Current Science

S. Saha ¹, D. Chakraborty ², B. U. Choudhury ², S. B. Singh ¹, N. Chinza ³, C. Lalzarliana ⁴, S. K. Dutta ¹, S. Chowdhury ¹, T. Boopathi ¹, Lungmuana ¹, A. R. Singh ¹, S. V. Ngachan ²

Affiliations
1 ICAR Research Complex for NEH Region, Mizoram Centre, Kolasib 796 081, IN
2 ICAR Research Complex for NEH Region, Umiam 796 103, IN
3 Directorate of Economics and Statistics, and Government of Mizoram, 796 001, IN
4 Directorate of Crop Husbandry, Government of Mizoram, 796 001, IN

Abstract

Long-term monthly rainfall observations (1986-2014) were analysed for 12 rain-gauge stations installed at variable altitudes of Mizoram. Our objective was to assess the temporal change in the standardized precipitation index (SPI) values at different timescales using Mann-Kendall trend tests. Significant reductions in post-monsoon and winter rainfall were recorded for most of the sites. Increasing dryness during the winter months may intensify the acute water shortage in Mizoram. Our results emphasize the altitudinal insensitivity of mean monthly rainfall trend and prove the urgent need for adopting suitable water management practices to cope with the water scarcity problem to increase the resiliency of rabi agriculture in Mizoram in near future.

Keywords

Agriculture, Rainfall Pattern, Standardized Precipitation Index, Spatial Variability.

Full Text

   

Anup Das ¹, R. K. Singh ¹, G. I. Ramkrushna ¹, Jayanta Layek ¹, A. K. Tripathi ¹, S. V. Ngachan ¹, B. U. Choudhury ¹, D. P. Patel ², D. J. Rajkhowa ¹, Debasish Chakroborty ¹, P. K. Ghosh ³

Affiliations
1 ICAR Research Complex for NEH Region, Umiam 793 103, IN
2 ICAR-National Institute of Abiotic Stress Management, Baramati 413 115, IN
3 ICAR-Indian Grassland and Fodder Research Institute, Jhansi 284 003, IN

Abstract

The north eastern region (NER) of India receives bountiful rains (>2000 mm) annually. However, there is extreme water scarcity during post- and premonsoon season (November-March). In such a situation, roof water harvesting (RWH) holds promise for multiple livelihood opportunities. RWH unit with polyfilm lined water collection tank of 37 m³ storage capacity (i.e. 5.5 x 4.5 x 1.5 m³) was demonstrated at 11 farmers fields mostly on hill tops in the Ri-Bhoi district (Meghalaya). The average demonstration area was 500 m²/farmer in the vicinity of homesteads (kitchen gardens). Volume of water harvested in a collection tank was about 53 m³ including about 16 m³ harvested during dry season due to seasonal replenishment. The cost of water harvesting was estimated at about Rs 144 and Rs 119/m³ considering lifespan of five and ten years respectively. Farmers used harvested water for diversified activities such as raising crops [maize, broccoli, French bean, laipatta (Brassica juncea), tomato, etc.] and livestock (pig or poultry) in addition to domestic use. The farmers without RWH could use land only during rainy season for crop cultivation. On an average, the net income from each RWH based model (500 m² demonstration area) was Rs 14,910 for crop + piggery and Rs 11,410 for crop + poultry farming which was 261 and 176% higher, respectively than the normal farmers' practice. Similarly, employment and water use efficiency enhanced by 221 and 586%; and 168 and 218% under crop + piggery and crop + poultry based farming respectively.

Keywords

Jalkund, Multiple Use of Water, NER Hills, Rain Water Harvesting, Silpaulin.

Full Text

   

S. K. Bal ¹, P. S. Minhas ¹, Yogeshwar Singh ¹, Mahesh Kumar ¹, D. P. Patel ¹, J. Rane ¹, P. Suresh Kumar ¹, P. Ratnakumar ¹, B. U. Choudhury ¹, N. P. Singh ¹

Affiliations
1 ICAR-National Institute of Abiotic Stress Management, Malegaon, Baramati, Pune 413 115, IN

Abstract

Vulnerability of agriculture to climate change is becoming increasingly apparent in recent years. During 2014 and 2015, India experienced trails of unusually widespread and untimely hailstorm events. The increased frequency of hailstorm events, especially in vulnerable ecosystem of Deccan Plateau region of India demanded appropriate measures to minimize adverse impact on agricultural crops. Therefore some of the post-hail measures including nutritional supplement, plant bio-regulators and canopy management were evaluated in field trials conducted at Maharashtra, India during 2014 and 2015. Amongst these, pruning of the hardy and indeterminate eggplant crop induced effective branches, which produced more flowers and fruits. Nitrogen supplemented with urea drenching and stress alleviating effects of salicylic acid promoted recovery in maize while drenching with humic acid along with spraying of potassium nitrate improved productivity of onion. These studies indicate the potential of technological interventions to cope with extreme events such as hailstorms.

Keywords

Bio-Regulators, Canopy Management, Crop Recovery, Hail-Damaged Crops, Nutritional Supplements.

Full Text

   

Gaurav Singhal ¹, Babankumar Bansod ², Lini Mathew ³, Jonali Goswami ⁴, B. U. Choudhury ⁵, P. L. N. Raju ⁴

Affiliations
1 CSIR-Central Scientific Instruments Organization, Chandigarh 160 030, IN
2 Academy of Scientific and Innovative Research, Ghaziabad 201 002, IN
3 National Institute of Technical Teachers, Training and Research, Chandigarh 160 019, IN
4 North East Space Application Centre, Barapani 793 103, IN
5 ICAR Research Complex for NEH Region, Umiam 793 103, IN

Abstract

The present study provides a systematic comparison of parametric and non-parametric retrieval methods using high-resolution data provided by the unmanned aerial vehicle (UAV). We used turmeric crop reflectance data to evaluate the vegetation index (VI)-based parametric methods and compared them with linear and nonlinear non-parametric methods to build a rigorous LCC estimation model. The study demonstrates that the best-performing VI was the normalized green red difference index (GNRDI), with R² = 0.68, RMSE = 0.13 and high processing speed of 0.08 s. With regard to non-parametric methods, almost all methods outperformed their parametric counterparts. Particularly, methods such as random forest (RF) and kernel ridge regression (KRR) showed the best performance characterized by R² > 0.72 and RMSE ≤ 0.12 mg/g of fresh leaf weight. These nonparametric methods possessed the benefit of total spectral information utilization and enabled robust, non-linear relationship between the predictor and target variables, but computational complexity is a major drawback.

Keywords

Chlorophyll, Machine Learning, Unmanned Aerial Vehicle, Vegetation Index.

Full Text