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Singh, Mayank
- Litter Production Studies in a Lake Margin Ecosystem
Authors
1 Department of Botany, T. D. (P.G.) College, Jaunpur-222 002, U. P., IN
Source
Nature Environment and Pollution Technology, Vol 11, No 1 (2012), Pagination: 159-162Abstract
The paper deals with an evaluation of magnitude of periodic change in biomass and productivity of litter in the neglected or abandoned land (Site-I) and winter season cultivated land or crop lands (Site-II) at 'Gujar Tal' lake margin Jaunpur (U.P.). The slight sloping lake-land ecotones of both the sites were distinguished into two zones, i.e. upper and lower. The peak biomass value of litter at Site-I was 79.44 g.m-2 in the upper zone and 43.12g.m-2 in the lower zone both in the month of May. The maximum litter accumulation during the fallow period of Site-II was 52.46g.m-2 in October in the lower zone after recession of flood-water. In contrast, in crop period of Site-II, it was 59.12 g.m-2 and 69.15 g.m-2 at the age of 120 days in upper and lower zones, respectively. The peak productivity value of litter at Site-I was 1.50g.m-2day-1 in the upper zone and 0.70 g.m-2day-1 in the lower zone both in the month of May. The highest productivity values of litter of plant community in the fallow lands of Site-II were 1.25g.m-2day-1 in upper zone in the month of March, and 1.10g.m-2day-1 in October in the lower zone. The net productivity of litter during crop period of Site-II was 2.63 and 2.98 g.m-2day-1 at the age of 15 days of crop during November in the upper and lower zones. The annual litter production at Site-I was 87.67 g.m-2yr-1 in the upper zone followed by 67.79 g.m-2yr-1 in the lower zone. Their respective peak annual production at Site-II was 91.12 and 141.24 g.m-2yr-1. Analysis of variance for litter biomass at Site-I showed that variation due to zones and months both was significant (p<0.001), while at Site-II it was not significant both due to months and depths.Keywords
Litter Production, Productivity, Biomass, Lake Margin Ecosystem.Full Text
- A Study on Artificial Intelligence (AI) in Agriculture
Authors
1 Department of Electronics & Computer Education, Faculty of Technical Education, Gazi University, Ankara-Turkiye, TR
Source
Programmable Device Circuits and Systems, Vol 12, No 5-7 (2020), Pagination: 84-86Abstract
Agriculture and farming are among the oldest and most important professions in the world. New advances in agricultural AI show increased and improved yields in research and development of cultivated crops. New artificial intelligence can now increase agricultural efficiency by predicting how long it will take for crops such as tomatoes to mature and ready for harvest. These advances include crop and soil monitoring, agricultural robotics and predictive analytics. Crop and soil monitoring uses new algorithms and data collected in the field to manage and track the health of crops, making it easier and more sustainable for farmers. AI sensors can detect and target weeds and then determine which herbicide to use in the correct buffer zone. In addition to ground data, farmers monitor farms from the air.
