Impact of Zero Tillage on Productivity of Traditional Mung Bean-Wheat Cropping System of Punjab, Pakistan

Hussain, Imtiaz; Aulakh, Azhar Mahmood; Sohail, Muhammad; Hussain, Khalid; Ahmed, Ansaar; Hamid, Abdul; Imtiaz, Muhammad

doi:10.17582/journal.pjar/2020/33.4.896.904

Cited by 5 publications

(4 citation statements)

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“…In sequence with RTM, ZTW was superior to CTW but similar to RTW; whereas in sequence with CTM, all methods of wheat establishment were similar. Hussain et al (2020) in Pakistan have also reported 12% higher grain yield, CFU/ml = Number of colonies  Dilution factor Inculum volume Zero-tillage in Mung Bean-Wheat Cropping System: Impact on Soil Properties and Crop Productivity biological yield and HI of wheat when it was planted by zero tillage as compared to traditional farmers' practice in wheat (winter)-mung bean (summer) cropping sequence. The seed and stover yields of mung bean were similar under all crop establishment methods in MWCS.…”

Section: Crop Productivitymentioning

confidence: 99%

Zero-tillage in Mung Bean-Wheat Cropping System: Impact on Soil Properties and Crop Productivity

Kumar,

Singh,

Yadav

et al. 2024

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Background: Inclusion of legume crops in efficient cropping system is important to maintain soil bio-physical property for sustainable production systems. Conservation agriculture (CA) practices have been demonstrated to be successful in cereal based systems. But the information on zero-tillage as CA practice in legume based systems is lacking, which may further help in conservation of soil resources on sustainable basis. Hence study on impact of zero-tillage on soil biological and physico-chemical properties was undertaken at CCS Haryana Agricultural University, Regional Research Station, Bawal (Rewari), India during 2019-20 and 2020-21. Methods: The soil of experimental site was loamy sand in texture and low in organic carbon, N and P, while medium in K. The treatments included nine tillage combinations for crop establishment under mung bean [Vigna radiata (L.) Wilczek]-wheat (Triticum aestivum L.) cropping system (MWCS) viz., Zero-tillage mung bean (ZTM) fb zero-tillage wheat (ZTW), ZTM fb reduced tillage wheat (RTW), ZTM fb conventional tillage wheat (CTW), reduced tillage mung bean (RTM) fb ZTW, RTM fb RTW, RTM fb CTW, Conventional tillage mung bean (CTM) fb ZTW, CTM fb RTW, CTM fb CTW. The experiment was laid out in randomized block design with three replications. Result: After two years, there was no change in soil texture and EC of the soil due to different crop establishment methods, while the bulk density of upper soil layer (0-15 cm) decreased marginally under system-based ZT method. The Infiltration rate increased with decrease in the frequency of tillage in both the crops. Organic carbon in upper layer increased while pH decreased marginally under ZTM-ZTW as compared to CTM-CTW. Available N and P showed increasing trend with decrease in number of tillage operations after two years of MWCS. Tillage practices had no significant effect on available K. The grain yield of wheat was not influenced by tillage during 2019-20, but in 2020-21 it was significantly higher under ZT wheat sown after ZTM (5025 kg ha-1) than RTW (4622-4722 kg ha-1) and CTW (4593-4641 kg ha-1), irrespective of tillage practices in mung bean. The seed, stover and biological yields of mung bean were similar under all crop establishment methods in MWCS. This indicated sustainability of zero-tillage methods of establishment in MWCS.

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Section: Crop Productivitymentioning

confidence: 99%

Zero-tillage in Mung Bean-Wheat Cropping System: Impact on Soil Properties and Crop Productivity

Kumar,

Singh,

Yadav

et al. 2024

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“…Plants describedas a most beneficial nutrition and food system in the population [14]. An improved process of different seed (Plants) metabolomics, the information about the genetic-seed and bio-molecular process of rice grains metabolomics were different in the processing model [15].The rice intensification (SRI) system has used minimum water in crops and evaluated rice yield using synergy with different agronomic arrangements.…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Vision Approach for Identification of Four Variant Pakistani Rice Using Multi-Features Dataset

Aslam,

Muhammad Ijaz,

Ur Rehman

et al. 2023

Research Journal Of Computer Science And Information Technology

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Crops are the most important and beneficial food source in Pakistan. The demand for food has been an increase in Pakistan due to population growth. Pakistan produced 7,410 million tons of rice according to the financial year survey 2020 (FYS-2020). Pakistani rice has been cultivated in 3,304 hectares of the agricultural land zone, and it is also export around the world. Rice is also increased by 0.6% Gross Domestic Product (GDP) of Pakistan (FYS-2020). The old and manual process of rice classification is more expensive and time-consuming. In this study, we describe a machine vision approach for rice identification. We use four different varieties of rice for the experimental process such as Pakei_Kaynat, Kaynat_Kauchei, and Kauchei_Super_Banaspati and Tootaa_Kauchei (P1, P2, P3, and P4). The 100 images dataset have been used for practical work and total calculated of 400 (4 x 100) image of rice. The different process has been deploying on available datasets such as introduction, preprocessing methodology, and result discussion. A quality enhancement technique has been implementing for clarifying between rice color and shape sampling, and it is also converted color image in gray scale level. Every image has been employing six different non-overlapping regions of interest (ROI’s) and calculated a total of 2400 (6 x 400) ROI’s. Binary (B), Histogram (H) and Texture (T) features have been implemented and extract 43 features on each ROI’s and total calculated 103,200 (2400 x 43) machine learning (ML) features. Best First Search (BFS) Algorithm was used for feature optimization. Different ML classifiers are implementing for experimental process namely; Function Multi-Layer-Perception, Function SMO, Random Tree, J48 Tree, Meta Classifier via Regression and Meta Bagging. The Function Multi-Layer-Perception overall accuracy (OA) has describe better accuracy result is 99.8333%.

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Rice is grown in all provinces of Pakistan, but only Punjab province is famous for top quality Basmati rice production. Rice growers generally follow Rice -Wheat cropping system in this area (Hussain et al, 2012). The popular Basmati

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confidence: 99%

Pyramid Breeding Strategy for Integration of Multiple Traits for High Yield, Earliness, Short Stature and extra-Long Grain to Evolve a New Variety

Akhter,

Mahmood,

Haider

2018

J Biotechnol Bioeng

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Impact of Zero Tillage on Productivity of Traditional Mung Bean-Wheat Cropping System of Punjab, Pakistan

Cited by 5 publications

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Zero-tillage in Mung Bean-Wheat Cropping System: Impact on Soil Properties and Crop Productivity

Zero-tillage in Mung Bean-Wheat Cropping System: Impact on Soil Properties and Crop Productivity

Machine Vision Approach for Identification of Four Variant Pakistani Rice Using Multi-Features Dataset

Pyramid Breeding Strategy for Integration of Multiple Traits for High Yield, Earliness, Short Stature and extra-Long Grain to Evolve a New Variety

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