Influence of weed management practices on direct-seeded rice grown under rainfed and irrigated agroecosystems

Rice seedlings and weeds emerge concurrently in direct-seeded rice (DSR) production systems, while there is no flooding water to inhibit weed germination, emergence and development at crop emergence. Because of this, weeds are considered the biggest living barrier in DSR and significantly reduce yield. The purpose of the research was to devise an approach for management of weeds in the direct-seeded rice crop cultivated under various agroecosystems, while optimizing growth and production utilizing herbicides or herbicidal combinations. The impacts of several weed management techniques were assessed to determine the most efficient and cost-effective approach of managing weeds in DSR at the CoA, JNKVV, Jabalpur (MP) during 2019 rainy season under spilt plot design with 2 main plot treatments viz., rainfed agroecosystem, irrigated agroecosystem and 8 sub-plot treatments, i.e. different herbicide treatments with hand weeding and weedy check. Further growth parameters as well as yield attributes were documented. Conventional statistical techniques were used to evaluate the data. Bispyribac sodium at the dose of 25 g/ha efficiently controlled both narrow and broad leaved weeds under agroecosystems. Highest growth as well as yield parameters were recorded for irrigated agroecosystems compared to rainfed agroecosystems. The treatment with bispyribac sodium at the dose of 25 g/ha produced the greatest values for growth and yield indices as well as the maximum yield (3.68 t/ha), with the exception of manual weeding

Neural Network based Predictors for Evaporation Estimation at Jabalpur in Central India

319-328Free water evaporation is an imperative parameter for estimation of crop water requirement, and irrigation scheduling. This study aims to evaluate different techniques to estimate evaporation with weather parameters inputs. Multilayer Perception (MLR), Radial Basis Function (RBF) based neural network, traditional statistical Linear Regression (LR) approach and conventional empirical methods of Linacre and Christianson were used to estimate the evaporation at Jabalpur station situated under Kymore Plateau and Satpura Hills Agro-climatic Zone of Madhya Pradesh in the Central India. The weather parameters considered for estimation of evaporation are temperature, humidity, sunshine hours and wind speed. Results indicate that MLP and RBF based models with input of all selected weather parameters is able to estimate evaporation much precisely than LR and empirical approaches. It was found that higher accuracy may be obtained with multiple weather data input and low accuracy with only temperature input. It was observed that with temperature used as input the performance accuracy reduces in estimating evaporation with the selected models. However, neural network approach seems to produce better results as compared to statistical and empirical approach. The neural network based model RBF found more efficient in estimation of evaporation as compared to MLP. This study suggests that evaporation can be estimated by RBF model of a station, where there is no standard instrument available for its observation

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Not AvailableA field experiment was conducted during rabi season of 2014- 15 at Jabalpur, to study the effect of sowing date on weed infestation and yield of wheat varieties under different irrigation schedules. The results indicated that the crop sown on 27th November exhibit significantly highest grain (4.85 t/ ha) and straw (6.99 t/ha) yield as compared to delayed sowing. Similarly wheat cv. GW 366 under four irrigations at critical stages i.e. CRI, flowering, late jointing and milking recorded highest grain and straw yield over rest of the treatments under study. The lowest total weed density (29.11/ m2) and biomass (3.46 g/m2) was obtained in delayed sowing (27th December). Among varieties MP 1202 recorded lowest total weed density (41.10/m2) and biomass (8.57 g/m2) as compared to GW 366. While low frequency of irrigation i.e. two irrigation at CRI and flowering produced lowest weed density and biomass. Amongst the different weed species Melilotus indica and Chenopodium album are the predominant broad leaved weed species observed through out the crop growth of wheat crop. Weed density and biomass did not reveal any significant difference irrespective of varieties and irrigations schedules indicating sowing dates had prime importance in respect of density and biomass.Not Availabl

Phenology and Heat Unit Requirement of Wheat Varieties under Different Thermal Environments and IW: CPE Ratio-Based Irrigation Scheduling

