The Growing Heterogeneity in the Farm Sector and Its Implications*

Weersink, Alfons

doi:10.1111/cjag.12163

Cited by 19 publications

(26 citation statements)

References 11 publications

(17 reference statements)

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“…Emerging Big Data analytical platforms, e.g., cloud computing and machine learning algorithms, drive artificial intelligence [26,[71][72][73][74] and have supported a relevant growth in the volume, velocity, variety, and veracity of data generated in agriculture [26,75,76]. Subsequently, agricultural data are quickly providing a main driver not only of revolutions in output and the food chain, but also in environmental management [67,75]. Agriculture 4.0 technologies refer to production systems that deploy robotics, sensors, and Big Data analytics allowing farmers to manage their farms at detailed spatial and temporal scales [26].…”

Section: Virtualization Of An Agro-food Supply Chainmentioning

confidence: 99%

“…Agriculture 4.0 technologies refer to production systems that deploy robotics, sensors, and Big Data analytics allowing farmers to manage their farms at detailed spatial and temporal scales [26]. Though precision agricultural technologies have been in use for about a decade and normally take the form of yield monitors in cropping systems and robotic milking parlors for dairy, the step of innovation has picked up since the cost of sensors and robotics has fallen [67]. The constituent elements are products (inputs, such as pesticides and fertilizers, mature agricultural products, harvests, shipments and orders, packaging, any ingredients for processing) that flow between transformations and actors (e.g., agricultural producers, product processors, traders) ( Figure 5).…”

Section: Virtualization Of An Agro-food Supply Chainmentioning

confidence: 99%

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Revolution 4.0: Industry vs. Agriculture in a Future Development for SMEs

et al. 2019

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The present review retraces the steps of the industrial and agriculture revolution that have taken place up to the present day, giving ideas and considerations for the future. This paper analyses the specific challenges facing agriculture along the farming supply chain to permit the operative implementation of Industry 4.0 guidelines. The subsequent scientific value is an investigation of how Industry 4.0 approaches can be improved and be pertinent to the agricultural sector. However, industry is progressing at a much faster rate than agriculture. In fact, already today experts talk about Industry 5.0. On the other hand, the 4.0 revolution in agriculture is still limited to a few innovative firms. For this reason, this work deals with how technological development affects different sectors (industry and agriculture) in different ways. In this innovative background, despite the advantages of industry or agriculture 4.0 for large enterprises, small- and medium-sized enterprises (SMEs) often face complications in such innovative processes due to the continuous development in innovations and technologies. Policy makers should propose strategies, calls for proposals with aim of supporting SMEs to invest on these technologies and making them more competitive in the marketplace.

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Section: Virtualization Of An Agro-food Supply Chainmentioning

confidence: 99%

Section: Virtualization Of An Agro-food Supply Chainmentioning

confidence: 99%

Revolution 4.0: Industry vs. Agriculture in a Future Development for SMEs

et al. 2019

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“…Historical analysis links increases in heterogeneity among members to factors such as divergence in farm size, distinct farm-level strategies, cooperative consolidation through merger and acquisition, and changing consumer demand [46][47][48]. However, internal organizational processes such as divergent proportions of allocated equity and emergent special interest groups seeking to pressure management are also recognized as sources of heterogeneity increasing over the cooperative lifespan [37,49,50].…”

Section: Diachronic Increases In Heterogeneitymentioning

confidence: 99%

“…Founders may have possessed relatively homogeneous interests; however, a natural exodus of founding members occurs [46,[51][52][53]. Replacement entrants may introduce heterogeneity in preferences.…”

Section: Diachronic Increases In Heterogeneitymentioning

confidence: 99%

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A Life Cycle Explanation of Cooperative Longevity

Cook

2018

Sustainability

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A micro analytical, interdisciplinary informed framework is presented to postulate why and how some cooperatives endure for long periods of time. This five-phase framework was developed through an extended research process employing inductive and deductive approaches. The paper concludes that cooperative longevity is associated with multiple factors, primarily among them, ability to adapt and ameliorate frictions and subgroup factions. This adaptability leads to multi "life cycles". Cooperative multi life cycle regeneration is facilitated by a learned and embedded process called "cooperative genius".

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Limits to Profit Maximization as a Guide to Behavior Change

Weersink¹,

Fulton²

2020

Applied Eco Perspectives Pol

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Economists have long been interested in why farmers decide to adopt new technologies. Modeling decision making requires modeling behavior; the key behavioral assumption is profit maximization. In addition, most empirical studies assume a binary decision—adopt or not. This paper argues that adoption should be understood as a process with multiple stages in which the final decision to use the new technology only occurs if the previous stages are completed. While profit considerations are clearly important, particularly in the later stages of the process, they need to be supplemented with other social and cognitive considerations, particularly in the early stages. Understood this way, profit maximization assumptions can provide predictions for the upper or lower bounds of adoption. The adoption rate suggested by profit maximization will be an upper bound if noneconomic factors are expected to either slow down or deter adoption, while that rate will be a lower bound if noneconomic factors are expected to encourage adoption. To capture these elements, econometric models need to pay attention to the timing of decisions and use techniques that condition later‐stage decisions on previous stage outcomes. In addition, the paper suggests the use of expert systems that have economic, learning and social aspects explicitly built into them would be valuable.

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The Growing Heterogeneity in the Farm Sector and Its Implications*

Abstract: The farm sector has moved from one that was very homogeneous to one with significant differences in size and/or orientation. The decline in the number of "average-sized" farm

Cited by 19 publications

References 11 publications

Revolution 4.0: Industry vs. Agriculture in a Future Development for SMEs

Revolution 4.0: Industry vs. Agriculture in a Future Development for SMEs

A Life Cycle Explanation of Cooperative Longevity

Limits to Profit Maximization as a Guide to Behavior Change

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