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This paper examines the cost structure and cost efficiency for an unbalanced sample of 42 animal feed and 115 farm petroleum co‐operatives in Canada over the period 1984‐2001 using heterogeneous technology stochastic frontier models. The parameter estimates of the cost frontier and the resulting cost efficiency scores indicate there are statistically and economically significant cost inefficiencies. Further analysis revealed that financial structure and firm size have likely contributed to variations in cost efficiency. Obtaining sufficient equity capital is expected to improve co‐operative efficiency

The purpose of this paper is to evaluate cow-calf producer incentive to adopt innovations in traits with important environmental and economic implications for the beef supply chain.

A whole farm multi-year farm optimization model that tracks changes in discounted net returns and methane emissions from the use of newer DNA-related technologies to breed for feed efficient cattle is developed. The analysis is situated within the context of whole beef cattle supply chain. This allows for the derivation of the entire value and environmental impact of the innovation, and the decomposition of value by different participants. The impact of different policies that can stimulate producer uptake and the diffusion of the innovation is also addressed.

The results of the study showed that whilst the use of the breeding technology yielded positive economic and environmental benefits to all producers in the supply chain, primary adopters were unlikely to adopt. This paper finds evidence of the misalignment in incentives within the supply chain with a significant proportion of the additional value going to producers who do not incur any additional cost from the adoption of the innovation. The study also highlighted the role of both public and market-based mechanisms in the innovation diffusion process.

This paper is unique as it is the first study that addresses producer incentive to adopt genomic selection for feed efficiency across the entire beef cattle supply chain, and incorporates both economic and environmental outcomes.

The agribusiness co-operative sector in Canada has been affected by ongoing changes in economic, political, and social policies. Increased competition from local investor-owned firms and multinational companies, deregulation and globalization of trade and increased concentration of suppliers and purchasers have put tremendous competitive pressure on agribusiness marketing co-operatives. The enhanced level of competitive rivalry may force co-operatives into lowering costs and prices. Improvement in cost or operating efficiency of agribusiness marketing co-operatives may be crucial as changes in regulation, technology, and other market developments bring into question the long-term viability of co-operative businesses. Therefore, information as to the efficiency with which agribusiness co-operative firms operate would be useful.

The purpose of this study is to analyze the horizontal expansion of Burayu Town between 1990 and 2020. The study typically acts as a baseline for integrated spatial planning in small- and medium-sized towns, which will help to plan sustainable utilization of land.

Landsat5-TM, Landsat7 ETM+, Landsat5 TM and Landsat8 OLI were used in the study, along with other auxiliary data. The LULC map classifications were generated using the Random Forest Package from the Comprehensive R Archive Network. Post-classification, spatial metrics, and per capita land consumption rate were used to understand the manner and rate of expansion of Burayu Town. Focus group discussions and key informant interviews were also used to validate land use classes through triangulation.

The study found that the built-up area was the most dynamic LULC category (85.1%) as it increased by over 4,000 ha between 1990 and 2020. Furthermore, population increase did not result in density increase as per capita land consumption increased from 0.024 to 0.040 during the same period.

As a result of financial limitations, there were no high-resolution satellite images available, making it challenging to pinpoint the truth as it is on the ground. Including senior citizens in the study region allowed this study to overcome these restrictions and detect every type of land use and cover.

Data on urban growth are useful for planning land uses, estimating growth rates and advising the government on how best to use land. This can be achieved by monitoring and reviewing development plans using satellite imaging data and GIS tools.

The use of Random Forest for image classification and the employment of local knowledge to validate the accuracy of land cover classification is a novel approach to properly customize remote sensing applications.