A field experiment was conducted during rabi seasons of 2020-21 and 2021-2022 to study the heat unit indices viz; accumulated growing degree days (GDD), helio-thermal unit (HTU), photo-thermal unit (PTU), phenothermal index, helio-thermal use efficiency (HTUE), photothermal use efficiency (PTUE) and heat use efficiencies (HUE) at different phenological stages of two wheat (Triticum aestivum L.) varieties (Lok 1 and MP 3336) grown under different thermal environments (3rd December, 18th December and 2nd January) and irrigation schedules (IW: CPE= 1.0, IW: CPE= 0.9, IW: CPE= 0.8 and IW: CPE= 0.7). Results of present study reveals that the crop sown on thermal environment of 3rd December took maximum duration (113), GDD (1595.7°C days), HTU (11,146.3°C days hours), PTU (16,628.6°C days hours), HUE (2.97 kg ha-1°C-1 day), HTUE (0.43 kg ha-1°C-1 day), and PTUE (0.28 kg ha-1°C-1 day). Among irrigation schedule I1 (IW: CPE= 1.0) attained maximum crop duration (103 days), GDD (1513.8°C days), HTU (10,550.0°C days hours), PTU (15,461.3°C days hours), HUE (2.96 kg ha-1°C-1 day), HTUE (0.43 kg ha-1°C-1 day) and PTUE (0.29 kg ha-1°C-1 day). As regards varieties, MP 3336 took maximum duration (104), GDD (1548.1°C days), HTU (10818.9°C days hours), PTU (15863.5°C days hours), HUE (2.84 kg ha-1°C-1 day), HTUE (0.41 kg ha-1°C-1 day) and PTUE (0.28 kg ha-1°C-1 day). The heat unit indices decrease during vegetative stages but increases during reproductive phase. The crop duration, heat indices (GDD, HTU, PTU and PTI), HUE, HTUE, PTUE and grain yield was higher under thermal environment of 3rd December sown crop with irrigation schedule I1 (IW: CPE= 1.0) and wheat variety MP 3336

Influence of Irrigation Scheduling on Physiological Growth Parameters and Yield of Wheat under Different Sowing Dates

Sowing time and irrigation play an important role to ensure higher production. Delay in sowing affects the growth and development of crop. Studying the effect of irrigation scheduling and sowing dates can provide a knowledge for improving production in late sowing conditions. A field experiment was conducted during the rabi season of 2020-21 at Jawaharlal Nehru Krishi Vishwa Vidyalaya, Jabalpur, Madhya Pradesh to study the influence of irrigation scheduling on physiological growth parameters and yield of wheat under different sowing dates. The experiment comprised of 12 treatment combinations having three sowing dates, 3rd December, 18th December and 2nd January in the main-plot and four Irrigation Water/ Cumulative Pan Evaporation (IW/CPE) based irrigation scheduling, viz. 1.0, 0.9, 0.8 and 0.7 in the sub-plot was analyzed in split-plot design with three replications. The findings of present investigation revealed that the physiological growth parameters and yield were higher in 3rd December sown date. Among the irrigation scheduling, IW/CPE ratio 1.0 recorded significantly higher growth parameters and yield. The results of the present study conclude that adjusting sowing date with irrigation scheduling could assist in enhancing yield

Influence of Irrigation Scheduling on Weeds among Different Sowing Dates in Wheat under Vertisol

Wheat is an important cereal grain crop and is staple to millions. Weeds are the major constraint that lower the wheat yield. The knowledge of weeds under different sowing dates and Irrigation can assist in controlling weeds.  A field experiment was conducted during the rabi season of 2020-21 at Jawaharlal Nehru Krishi Vishwa Vidyalaya, Jabalpur, M.P. to study the influence of irrigation scheduling on weeds at different sowing dates in wheat. The experiment was laid out in split-plot design with three replications. The main plot consisted of three sowing dates, i.e., 3rd December, 18th December and 2nd January and four Irrigation Water/Cumulative Pan Evaporation (IW/CPE) based irrigation scheduling, 1.0, 0.9, 0.8 and 0.7 in the sub-plots. The results revealed that lowest weed density and dry weight were observed in 2nd January sown date, as compared to 3rd December and 18th December sown date. Among the irrigation schedules, 0.7 IW/CPE observed lowest weed density and dry weight than 1.0, 0.9, 0.8 IW/CPE ratio. 3rd December sown date exhibited highest grain yield (4637 kg ha-1) and straw yield (6788 kg ha-1) than 18th December and 2nd January sown date. Among the irrigation schedules, 1.0 IW/CPE ratio exhibited maximum grain yield (4510 kg ha-1) than 0.9, 0.8 and 0.7 IW/CPE ratio. The results of the study concludes that sowing dates and irrigation schedules had crucial role for controlling weeds

