Survey of Impact of Technology on Effective Implementation of Precision Farming in India

The advancements in technology have made its impact on almost every field. India being an agricultural country, proper use of technology can greatly help in improving the standard of living of the farmers. With varying weather conditions, illiteracy of farmers and non-availability of timely assistance, the farmers of this country could not get the best out of their efforts. Precision farming focuses mainly on the aspects that can improve the efficiency based on the data collected from various sources viz. meteorology, sensors, GIS, GPS, etc. The information pertaining to farmland (e.g., soil moisture, soil pH, soil nitrogen) and agro-meteorology (e.g., temperature & humidity, solar radiation, wind speed, atmospheric CO2 concentration, rainfall, climate change and global warming) are used as input parameters to decide the varying requirements of the crop cultivation. Historical farm land data are used as a means to decide on the kind of actions to be taken under a specific scenario. This paper surveys the existing methods of precision farming and highlights the impact of technology in farming. An overview of different technologies used in precision farming around the world and their implications on the yield are discussed. The methods adopted towards managing different types of crops, the varying environmental conditions and the use of realtime data being collected through sensors are also analyzed. Also, the need for dynamic approaches to assist the farmers in taking context specific decisions has been highlighted

Analysis report of the first benchmark survey of Mahaweli System C Upgrading Project

River basins / Development projects / Monitoring / Evaluation / Irrigated farming / Farmers’ associations / Water delivery / Water loss / Data storage and retrieval / Infrastructure / Operations / Maintenance / Agricultural production / Rice / Costs / Labor / Credit / Sri Lanka / Mahaweli Project

Development and Assessment of Nematode Management Zones in Cotton: \u3ci\u3eGossypium hirsutum\u3c/i\u3e

Populations of plant-parasitic nematodes are difficult to manage due to their inherently sporadic nature and uneven distribution throughout a field. Soil sampling accompanied by laboratory extraction is the preferred method for estimating densities and locations of nematodes within a field. The uneven and sporadic nature of nematodes make them well suited for zone management in row crops, provided that effective zones can be defined. Effective zone definition for precision agriculture requires that differences in factors between zones are large and differences within zones are small. This study compared methods of defining zones based on physical soil properties, soil SSURGO data, and grids of similar area to cost-effectively direct nematode sampling efforts. Twenty-six methods of zone definition were investigated based on soil electrical conductivity (EC), physical soil properties and relative nematode index predictions in various combinations. For each zone definition method, the fitness of models used to define zones was evaluated using the Davies-Bouldin Index (DBI) for measuring cluster separation where effectiveness of zone definitions decrease as the DBI increases. The DBI range for all zone methods investigated was 24.918, with a minimum of 5.086 and maximum of 30.004. The most effective zone was created by contouring relative weighted nematode index predictions, with predictions based on soil EC, with a delineation range of one standard deviation, which returned the lowest DBI. Zones created based on a three equal range division of field silt levels returned the highest DBI indicating the least effective zone method. Using silt content in any range delineation showed to be inappropriate for zone definition. The two highest DBI values returned were when silt was used at a range delineation of 0.5 standard deviation, DBI of 29.0399, and a three equal division range, DBI of 30.004. Use of SSURGO soil data was also found to be significantly less effective for defining zones with a DBI of 27.155 compared with zones definitions based on soil EC. Zones defined using soil EC as a contributing factor demonstrated significantly effective zones. Of the nine zone definitions that were significantly effective, seven were defined using soil EC as some factor. A second goal of this project was to asses multi-hybrid planting technology as a tool for the management of nematodes. Cotton varieties are now available that are resistant to Southern root-knot nematode, the most common and important species on cotton. For this study, a field was chosen based on the ability to grow two consecutive years of cotton within a two-year cotton to one-year peanut crop rotation and an unknown distribution of nematode density and species. This field did not return Southern root-knot nematode densities in adequate quantities for any solid conclusions to be made as to the use of resistant cotton varieties for determination of Southern root-knot nematode aggregations to be used as a basis for multi-hybrid planting or variable rate application for nematode control. The cost of this approach can be prohibitive as it can include higher seed costs, planter upgrades, and creation of planting prescriptions, which may be based on costly nematode sampling. If accurate nematode sampling zones can be determined, the overall cost of implementing this technology can be reduced

