92. Precision agriculture adoption, farm size and soil variability

2021 ◽  
Author(s):  
D. Schimmelpfennig ◽  
J. Lowenberg-DeBoer
Keyword(s):  
Precision Agriculture ◽  
Farm Size ◽  
Soil Variability

scholarly journals Economics of autonomous equipment for arable farms

2021 ◽  
Author(s):  
James Lowenberg-DeBoer ◽  
Kit Franklin ◽  
Karl Behrendt ◽  
Richard Godwin
Keyword(s):  
Precision Agriculture ◽  
Environmental Performance ◽  
Crop Production ◽  
Practical Experience ◽  
Production Costs ◽  
Farm Size ◽  
Medium Size ◽  
Economic Implications ◽  
The United Kingdom ◽  
Crop Agriculture

AbstractBy collecting more data at a higher resolution and by creating the capacity to implement detailed crop management, autonomous crop equipment has the potential to revolutionise precision agriculture (PA), but unless farmers find autonomous equipment profitable it is unlikely to be widely adopted. The objective of this study was to identify the potential economic implications of autonomous crop equipment for arable agriculture using a grain-oilseed farm in the United Kingdom as an example. The study is possible because the Hands Free Hectare (HFH) demonstration project at Harper Adams University has produced grain with autonomous equipment since 2017. That practical experience showed the technical feasibility of autonomous grain production and provides parameters for farm-level linear programming (LP) to estimate farm management opportunities when autonomous equipment is available. The study shows that arable crop production with autonomous equipment is technically and economically feasible, allowing medium size farms to approach minimum per unit production cost levels. The ability to achieve minimum production costs at relatively modest farm size means that the pressure to “get big or get out” will diminish. Costs of production that are internationally competitive will mean reduced need for government subsidies and greater independence for farmers. The ability of autonomous equipment to achieve minimum production costs even on small, irregularly shaped fields will improve environmental performance of crop agriculture by reducing pressure to remove hedges, fell infield trees and enlarge fields.

scholarly journals Automatic Section Control Technologies and GPS Auto-guidance Systems Adoption in Cotton Production

2018 ◽  
Vol 10 (7) ◽  
pp. 282 ◽  
Author(s):  
Brittani Edge ◽  
Margarita Velandia ◽  
Christopher Boyer ◽  
James Larson ◽  
Dayton Lambert ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Farm Size ◽  
Cotton Production ◽  
Probit Regression ◽  
Field Geometry ◽  
Production Practices ◽  
Cotton Farmers ◽  
Using Data ◽  
Control Technologies ◽  
Automatic Section Control

Using data from a survey of cotton producers in 14 US states, and a bivariate probit regression, this study examined the effects of the following measured parameters on the adoption of Automatic Section Control (ASC) technologies and GPS Auto-Guidance (AG) systems: age, education, farm size, field geometry, information sources, as well as the use of specific production practices and other Precision Agriculture (PA) technologies. Results suggest that younger, more educated producers, consulting farm dealers for information about PA technologies, using other PA technologies, and managing larger farming operations located in counties with more irregularly shaped fields are more likely to adopt ASC technologies and AG systems. Predicted adoption probabilities estimated using regression results suggest the use of other PA technologies and farm dealers as a source of precision farming information have the largest impact on the probability of adopting ASC by cotton farmers. Additionally, these results suggest farmers with operations in eastern Arkansas, western Tennessee, and a couple of counties in middle Tennessee are more likely to adopt ASC technologies. Producers in these regions had the highest percentages of users of other PA technologies and farm dealers to obtain PA information.

Precision Agriculture

2021 ◽  
pp. 23-45 ◽  
Author(s):  
Waleed Fouad Abobatta
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Information Technology ◽  
Social Interaction ◽  
Precision Agriculture ◽  
Geographical Information Systems ◽  
Advanced Technology ◽  
Geographical Information ◽  
Farm Size ◽  
World Population

Precision agriculture is a management system that aims to reduce inputs like seeds, water, and energy; protect the environment; and maximize profitability. Precision agriculture uses advanced technology like positioning technology, geographical information systems, satellite navigation, and remote sensing. There are different factors affect the adoption of precision agriculture like farm size, legal affairs, and social interaction. Under climate change and increases in world population, adoption of precision agriculture could assist farmers to face various challenges to achieve ideal production and maximizing profitability. Information, technology, and management are considered the backbone of the precision agriculture system, and combining these elements reduces inputs and maximizes productivity. Different threats attacked precision agriculture including threats to confidentiality, threats to integrity, threats to availability, and crowding of the spectrum signal. This chapter explains the different roles of precision agriculture in developing agricultural production.

scholarly journals Assessing the financial and environmental impacts of precision irrigation in a humid climate 