Neural Network based Predictors for Evaporation Estimation at Jabalpur in Central India

Free water evaporation is an imperative parameter for estimation of crop water requirement, and irrigation scheduling. This study aims to evaluate different techniques to estimate evaporation with weather parameters inputs. Multilayer Perception (MLR), Radial Basis Function (RBF) based neural network, traditional statistical Linear Regression (LR) approach and conventional empirical methods of Linacre and Christianson were used to estimate the evaporation at Jabalpur station situated under Kymore Plateau and Satpura Hills Agro-climatic Zone of Madhya Pradesh in the Central India. The weather parameters considered for estimation of evaporation are temperature, humidity, sunshine hours and wind speed. Results indicate that MLP and RBF based models with input of all selected weather parameters is able to estimate evaporation much precisely than LR and empirical approaches. It was found that higher accuracy may be obtained with multiple weather data input and low accuracy with only temperature input. It was observed that with temperature used as input the performance accuracy reduces in estimating evaporation with the selected models. However, neural network approach seems to produce better results as compared to statistical and empirical approach. The neural network based model RBF found more efficient in estimation of evaporation as compared to MLP. This study suggests that evaporation can be estimated by RBF model of a station, where there is no standard instrument available for its observation

Economics of Lac Production on Annual Host Cajanus cajan under Different Plant Density and Soil Moisture Condition

Inclusive of cash crop in crop production system a small and marginal farmer is an important state to shift them from sustainable farming to an economical farming. Lac is a cash crop while Cajanus cajan is a popular pulse crop in India. The present attempt was to evaluate economics of lac and grain production on C. cajan indifferent plant densities and soil moisture conditions. The two year data revealed that the highest net returnRs. 125.21 per plant, Rs. 149749.08 per hectare, in (S2W3) medium plant density (S2) and higher level of irrigation (W3), also highest input-output ratio (2.95) and B:C ratio (1.95)

Diversity and Abundance of Butterfly Species Complex in Two Diverse Habitats of Jawaharlal Nehru Krishi Vishwavidyalaya, India

Butterflies are important bio-indicators that should be protected to conserve the biodiversity and environment. They play an important role in the food chain and are valuable pollinators in the local environment. The present study investigated and compared the butterfly abundance and diversity within two different habitats (i) Undisturbed and (ii) Disturbed, in Jawaharlal Nehru Krishi Vishwavidyalaya Campus, Jabalpur Madhya Pradesh. A total of 24 butterfly species were recorded during the study from June 2022 to July 2023 using transects with the aid of sweep nets. An overall total of 2537 butterflies were recorded, which spread across 05 families 17 genera and 23 species. The most abundant family of butterflies caught in undisturbed ecosystems was Pieridae 38% followed by Nymphalidae 27%, Lycaenidae 14%, Papilionidae and Hesperiidae 7%. In disturbed ecosystems butterflies were distributed as Pieridae being dominated with 52% followed by Lycaenidae at 22%, Nymphalidae at 16%, Hesperiidae at 7% and Papilionidae at 3%. The undisturbed habitat was more diversified (H’-1.59) in butterfly diversity than the disturbed habitat (H’- 1.20)

Performance of Wheat Varieties at Different Sowing Dates under Open and Pongamia pinnata Based Agroforestry Systems

The experiment was carried out  in a Pongamia pinnata-based agroforestry system to assess the impact of land use systems, sowing dates, and wheat varieties on wheat cultivation at the Forestry Research Farm, JNKVV, Jabalpur during the Rabi season of 2021-22 The experiment followed a three-factor double split plot design with two systems (open system and agroforestry system) as the main plot, three sowing dates (12th November, 27th November, and 12th December) as subplots, and two wheat varieties (MP-3336 and GW-322) as sub-sub plots. The results showed that the open system outperformed the agroforestry system in terms of plant population, plant height at harvest, grain yield, straw yield, biological yield, and harvest index. Early-sown wheat consistently showed better performance in most parameters compared to timely-sown and late-sown varieties. Among the wheat varieties, the MP-3336 variety exhibited higher plant population, while the GW-322 variety showed taller plants at harvest, longer spikes, higher grain yield, and better harvest index. These findings provide valuable insights into optimizing wheat cultivation in agroforestry systems and emphasize the importance of considering land use systems, sowing dates, and wheat varieties to maximize crop productivity