Soil-Specific Efficacy of Mechanised Rice Planting and Weeding Equipment: A Multi-Terrain Evaluation | Chapter 3 | Current Research Progress in Agricultural Sciences Vol. 5

Labour availability is one of the factors deciding the timely completion of sowing or transplanting at peak seasons of delta areas in rice cultivation. Mechanisation supports the timely completion of operations and supports maintaining productivity with less cost of cultivation. Machinery performance especially depends on the soil type and crop ecosystem. With this background, the experiment was conducted with the objectives to assess the performance of current rice transplanter models and power weeder models, in different soil conditions and reduce cultivation costs while improving rice production and profitability. A strip plot design was used, with major three soil types as the main plot treatments, existing rice transplanter five models as the sub-plot treatments, and current power weeder three models as the sub-sub-plot treatments. Among the growth and yield parameters, the 6-row walking type transplanter combined with a cono weeder showed superior performance, recording higher tillers and productive tillers (16.28 and 20.65 per hill), filled grains (113 and 145 per panicle), and seed yields (5922 and 5733 kg ha^-1) in sandy clay loam and sandy loam soils, respectively. This combination also achieved higher net returns (Rs. 70,195 ha^-1 and Rs. 55,343 ha^-1) and benefit-cost ratios (BCRs) of 2.62 and 2.28. Additionally, it resulted in an extra grain yield of 1769 kg ha^-1 and 1873 kg ha^-1, with an additional net profit of Rs. 37,027 ha^-1 and Rs. 34,813 ha^-1 for sandy clay loam and sandy loam soils, respectively. In clay loam soil, the riding type 8-row transplanter combined with a single-row power weeder produced higher productive tillers (15.25 per hill), filled grains (122 per panicle), and seed yields (5506 kg ha^-1), along with a higher net return of Rs. 58,175 ha^-1 and a BCR of 2.32. This combination also resulted in an additional grain yield of 1121 kg.ha^-1, leading to an extra net profit of Rs. 24,618 ha^-1 and a corresponding net income increase compared to traditional farming practices. In conclusion, using the 6-row riding type transplanter and cono weeder, along with appropriate agronomic practices, is the key to achieving higher yields, net returns, and BCR in sandy clay loam and sandy loam soils. Similarly, the 8-row riding transplanter combined with a single-row power weeder is essential for higher yield performance and profitability in clay loam soil.

 

Author (s) Details

S. Vallal Kannan
Coastal Saline Research Centre, Tamil Nadu Agricultural University (TNAU), Ramanathapuram - 623 503, India.

 

Sangeetha Jebalin, V. V.
Department of Agronomy, Agricultural College and Research Institute (TNAU), Madurai - 625 104, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/crpas/v5/3142 

Integrating Drone Technology in Agricultural Practices: Evidence from Tamil Nadu’s Farmer Producer Organizations | Chapter 3 | Food Science and Agriculture: Research Highlights Vol. 1

The advent of drone technology has revolutionized various industries, with agriculture being one of the most significantly impacted sectors. The integration of drones into agricultural practices contributes to environmental sustainability by promoting more efficient use of resources. Indian agriculture makes a substantial contribution to the nation's GDP, employment, and food security. It directly employs over half of the country's workforce, supporting the livelihoods of rural communities. Historically, Indian agriculture has been labour-intensive and reliant on traditional practices, resulting in inefficient resource utilization. To address the numerous challenges facing the Indian agriculture sector, the adoption of emerging technologies, such as drones, is imperative. Drones have the capacity to significantly enhance agricultural practices, increasing productivity and reducing resource wastage. This study explores the economic dynamics of drone technology in agriculture, addressing a gap in past research amid the growing use of Artificial Intelligence in the sector. This study integrated the Partial budgeting approach as it allows for assessing the impact of a change in the production system on a farmer’s net income without knowing all costs of production. This study was conducted in two districts of Tamil Nadu using a multistage sampling technique to collect the data. Conducted in the paddy cultivation regions of Thanjavur and Madurai districts in Tamil Nadu, the study involves a sample of 80 for UAV technology and 120 for conventional methods. The findings reveal significant cost savings and higher profitability with drone-assisted farming, where total expenses decrease from ₹27,723.20 for conventional farming to ₹22,857.50 for drones, primarily due to reduced pesticide and herbicide use and improved application efficiency. While both methods yield similar gross returns of ₹39,100 for conventional and ₹40,640 for drone-assisted—the net returns are markedly higher for drones at ₹17,782.50 versus ₹11,376.80 for conventional practices. A per-acre comparison shows substantial reductions in labor costs, with human labor decreasing from ₹11,077 to ₹5,628 and pesticide costs falling from ₹2,032 to ₹950. Although machine labor costs rise slightly with UAVs, overall savings enhance the financial viability of drone-assisted farming. The partial budget analysis indicates a net profit increase of ₹7,331, underscoring the economic advantages of adopting drone technology in agriculture. Finally, it is concluded that the integration of drone technology into Indian agriculture, particularly in paddy cultivation in Tamil Nadu's Thanjavur and Madurai districts, signifies a transformative shift towards more efficient and profitable farming practices.

 

Author (s) Details

 

Malaisamy A
Department of Agricultural Economics, Agricultural College and Research Institute, Madurai, Tamil Nadu-625104, India and International Livestock Research Institute, India.

Yazhini A
Department of Agricultural Economics, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu-641003, India.

 

Raswanthkrishna M
Department of Computer Science and Engineering (AI), Amrita University, Coimbatore- 641 112, Tamil Nadu, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/fsarh/v1/4322