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The study compares the performance of satellite-based datasets and the Ångström–Prescott (AP) model in estimating the daily global horizontal irradiance (GHI) for stations in South Africa. The daily GHI from four satellites (namely SOLCAST, CAMS, NASA SSE, and CMSAF SARAH) and the Ångström–Prescott (AP) model are evaluated by validating them against ground observation data from eight radiometric stations located in all six macro-climatological regions of South Africa, for the period 2014-19. The evaluation is carried out under clear-sky, all-sky, and overcast-sky conditions. CLAAS-2 cloud fractional coverage data are used to determine clear and overcast sky days. The observed GHI data are first quality controlled using the Baseline Surface Radiation Network methodology and then quality control of the HelioClim model. The traditional statistical benchmarks, namely the relative mean bias error (rMBE), relative root mean square error (rRMSE), relative mean absolute error (rMAE), and the coefficient of determination (R2) provided information about the performance of the datasets. Under clear skies, the estimated datasets showed excellent performance with maximum rMBE, rMAE, and rRMSE less than 6.5% and a minimum R2 of 0.97. In contrast, under overcast-sky conditions there was noticeably poor performance with maximum rMBE (24%), rMAE (29%), rRMSE (39%), and minimum R2 (0.74). For all-sky conditions, good correlation was found for SOLCAST (0.948), CMSAF (0.948), CAMS (0.944), and AP model (0.91); all with R2 over 0.91. The maximum rRMSE for SOLCAST (10%), CAMS (12%), CMSAF (12%), and AP model (11%) was less than 13%. The maximum rMAE for SOLCAST (7%), CAMS (8%), CMSAF (8%), and AP model (9%) was less than 10%, showing good performance. While the R2 correlations for the NASA SSE satellite-based GHI were less than 0.9 (0.896), the maximum rRMSE was 18% and the maximum rMAE was 15%, showing rather poor performance. The performance of the SOLCAST, CAMS, CMSAF, and AP models was almost the same in the study area. CAMS, CMSAF, and AP models are viable, freely available datasets for estimating the daily GHI at South African locations with quantitative certainty. The relatively poor performance of the NASA SSE datasets in the study area could be attributed to their low spatial resolution of 0.5° × 0.5° (~55 km × 55 km). The feasibility of the datasets decreased significantly as the proportion of sky that was covered by clouds increased. The results of the study could provide a basis/data for further research to correct biases between in situ observations and the estimated GHI datasets using machine learning algorithms.

This study validates the hourly satellite based and reanalysis based global horizontal irradiance (GHI) for sites in South Africa. Hourly GHI satellite based namely: SOLCAST, Copernicus Atmosphere Monitoring Service (CAMS), and Satellite Application Facility on Climate Monitoring (CMSAF SARAH) and two reanalysis based, namely, fifth generation European Center for Medium-Range Weather Forecasts atmospheric reanalysis (ERA5) and Modern-Era Retrospective Analysis for Research and Applications (MERRA2) were assessed by comparing in situ measured data from 13 South African Weather Service radiometric stations, located in the country’s six macro climatological regions, for the period 2013–2019. The in situ data were first quality controlled using the Baseline Surface Radiation Network methodology. Data visualization and statistical metrics relative mean bias error (rMBE), relative root mean square error (rRMSE), relative mean absolute error (rMAE), and the coefficient of determination (R2) were used to evaluate the performance of the datasets. There was very good correlation against in situ GHI for the satellite based GHI, all with R2 above 0.95. The R2 correlations for the reanalysis based GHI were less than 0.95 (0.931 for ERA5 and 0.888 for MERRA2). The satellite and reanalysis based GHI showed a positive rMBE (SOLCAST 0.81%, CAMS 2.14%, CMSAF 2.13%, ERA5 1.7%, and MERRA2 11%), suggesting consistent overestimation over the country. SOLCAST satellite based GHI showed the best rRMSE (14%) and rMAE (9%) combinations. MERRA2 reanalysis based GHI showed the weakest rRMSE (37%) and rMAE (22%) combinations. SOLCAST satellite based GHI showed the best overall performance. When considering only the freely available datasets, CAMS and CMSAF performed better with the same overall rMBE (2%), however, CAMS showed slightly better rRMSE (16%), rMAE (10%), and R2 (0.98) combinations than CMSAF rRMSE (17%), rMAE (11%), and R2 (0.97). CAMS and CMSAF are viable freely available data sources for South African locations.

