scholarly journals Farm Types and Precision Agriculture Adoption: Crops, Regions, Soil Variability, and Farm Size

2020 ◽  
Author(s):  
David Schimmelpfennig ◽  
Jess Lowenberg-DeBoer
Author(s):  
James Lowenberg-DeBoer ◽  
Kit Franklin ◽  
Karl Behrendt ◽  
Richard Godwin

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.


2001 ◽  
Vol 33 (1) ◽  
pp. 35-48 ◽  
Author(s):  
Vangelis Tzouvelekas ◽  
Christos J. Pantzios ◽  
Christos Fotopoulos

AbstractUsing recent advances in the stochastic production frontier framework, this paper presents an empirical analysis of technical, allocative and economic efficiency of a sample of organic and conventional cotton farms located in Greece. The results suggest that both farm types in the sample examined are technically, allocatively and economically inefficient. Farmer's age and education and farm size are important factors in explaining differentials in efficiency estimates. In comparative terms, organic farms exhibit lower efficiency scores vis-à-vis their conventional counterparts in terms of technical and economic efficiency; regarding allocative efficiency both farm types are almost equally inefficient. Low efficiency scores in both types of farming may be attributed to the respective intervention policies of the last 20 years.


2018 ◽  
Vol 10 (7) ◽  
pp. 282 ◽  
Author(s):  
Brittani Edge ◽  
Margarita Velandia ◽  
Christopher Boyer ◽  
James Larson ◽  
Dayton Lambert ◽  
...  

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.


Author(s):  
Waleed Fouad Abobatta

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.


2020 ◽  
Vol 50 (7) ◽  
Author(s):  
Silvana Builes Gaitan ◽  
Marcela Duque Ríos

ABSTRACT: A typology of avocado cv. Hass farms was constructed based on information collected from productive units in Antioquia, Colombia. The study aimed to provide key information about the farms for those involved in the design of programs and public policies directed to growers. The data were scrutinized through Multiple Correspondence Analysis (MCA) and Cluster Analysis. The sample was made up of 214 growers of the crop. Quantitative and qualitative variables were employed for the MCA, which rendered 40 dimensions, out of which 16 accounted for 70% of the total inertia (variance) found in the data. These 16 dimensions were used as input for the Cluster Analysis, which provided the following results: 52%, 32% and 15% of the farms were located in Clusters 1, 2 and 3, respectively. The identified farm types can be associated to peasant (Cluster 1) and commercial (Cluster 3) agricultural schemes, plus a transition between them (Cluster 2). The most discriminating variables regarding such categorization were: farm size, farm registry at ICA (Colombian Agricultural Institute), Avocado cv. Hass yield, labor source, presence of crops other than avocado, existence of formal commercial alliances and technical assistance type. The percentage of farmers belonging to grower organizations was high in the three clusters. Growers in Clusters 3 and 2 had established formal commercial alliances which enabled them to access better sale prices for the crop. A trend towards establishing cv. Hass as a monocrop is observed, which may threaten both crop biodiversity and the food security of peasant growers.


2019 ◽  
Vol 46 (No. 1) ◽  
pp. 43-52 ◽  
Author(s):  
Daniel El Chami ◽  
Jerry W. Knox ◽  
André Daccache ◽  
Edward Keith Weatherhead

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. 


2019 ◽  
Vol 11 (9) ◽  
pp. 1036 ◽  
Author(s):  
Md Saifuzzaman ◽  
Viacheslav Adamchuk ◽  
Roberto Buelvas ◽  
Asim Biswas ◽  
Shiv Prasher ◽  
...  

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.


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