Constraints on farmer adaptability in the Iowa-Cedar River Basin

Bitterman, Patrick; Bennett, David A.; Secchi, Silvia

doi:10.1016/j.envsci.2018.11.004

Cited by 21 publications

(16 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The impacts of CEEs could potentially be caused by various factors. First, different geographical conditions have different impacts on CEEs, requiring different methods of adaptation [6,10,21,25,34,35]. Additionally, the adaptation plan could also be based on the perceptions of households and their capacity [33].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Some studies have pointed out that different communities suffer differently from the impacts of CEE, even if they live in similar regions. Consequently, some scholars have conducted related studies to identify the divergence of impacts and adaptation strategies between communities [35]. Some scholars have distinguished adaptation strategies based on external and internal factors [6,10].…”

Section: Literature Reviewmentioning

confidence: 99%

“…As a result, strengthening the links between the community, related actors, stakeholders, and multiple levels of local government can contribute significantly to reducing the impacts of CEEs [10]. Furthermore, many scholars take into account gender issues, assuming that female-headed households are comparatively more vulnerable because they have less power in decision-making and find it difficult to access related training and updated information [6,21,[35][36][37]. When analyzing the adaptation method from the point of view of households, it is necessary to pay more attention to other factors such as natural resources, represented by land scale; the human capital, represented by education level and birth rate of the household; training course participation; and social capital such as mass organization networks as well as the number of livelihood activities [20].…”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Adapting to Climate Extreme Events Based on Livelihood Strategies: Evidence from Rural Areas in Thua Thien Hue Province, Vietnam

2020

View full text Add to dashboard Cite

The key farming communities in Vietnam are generally poor and lack resources to adapt to and mitigate the impacts of climate extreme events (CEEs), but the extent of their adaptation strategies is not well understood. This study aims to analyze the impacts of CEEs, current barriers, and adaptation strategies based on three categories of livelihood strategies. The classification method is first used to divide the livelihood strategy into these three categories, and a multinomial logistic model (MLS) is then applied to determine the set of parameters that affect adaptation options. CEEs result in significant damage in terms of both financial and health dimensions. Various barriers remain, such as the low capacity of relevant staff, lack of local budgets, and outdated methods being used to estimate and mitigate the impacts of CEEs. Notably, there were over 44%, and 28% conducted reactive and proactive adaptations, respectively, while a high percentage of households did not implement at least one adaptation method, around 27%. The MLS model is able to explain about 51.2% of the driving factors that influence adaptation strategies. In addition, the behavior of households in choosing adaptation methods shows the difference between perceptions of CEEs’ impacts and livelihood strategy profiles. There is a need for a package of adaptive solutions to address the impacts of CEEs that cover the many different household perspectives and involve stakeholders at multiple levels.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Adapting to Climate Extreme Events Based on Livelihood Strategies: Evidence from Rural Areas in Thua Thien Hue Province, Vietnam

2020

View full text Add to dashboard Cite

show abstract

“…Household-related factors impacting farmers' low-carbon technology adoption behaviors include their education level [11], their environmental understanding and perceptions [12], the age of the head of household [13], and their gender [14,15]. Land-based factors impacting low-carbon technology adoption by farmers include land scale [16,17], land ownership [18], natural capital [14], and policy factors [19]. Risk perceptions have also been found to play an important role, including the influence of risk tolerance [20], the degree of risk aversion [21], and the perceived risk [17,22,23].…”

Section: Introductionmentioning

confidence: 99%

Livelihoods, Technological Constraints, and Low-Carbon Agricultural Technology Preferences of Farmers: Analytical Frameworks of Technology Adoption and Farmer Livelihoods