The Satellite Symposium on Natural Farming, held on 24th February during the 13th Indian Fisheries and Aquaculture Forum (13th IFAF) from 23 to 25 February, 2024, convened notable figures in the field, including Shri Parshottam Rupala ji, Union Cabinet Minister, Fisheries, Animal Husbandry, and Dairying (Govt. of India), and Dr. Himanshu Pathak, DG ICAR and Secretary DARE (Govt of India), and other esteemed delegates. This symposium focused on the purpose and achievements of natural farming within aquaculture, fisheries, and soil and water conservation. Natural farming was highlighted as a sustainable solution to industry challenges, surpassing conventional practices. By integrating agroecology, traditional knowledge, and modern science, natural farming minimizes environmental impact and promotes healthy aquatic ecosystems. The event featured keynote speakers and panelists who shared valuable insights, fostering a rich exchange of knowledge. The symposium emphasized natural farming's alignment with global sustainability goals by reducing reliance on synthetic inputs, lowering carbon emissions, and promoting social inclusivity. However, addressing challenges such as knowledge gaps, policy limitations, and market complexities requires collaborative efforts. As the demand for seafood rises, natural farming emerges as a promising alternative, particularly for small-scale producers and resource-constrained regions, contributing to a sustainable blue economy. Opportunities in natural farming are abundant, especially in the context of aquaculture and related sectors. Adopting natural farming practices opens doors to improved productivity, cost-efficiency, and environmental sustainability. Small-scale farmers, in particular, stand to benefit from reduced input costs and enhanced market opportunities. Additionally, natural farming fosters biodiversity and ecosystem resilience, providing long-term benefits for communities reliant on aquatic resources. The 13th IFAF underscored the importance of embracing natural farming for a resilient, equitable, and sustainable future in aquaculture and related communities. This symposium highlighted ongoing efforts to promote natural farming practices, showcasing its potential to revolutionize the industry and foster greater environmental stewardship. [ABSTRACT FROM AUTHOR]

Given the limited observed and reliable data for solar irradiance in rural parts in South Africa, a correlation equation of the Angström-Prescott linear type has been used to estimate the regression coefficients in the Vhembe District, Limpopo Province, South Africa. Five stations were selected for the study, with the greatest distance between stations less than 180 km. Monthly regression coefficients were derived for each station based on an observation dataset of sunshine duration hours and global horizontal irradiance. The correlation coefficients appear to be above 0.9. The representative Angström-Prescott model for the Vhembe Region was found by collating the data for each station and then averaging the respective correlation coefficients. This paper presents the generated regression coefficients for each station and for the Vhembe Region.

This study assesses the performance of six global horizontal irradiance (GHI) clear sky models, namely: Bird, Simple Solis, McClear, Ineichen–Perez, Haurwitz and Berger–Duffie. The assessment is performed by comparing 1-min model outputs to corresponding clear sky reference 1-min Baseline Surface Radiation Network quality controlled GHI data from 13 South African Weather Services radiometric stations. The data used in the study range from 2013 to 2019. The 13 reference stations are across the six macro climatological regions of South Africa. The aim of the study is to identify the overall best performing clear sky model for estimating minute GHI in South Africa. Clear sky days are detected using ERA5 reanalysis hourly data and the application of an additional 1-min automated detection algorithm. Metadata for the models’ inputs were sourced from station measurements, satellite platform observations, reanalysis and some were modelled. Statistical metrics relative Mean Bias Error (rMBE), relative Root Mean Square Error (rRMSE) and the coefficient of determination (R2) are used to categorize model performance. The results show that each of the models performed differently across the 13 stations and in different climatic regions. The Bird model was overall the best in all regions, with an rMBE of 1.87%, rRMSE of 4.11% and R2 of 0.998. The Bird model can therefore be used with quantitative confidence as a basis for solar energy applications when all the required model inputs are available.

