A Key Input for Reducing of Mechanization Cost in Smallholdings: Mini Tractor Drawn Multi Task Toolbar

The present study aims to determine the key input for reducing of mechanization cost in smallholdings: Mini tractor-drawn multi task toolbar. Farm power and mechanization are essential inputs agricultural production system and will raise the labour and land productivity. Inadequate equipment and practices can damage severely natural resources. A focus on only one aspect, primary tillage (or) seeding (or) tilling (or) spraying results in low utilization rate which is not profitable to small holder farmer. Hence the attempt was made and developed "multi-task tool frame" that can house all the components required for a variety of operations such as seeding, fertilizing, weeding, tilling, spraying and transportation thereby reducing investment, operating cost, time and fuel for operation. The use of multi-task tool frame could reduce the cost of sowing and fertilizer application reduced from Rs. 3800/- to Rs. 1674/- per ha through developed machine planting. Moreover, the man hour requirement came down from 152 hour to 3 hours. Combining the spraying and weeding operation found to be good in 1st speed ie 1.8 kmph over the 2nd speed 3.2 kmph. However, the cost of reduction was 71 percent in machine operation when compared to manuel weeding & spraying and 44 percent over independent machine (spraying weeding) operation, moreover and saving in man-days about 48 man days per hectare. Fuel-saving of 8.15 Lha-1 which amounts to 47.8% due to combining the two operations like tilling and spraying compared to independent operations.

Analysis of sentinel-1 data for regional crop classification: a multi-data approach for rabi crops of district Hisar (Haryana)

Generation of spatio-temporal information such as land use system and management practices is one of the key ingredients for carrying out the regional level agro-ecosystem modelling. However, at the regional level availability of such data is scarce, where analysis of a cropping system is essential and a pre-requisite for studying the overall sustainability of the agricultural production system. The present investigation was carried out to identify the actually practised cropping pattern and their mapping in Hisar district of Haryana (India) using Multi-Data Approach (MDA). Multi-date sentinel-1 for the rabi season of 2019 was classified using multi-phase unsupervised classification approach and classes of interest were identified. Finally, classified images were subjected to logical combinations which helped in generating crop classification maps and statistics. Results showed that cropping pattern of the district exhibited huge variations and area under wheat was observed to be highest (204.76 thousand ha) in comparison to mustard crop (64.42 thousand ha) and least was under the sugarcane crop (0.97 thousand ha). Some other crops like vegetables and horticultural crops were also identified during this period, but the major crops that were identified during rabi 2019 were wheat and mustard. Hence, regional crop classification using sentinel-1 data appears to be a valuable tool for predicting a specific regions cropping pattern, which is considered to be the most significant aspect of an agricultural production system.

Modeling Adaptation Strategies against Climate Change Impacts in Integrated Rice-Wheat Agricultural Production System of Pakistan

There are numerous anticipated effects of climate change (CC) on agriculture in the developing and the developed world. Pakistan is among the top ten most prone nations to CC in the world. The objective of this analysis was to quantify the economic impacts of CC on the agricultural production system and to quantify the impacts of suggested adaptation strategies at the farm level. The study was conducted in the Punjab province’s rice-wheat cropping system. For this purpose, climate modeling was carried out by using two representative concentration pathways (RCPs), i.e., RCPs 4.5 and 8.5, and five global circulation models (GCMs). The crop modeling was carried out by using the Agricultural Production Systems Simulator (APSIM) and the Decision Support System for Agrotechnology Transfer (DSSAT) crop simulation models (CSMs), which were tested on the cross-sectional data of 217 farm households collected from the seven strata in the study area. The socio-economic impacts were calculated using the Multidimensional Impact Assessment Tradeoff Analysis Model (TOA-MD). The results revealed that CC’s net economic impact using both RCPs and CSMs was negative. In both CSMs, the poverty status was higher in RCP 8.5 than in RCP 4.5. The adaptation package showed positive results in poverty reduction and improvement in the livelihood conditions of the agricultural households. The adoption rate for DSSAT was about 78%, and for APSIM, it was about 68%. The adaptation benefits observed in DSSAT were higher than in APSIM. The results showed that the suggested adaptations could have a significant impact on the resilience of the atmospheric changes. Therefore, without these adaptation measures, i.e., increase in sowing density, improved cultivars, increase in nitrogen use, and fertigation, there would be negative impacts of CC that would capitalize on livelihood and food security in the study area.