Soil resource evaluation using precision farming techniques for selected sites in Tennessee

Crop yields have been shown to vary both between and within fields. Current technology allows for an accurate measurement of yield variability using Global Positioning System (GPS) and yield monitoring equipment. However, determination of the source of this variability is complicated by spatial differences in soil fertility, soil series, slope, and past management practices. This statewide study was designed to test the effectiveness of conventional soil survey maps against an intensive soil map created for various sites in Tennessee. Field Specific soil maps were developed using intensive soil sampling, incorporated with GPS and Geographic Information System (GIS) software. Relationships between soybean yield and soil mapping units were then statistically compared using spatial correlation models and S AS proc mixed procedures. Intensive soil maps better explained soybean yield variation (α = 0.05) although neither mapping technique was strongly related to yield. The site-specific maps were also better at distinguishing distinct yield groups by individual soil mapping unit. Specific properties of the soil and crop landscape were also investigated to determine their affect on soybean yield. Properties that had a significant affect on yield included subsoil texture, slope, and pH. Slope and subsoil texture interactions and drainage and effective rooting depth (ERD) interactions also showed yield differences. Soil properties that did not affect yield included soil drainage class, ERD, available phosphorous, and available potassium. Interactions of ERD and subsoil texture, ERD and slope, drainage and subsoil texture, and drainage and slope also showed no yield differences. Results of this study indicate that conventional mapping methods may not provide the necessary detail for use in today\u27s precision farming applications. When investigating specific properties within soil units, a limit is set in explaining yield variation by the soil unit boundary and further variability related to the soil unit cannot be explained. Although site specific maps are better than conventional mapping methods at predicting yields, an investigation into specific soil properties within a field may be necessary in providing a useful tool for producers implementing precision farming crop management

iGrow Wheat: Best Management Practices for Wheat Production

https://openprairie.sdstate.edu/plant_book/1000/thumbnail.jp

Using precision agriculture technology to evaluate environmental and economic tradeoffs of alternative CP-33 enrollments

United States Department of Agriculture’s Farm Bill conservation programs provide landowner incentives to remove less productive and environmentally sensitive lands from agricultural production and re-establish them in natural vegetation to achieve conservation objectives. However, removal of arable land from production imposes an opportunity cost associated with loss in revenue from commodities that otherwise would have been produced. The Habitat Buffers for Upland Birds practice (CP-33) under the Continuous Conservation Reserve Program is a targeted conservation practice designed to increase northern bobwhite populations in agricultural landscapes. However, establishing CP-33 buffers on profitable farmland may be incompatible with economic objectives of landowners. To determine how CP-33 enrollment influenced field profitability and bobwhite abundance; I simulated CP-33 buffers on crop fields across a range of commodity prices and modeled profitability and predicted bobwhite abundance. CP-33 increased field revenue on a percentage of fields at all commodity prices and increased bobwhite abundance up to 30%

Using precision agriculture technology to evaluate environmental and economic tradeoffs of alternative CP-33 enrollments

United States Department of Agriculture’s Farm Bill conservation programs provide landowner incentives to remove less productive and environmentally sensitive lands from agricultural production and re-establish them in natural vegetation to achieve conservation objectives. However, removal of arable land from production imposes an opportunity cost associated with loss in revenue from commodities that otherwise would have been produced. The Habitat Buffers for Upland Birds practice (CP-33) under the Continuous Conservation Reserve Program is a targeted conservation practice designed to increase northern bobwhite populations in agricultural landscapes. However, establishing CP-33 buffers on profitable farmland may be incompatible with economic objectives of landowners. To determine how CP-33 enrollment influenced field profitability and bobwhite abundance; I simulated CP-33 buffers on crop fields across a range of commodity prices and modeled profitability and predicted bobwhite abundance. CP-33 increased field revenue on a percentage of fields at all commodity prices and increased bobwhite abundance up to 30%