Background: Soybean [Glycie max (L.) Merril] is a very important oil seed and protein rich crop. It has the specific character to fix atmospheric nitrogen through root nodule bacteria in symbiotic relationship. A field experiment was conducted during kharif, 2019 at ARS farm, College of Agriculture, Ummedganj, Kota (Rajasthan). To find out the effect of organic and inorganic sources of nutrients on growth, yield attributes and nutrient uptake of soybean in Vertisols of Rajasthan. The soil of the experimental field comes under Vertisols with clay loam texture having low to medium fertility status and good porosity. Methods: The experiment consisted of 10 treatments viz. T0- Control (Absolute), T1- RDF (20-40-40), T2- 75% RDF + FYM (2.0 t ha-1), T3- 50% RDF + FYM (4.0 t ha-1), T4- 75% RDF + FYM (2.0 t ha-1)+Rhizobium, T5- 50% RDF + FYM (4.0 t ha-1) + Rhizobium, T6- 75% RDF + Vermicompost (1.0 t ha-1), T7- 50% RDF + Vermicompost (2.0 t ha-1), T8- 75% RDF+Vermicompost (1.0 t ha-1) + Rhizobium, T9- 50% RDF + Vermicompost (2.0 t ha-1) + Rhizobium. The experiment was laid out in randomised block design and was replicated thrice. Result: Among the nutrient management treatment, application of 75% RDF + Vermicompost (1.0 t ha-1) + Rhizobium (T8) resulted into maximum plant height (58.70 cm), branches plant-1 (5.60), chlorophyll content (2.84 mg g-1), total nodules per plant (47.40), effective nodules (31.59) and dry weight (84.20 mg), pods plant-1 (41.90), seeds pod-1 (3.47), grain yield (1380 kg ha-1), haulm yield (1885 kg ha-1), biological yield (3325 kg ha-1), protein (42.25%) and oil content (19.92%), N, P, K content and their uptake, with maximum gross return (53042.71 Rs ha-1), net return (28980.71 Rs ha-1) and B:C ratio (2.20) in soybean crop. [ABSTRACT FROM AUTHOR]

Background and Objectives: The virtuous act of blood donations should be encouraged and promoted by all means while safeguarding the well-being of the donor. If regular blood donations lead to iron store depletion in any individual, it may be identified and preventive measures should be taken. The current practice of hemoglobin (Hb) testing alone before donation may not detect the depletion of iron stores. Yet, a policy to introduce tests for iron stores and iron supplementation in regular donors requires extensive research in this area because of the cost and feasibility concerns. The objectives were to study the prevalence of subclinical anemia among voluntary blood donors using serum ferritin levels attending the blood transfusion center and to compare the average Hb and serum ferritin levels of donors across different age groups, gender, and donation frequencies. Methods: A cross-sectional study was done on 300 consecutively enrolled voluntary whole-blood donors (December 2018-November 2019) who fulfilled eligibility criteria. Donors were further divided into four groups, depending on their number of donations. Their samples were tested for Hb and serum ferritin to check for reduced body iron stores. The data obtained were analyzed using SPSS version 23. Results: A total of 300 regular voluntary donors were included in the study. 57.7% of donors were in the age group of 20-29 years, of which 83.33% were males. The total prevalence of subclinical anemia was 16%, in whom 54.2% were males and 45.8% were females. Out of the total female donors in our study (50), 44% suffered from subclinical anemia. Out of the total male donors in the study (250), 10.4% suffered from subclinical anemia. There was a significant association between frequency of donation and subclinical anemia (odds ratio [OR] =6.36, P < 0.001). There was a significant association between subclinical anemia across gender (high for females; OR = 6.76, P < 0.001). There was a significant correlation between the frequency of donation and serum ferritin (Spearman correlation coefficient: -0.299), but there was no correlation observed between the frequency of donation and Hb levels. Conclusion: The study demonstrates the depletion of iron store with increasing donation frequency. This calls for continual supervision of body iron stores rather than Hb alone to ensure the continuous supply of healthy blood donors. [ABSTRACT FROM AUTHOR]