Zhao

Hong

2021

IJERPH

View full text Add to dashboard Cite

In the context of achieving carbon neutrality, it is scientifically important to quantitatively explore the relationships among livelihoods, technological property constraints, and the selection of low-carbon technologies by farmers to promote agricultural modernization and carbon neutrality in the agricultural sector of China. Based on the scientific classifications of farmer capital and low-carbon agricultural technologies, a farmer technology selection theory model considering capital constraints was developed in this study. Microcosmic survey data were collected from farmers in the Jiangsu province for empirical testing and analyses. A total of four low-carbon technologies related to fertilizer usage and three types of farmers’ livelihoods and their relationships were examined by using a logistic model. The results showed the existence of a significant coupling relationship between the intrinsic decision mechanism involved in selecting low-carbon agricultural technology and the properties of low-carbon agricultural technology for different types of farmers. Significant differences exist in the selection of different low-carbon technologies among large-scale farmers, mid-level part-time farmers, and low-level (generally small) part-time farmers. (1) When selecting technology, large-scale farmers are more inclined to accept capital-intensive, low-carbon technologies, such as new varieties, straw recycling, soil testing, and formulated fertilization. Mid-level part-time farmers are more inclined to accept capital intensive, labor saving, or low risk low-carbon agricultural technologies. In contrast, low-level part-time farmers are inclined to accept labor intensive technologies to reduce capital constraints and agricultural risks. (2) Large-scale farmers and low-level part-time farmers are influenced by household and plot characteristics, while mid-level part-time farmers are more influenced by plot characteristics. (3) Households with capital constraints created by differentiated livelihoods face challenges adopting capital-intensive low-carbon agricultural technologies, such as straw recycling, new varieties, soil testing, and formulated fertilization. However, farmers with stronger constraints in the areas of land and labor are more inclined to accept labor-saving technologies, such as soil testing and formulated fertilization technology. Moreover, farmers with stronger risk preferences tend to accept high-risk technologies, such as new technologies like straw recycling. The results of this study can provide a scientific basis for formulating carbon emission reduction policies and low-carbon technology policies for the agricultural sector.

show abstract

“…Ji et al [31] have found that the shortage of a labor force can be supplemented by increasing capital input. In addition, other reasons may also lead to investment adjustment behavior such as attitudes toward capital input [4], the amount of agricultural income [32], expected financial benefits from capital input [33], and differences in adjustment capabilities [34].…”

Section: Introductionmentioning

confidence: 99%

How Farmers Make Investment Decisions: Evidence from a Farmer Survey in China

et al. 2019

View full text Add to dashboard Cite

Based on the grain production data hand collected in Mid-East China, a multinomial Logit model was employed to analyze factors that are critical to farmers’ investment decisions in food production. Reasonable explanations are provided to help understand differences between expected farmers’ investments in grain production and the actual results. It was found that the cost of machinery and the number of farmers is key factors affecting farmers’ willingness to adjust investment. Further research shows that most of the farmers who had the willingness to adjust investment did not implement the adjustments in the short-term. From the micro-adaptability expectation perspective, the time that it takes to adjust the planting area could explain farmers’ investment adjustment intention and the behavior. From the macro-investment perspective, short-term output elasticity of physical capital is less than long-term output elasticity. The differences between farmers’ willingness to invest and the actual results are therefore generated. These findings suggest that it is necessary to strengthen the application of big data technology in agriculture in order to improve the platforms’ efficiency in data releasing and reaching out to farmers to provide more accurate advice regarding investment adjustment.

show abstract

Constraints on farmer adaptability in the Iowa-Cedar River Basin

Cited by 21 publications

References 55 publications

Adapting to Climate Extreme Events Based on Livelihood Strategies: Evidence from Rural Areas in Thua Thien Hue Province, Vietnam

Adapting to Climate Extreme Events Based on Livelihood Strategies: Evidence from Rural Areas in Thua Thien Hue Province, Vietnam

Livelihoods, Technological Constraints, and Low-Carbon Agricultural Technology Preferences of Farmers: Analytical Frameworks of Technology Adoption and Farmer Livelihoods

How Farmers Make Investment Decisions: Evidence from a Farmer Survey in China

Contact Info

Product

Resources

About