical for designing an optimal drip irrigation system. The Nash-Sutcliffe efficiency (NSE), coefficient of correlation (CC), and root mean square error (RMSE) criteria were used to assess the models' performance. The results showed that the Polynomial model was the most accurate for horizontal advance, with 0.94, 0.93, and 1.33 (cm) values for CC, NSE, and RMSE, respectively. For vertical advance, the logarithmic model showed 0.96, 0.96, and 0.72 (cm) values for CC, NSE, and RMSE. Thus, in the absence of a wetting pattern and under identical conditions, these models can be utilized to generate synthetic horizontal and vertical advances data.

Mapping geographical inequalities in childhood diarrhoeal morbidity and mortality in low-income and middle-income countries, 2000–17 : analysis for the Global Burden of Disease Study 2017

Background Across low-income and middle-income countries (LMICs), one in ten deaths in children younger than 5 years is attributable to diarrhoea. The substantial between-country variation in both diarrhoea incidence and mortality is attributable to interventions that protect children, prevent infection, and treat disease. Identifying subnational regions with the highest burden and mapping associated risk factors can aid in reducing preventable childhood diarrhoea. Methods We used Bayesian model-based geostatistics and a geolocated dataset comprising 15 072 746 children younger than 5 years from 466 surveys in 94 LMICs, in combination with findings of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017, to estimate posterior distributions of diarrhoea prevalence, incidence, and mortality from 2000 to 2017. From these data, we estimated the burden of diarrhoea at varying subnational levels (termed units) by spatially aggregating draws, and we investigated the drivers of subnational patterns by creating aggregated risk factor estimates. Findings The greatest declines in diarrhoeal mortality were seen in south and southeast Asia and South America, where 54·0% (95% uncertainty interval [UI] 38·1–65·8), 17·4% (7·7–28·4), and 59·5% (34·2–86·9) of units, respectively, recorded decreases in deaths from diarrhoea greater than 10%. Although children in much of Africa remain at high risk of death due to diarrhoea, regions with the most deaths were outside Africa, with the highest mortality units located in Pakistan. Indonesia showed the greatest within-country geographical inequality; some regions had mortality rates nearly four times the average country rate. Reductions in mortality were correlated to improvements in water, sanitation, and hygiene (WASH) or reductions in child growth failure (CGF). Similarly, most high-risk areas had poor WASH, high CGF, or low oral rehydration therapy coverage. Interpretation By co-analysing geospatial trends in diarrhoeal burden and its key risk factors, we could assess candidate drivers of subnational death reduction. Further, by doing a counterfactual analysis of the remaining disease burden using key risk factors, we identified potential intervention strategies for vulnerable populations. In view of the demands for limited resources in LMICs, accurately quantifying the burden of diarrhoea and its drivers is important for precision public health