2019 ◽  
Vol 46 (No. 1) ◽  
pp. 43-52 ◽  
Author(s):  
Daniel El Chami ◽  
Jerry W. Knox ◽  
André Daccache ◽  
Edward Keith Weatherhead
Keyword(s):  
Precision Agriculture ◽  
Energy Savings ◽  
Irrigation System ◽  
Cost Benefit ◽  
Added Value ◽  
Soil Variability ◽  
Humid Climate ◽  
Water Savings ◽  
Additional Equipment ◽  
Precision Irrigation

Precision agriculture is increasingly used where in-field spatial variability exists; however, the benefits of its use in humid climates are less apparent. This paper reports on a cost-benefit assessment of precision irrigation with variable rate technique (VRI) versus conventional irrigation, both compared to rainfed production, using a travelling hose-reel irrigator fitted with a boom on onions in eastern England. Selected environmental outcomes including water savings and CO<sub>2</sub>e emissions are evaluated. The modelled precision irrigation system, which responds to soil variability, generates better environmental outcomes than the conventional system in terms of water savings and reduced CO<sub>2</sub>e emissions (22.6% and 23.0% lower, respectively). There is also an increase in the ‘added value’ of the irrigation water used (£3.02/m<sup>3</sup> versus £2.36/m<sup>3</sup>). Although precision irrigation leads to significant financial benefits from water and energy savings, these alone do not justify the additional equipment investment costs. However, any changes in yield or quality benefits, equipment costs or greater soil variability than on this site would make investment in precision irrigation more viable. 

scholarly journals Clustering Tools for Integration of Satellite Remote Sensing Imagery and Proximal Soil Sensing Data

2019 ◽  
Vol 11 (9) ◽  
pp. 1036 ◽  
Author(s):  
Md Saifuzzaman ◽  
Viacheslav Adamchuk ◽  
Roberto Buelvas ◽  
Asim Biswas ◽  
Shiv Prasher ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Data Clustering ◽  
Bare Soil ◽  
Neighborhood Search ◽  
Soil Variability ◽  
Agricultural Fields ◽  
Essential Information ◽  
Spatial Continuity ◽  
Proximal Soil Sensing

Remote sensing (RS) and proximal soil sensing (PSS) technologies offer an advanced array of methods for obtaining soil property information and determining soil variability for precision agriculture. A large amount of data collected by these sensors may provide essential information for precision or site-specific management in a production field. Data clustering techniques are crucial for data mining, and high-density data analysis is important for field management. A new clustering technique was introduced and compared with existing clustering tools to determine the relatively homogeneous parts of agricultural fields. A DUALEM-21S sensor, along with high-accuracy topography data, was used to characterize soil variability in three agricultural fields situated in Ontario, Canada. Sentinel-2 data assisted in quantifying bare soil and vegetation indices (VIs). The custom Neighborhood Search Analyst (NSA) data clustering tool was implemented using Python scripts. In this algorithm, part of the variance of each data layer is accounted for by subdividing the field into smaller, relatively homogeneous, areas. The algorithm’s attributes were illustrated using field elevation, shallow and deep apparent electrical conductivity (ECa), and several VIs. The unique feature of this proposed protocol was the successful development of user-friendly and open source options for defining the spatial continuity of each group and for use in the zone delineation process.

scholarly journals Status quo of adoption of precision agriculture enabling technologies in Swiss plant production

2020 ◽  
Vol 21 (6) ◽  
pp. 1327-1350 ◽  
Author(s):  
Tanja Groher ◽  
Katja Heitkämper ◽  
Achim Walter ◽  
Frank Liebisch ◽  
Christina Umstätter
Keyword(s):  
Precision Agriculture ◽  
The State ◽  
Plant Production ◽  
Physical Workload ◽  
Farm Size ◽  
Small Scale ◽  
Full Potential ◽  
Electronic Measuring ◽  
Adoption Rate ◽  
Novel Technologies

Abstract This paper presents the state of application of Precision Agricultural enabling Technology (PAT) in Swiss farms as an example for small-scale, highly mechanised Central European agriculture. Furthermore, correlations between farm and farmers’ characteristics and technology adoption were evaluated. Being part of a comprehensive and representative study assessing the state of mechanisation and automation in Swiss agriculture, this paper focuses on the adoption of Driver Assistance Systems (DAS) and activities in which Electronic Measuring Systems (EMS) are used. The adoption rate of DAS was markedly higher compared to EMS in all agricultural enterprises. The adoption rate was highest for high-value enterprise vegetables and surprisingly low for the high-value enterprise grapes. The results of a binary logistic regression showed that farmers located in the mountain zone were less likely to adopt PAT compared to farmers in the valley. Small farm size correlated with low adoption rates and vice versa showing adoption happens country-specific in the upper farm size distribution. The results show the potential for novel technologies to be adopted by farmers of high-value products. Furthermore, technologies have been partially used to reduce physical workload but not yet to evaluate crop or management performance to support decisions. However, automatic collection and forwarding of data is a fundamental step towards Smart Farming realizing its full potential in the future.

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