4. Rice Production Systems

The damage caused by insect pest is the continual factor for the reduction of rice production. To date, 232 rice insect pest species are identified in Bangladesh and more than 100 species of insects are considered pests in rice production systems globally, but only about 20 - 33 species can cause significant economic loss. The major goal of this study is to explore all the possible ways of developed and proposed technologies for rice insect pests management and minimize economic losses. Insect pests cause 20% average yield loss in Asia where more than 90% of the world's rice is produced. In Bangladesh, outbreak of several insects such as rice hispa, leafroller, gallmidge, stem borers and brown planthopper (BPH) occurs as severe forms. Based on previous reports, yield loss can reach upto 62% in an outbreak situation due to hispa infestation. However, BPH can cause 44% yield loss in severe infestested field. To overcome the outbreaks in odd years and to keep the loss upto 5%, it is necessary to take some preventive measures such as planting of resistant or tolerant variety, stop insecticide spraying at early establishment of rice, establish early warning and forecasting system, avoid cultivation of susceptible variety and following crop rotation. Subsequent quick management options such as insecticidal treatment for specific insect pest should also be broadcasted through variety of information systems. Advanced genomic tool can be used to develop genetically modified insect and plants for sustainable pest management. In addition, to stipulate farmers not use insecticides at early crop stgae and minimize general annualized loss, some interventions including training rice farmers, regular field monitoring, digitalization in correct insect pests identification and their management (example; BRRI rice doctor mobile app), and demonstration in farmers field. Each technology itself solely or combination of two or more or all the packages can combat the insect pests, save natural enemies, harvest expected yield and contribute to safe food production in Bangladesh. Bangladesh Rice J. 25 (1) : 1-22, 2021

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Studies on nitrogenase activity of diazotrophic isolates from different rice production systems

Journal of Applied and Natural Science ◽

10.31018/jans.v8i1.787 ◽

2016 ◽

Vol 8 (1) ◽

pp. 284-289

Author(s):

S. Kanimoli ◽

K. Kumar

Keyword(s):

Acetylene Reduction ◽

Tamil Nadu ◽

Production Systems ◽

Nitrogenase Activity ◽

Rice Production ◽

Lowland Rice ◽

Aerobic Rice ◽

Marked Variation ◽

Reduction Assay ◽

First Time

The present study was carried out to evaluate the nitrogen fixing ability of diazotrophs isolated from the rhizosphere soils of rice which were grown in three different rice growing systems. A total of hundred and ten isolates obtained were subjected to Acetylene Reduction Assay (ARA) and ninety eight isolates recorded significant amount of nitrogenase activity in a range of 185.73 to 3794.55 nmoles of ethylene mg of protein-1 h-1. The highest nitrogenase activity was recorded by Derxia (3794.55 nmoles of ethylene mg of protein-1 h-1) isolated from Trichy (lowland). Among the three different rice production systems, isolates obtained from lowland rice (Derxia â€“ 3794.5 nmoles of ethylene mg of protein-1 h-1) recorded higher nitrogenase activity followed by Aerobic (Pseudomonas - 2194.89 nmoles of ethylene mg of protein-1 h-1) and SRI (Azotobacter - 1971.85 nmoles of ethylene mg of protein-1 h-1) rice isolates. The results revealed marked variation in the ARA of the diazotrophic isolates obtained from lowland, SRI and Aerobic rice. The nitrogenase activity of diazotrophs from rice fields have been reported earlier but the nitrogenase activity of diazotrophs from three different rice production systems from various parts of Tamil Nadu is reported for the first time from India.

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‘B-M MODEL’ FOR FARMERS’ KNOWLEDGE MANAGEMENT IN INCREASING RICE PRODUCTION

Knowex food and agriculture ◽

10.17501/2682728x.2021.2104 ◽

2021 ◽

Vol 2 (1) ◽

pp. 30-45

Author(s):

BK Mahalder ◽

◽

MB Ahmed ◽

H Bhandari ◽

MU Salam ◽

...

Keyword(s):

Knowledge Management ◽

Field Data ◽

Production Systems ◽

Rice Yield ◽

Rice Production ◽

Practice Change ◽

Clear Understanding ◽

Data Set ◽

Production Practices ◽

The Difference

Quantifying knowledge on agriculture can have many benefits to stakeholders. While many knowledge-based systems exist in modern days for farmers’ decision support, specific models are lacking on how knowledge traits can impact on agricultural production systems. This study employed modelling technique, supported by field data, to provide a clear understanding and quantifying how knowledge management in production practices can contribute to rice productivity in the environmentally stressed southwest Bangladesh. This research accounted for ‘Boro’ rice as the target crop and ‘BRRI dhan28’ as the test variety. The ‘B-M Model’ was developed following the principle and procedure from published literature, ‘brainstorming’ and data from field surveys. Three knowledge management traits (KMT) were defined and quantified as the inputs of the model. Those are: self-experience and observation (SEO), extension advisory services (EAS) and accessed information sources (AIS). The yield influencing process (YIP), the intermediate state variable of the model, was deduced by accounting for the two dominant agronomic practices, seedling age for transplanting and triple superphosphate (TSP) application. ‘Knowledge drives farmers’ practice change which in turn influences yield’ was composed as the theoretical framework of the ‘B-M Model’. The model performed strongly against an independently collected field data set. Across the 180 farmers’ data, the average relative rice yield (RRY) predicted by the model (0.705) and observed in the field (0.716) was close (root mean squared deviation (RMSD) = 0.018). The difference between predicted and observed RRY was not statistically different (LSD = 0.03), indicating the model fully captured the field data. A regression of predicted and observed RRY explained 96% variance in observation, further proving the model’s strength in estimating RRY in a wider range of farmers’ rice yield. In a normative analysis, the practicality and usefulness of the model to stakeholders were simulated for the understanding of how much achievable yield could be expected by changing farmers’ knowledge pool (the sum of three KMT) on rice production practices, and at what combination(s) of KMT to be considered at strategic hierarchy to materialize a targeted achievable yield. To the best of the knowledge, a model quantifying rice yield in relation to knowledge management trait does not exist in literature. Upon successful testing under diverse yield scenarios using multiple and sophisticated statistical tools that enhanced the credibility of the model, it is concluded that the model has the potential to be used for identifying quantitative pathways of farmers’ knowledge acquisition for practice change leading to improved productivity of rice in the southwest region of Bangladesh.

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