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Bryant, Christopher; Smit, Barry; Brklacich, Michael; Johnston, Tom; Smithers, John; Chjotti, Quentin; Singh, Bhawan

doi:10.1023/a:1005653320241

Cited by 225 publications

(31 citation statements)

References 27 publications

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“…Similar increases in grain sorghum, corn and soybean production were observed in the study region during the period as a result of hybrid improvement and adoption of better agronomic practices Assefa and Staggenborg 2011;Assefa et al 2012;Rincker et al 2014). Agricultural changes are further influenced by policy changes, urban development, and economic and social changes (Bryant et al 2000). Such changes can mask the sensitivity of agriculture to climate change and variability.…”

Section: Validation Of Eisupporting

confidence: 56%

“…Step-1: Choose the study region: The scale component is important because agriculture can be analyzed at multiple scales (Bryant et al 2000). The region could be based on physical or administrative boundaries (e.g., plot, field, county, state, country, continent, globe), and/or based on climate (e.g., point, climate zone), and/or based on hydrology (e.g., watershed, river basin).…”

Section: Flowchart To Estimate Eimentioning

confidence: 99%

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A system’s approach to assess the exposure of agricultural production to climate change and variability

2016

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Estimating the exposure of agriculture to climate variability and change can help us understand key vulnerabilities and improve adaptive capacity, which is vital to secure and increase world food production to feed its growing population. A number of indices to estimate exposure are available in literature. However, testing or validating them is difficult and reveals a considerable variability, and no systematic methodology has been developed to guide users in selecting indices for particular applications. This need is addressed in this paper by developing a flowchart from a conceptual model that uses a system's approach. Also, we compare five approaches to estimate exposure indices (EIs) to study the exposure of agriculture to climate variability and change: single stressor-mean climate, single stressor-extreme climate, multiple stressor-mean climate, multiple stressor-extreme climate; and combinations of the above approaches. The developed flowchart requires gathering information on the region of study, including its agriculture, stressor(s), climate factor(s) (CF), period of interest and the method of aggregation. The flowchart was applied to a case study in Kansas to better understand the five approaches to estimate EIs and the implications of the choices made in each step on the estimated the exposure. The flowchart provides options that guide EI estimation by selecting the most appropriate stressor(s), associated CF(s), and aggregation methods when a detailed methodological analysis is possible, or proposes a default method when data or resources do not allow a detailed analysis. Climate adaptation involves integration of a multitude of factors across complex systems. A more standardized approach to Climatic Change (2016) 136:647-659

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Section: Validation Of Eisupporting

confidence: 56%

Section: Flowchart To Estimate Eimentioning

confidence: 99%

A system’s approach to assess the exposure of agricultural production to climate change and variability

2016

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“…The future conditions may favour increased agriculture in some areas of the RDR Basin, with a lengthening frostfree growth season. Increased summer precipitation could also enable regional crop diversification (Bryant et al, 2000;McGinn et al, 1999).…”

Section: Different Hydroclimatic Modelling With a Range Of Gcms Andmentioning

confidence: 99%

Climate change and hydrology at the prairie margin: Historic and prospective future flows of Canada's Red Deer and other Rocky Mountain rivers

Philipsen

Gill

Shepherd

et al. 2018

Hydrological Processes

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The South Saskatchewan River Basin of southern Alberta drains the transboundary central Rocky Mountains region and provides the focus for irrigation agriculture in Canada. Following extensive development, two tributaries, the Oldman and Bow rivers, were closed for further water allocations, whereas the Red Deer River (RDR) remains open. The RDR basin is at the northern limit of the North American Great Plains and may be suitable for agricultural expansion with a warming climate. To consider irrigation development and ecological impacts, it is important to understand the regional hydrologic consequences of climate change. To analyse historic trends that could extend into the future, we developed century‐long discharge records for the RDR, by coordinating data across hydrometric gauges, estimating annual flows from seasonal records, and undertaking flow naturalization to compensate for river regulation. Analyses indicated some coordination with the Pacific decadal oscillation and slight decline in summer and annual flows from 1912 to 2016 (−0.13%/year, Sen's slope). Another forecasting approach involved regional downscaling from the global circulation models, CGCMI‐A, ECHAM4, HadCM3, and NCAR‐CCM3. These projected slight flow decreases from the mountain headwaters versus increases from the foothills and boreal regions, resulting in a slight increase in overall river flows (+0.1%/year). Prior projections from these and other global circulation models ranged from slight decrease to slight increase, and the average projection of −0.05%/year approached the empirical trend. Assessments of other rivers draining the central and northern Rocky Mountains revealed a geographic transition in flow patterns over the past century. Flows from the rivers in Southern Alberta declined (around −0.15%/year), in contrast to increasing flows in north‐eastern British Columbia and the Yukon. The RDR watershed approaches this transition, and this study thus revealed regional differentiation in the hydrological consequences from climate change.

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“…The study tested whether they were influenced by socio-economic factors among them Global-GAP policy compliance. Other factors deemed to influence the modelled climate change perceptions were based on literature review and included respondents' socio-economic and institutional factors [31,32,37]. Factors likely to influence the farmers' perceptions were assessed by estimation of a model that allows the inclusion of these respondents' socio-economic and institutional factors as independent variables into the perception function.…”

Section: Binary Regression Of Factors Influencing Farmers' Climate Chmentioning

confidence: 99%

“…This situation is further complicated by consumers' increasing environmental conservation and food safety concerns in the developed countries [6,7]. These concerns have led most European Union (EU) countries providing industry service providers in designing suitable and more focused adaptation strategies for fresh fruit and vegetables farming [32][33][34][35]. A number of previous studies have applied factor analysis and regression models to determine the effect of agri-policies on farmers' climate change perceptions [46].…”

Section: Introductionmentioning

confidence: 99%

Effect of Global-GAP Policy on Climate Change Perceptions of Smallholder French Beans Farmers in Central and Eastern Regions, Kenya

Otieno

Ogutu

Mburu

et al. 2017

Climate

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Abstract:The risks posed by climate change to Sub Saharan Africa's (SSA) smallholder fresh export fruit and vegetables production are amplifying the significance of farmers' climate change perceptions in enhancing adoption of suitable adaptation strategies. Production of fresh export fruit and vegetables in Kenya has increasingly been done under the Global-GAP standard scheme by smallholder farmers to improve both environmental conservation and market access. The objective of this study was to determine the effect of Global-GAP policy on climate change perceptions of smallholder French beans farmers. The analysis was based on data collected from a random sample of 616 households interviewed in the Central and Eastern regions of Kenya. The study used principal component analysis (PCA) to extract farmers' key prevailing climate change perceptions and logit regression model to examine the effect of Global-GAP policy on climate change perceptions among other socio-economic factors. The PCA analysis extracted three components proxying for 'droughts', 'delay in rainy seasons', 'diseases and pests' and three proxying for 'hot days', 'floods', and 'diseases and pests' as summarizing maximum variance in the perceptions in the Central and Eastern region respectively. The common, study area-wide climate change perception was identified as incidence of diseases and pest. Logit regression analysis found that Global-GAP policy significantly influenced and improved farmers' probability of perceiving climate change. Other factors found to influence farmers' probability of having the identified climate change perceptions included regional specificity, access to agricultural extension service, access to credit, plot size, and soil fertility. The policy implication of this study is that the government and service providers should mainstream factors like Global-GAP compliance and regional considerations found to improve probability of perceiving climate change in awareness creation extension strategies, towards enhancing adoption of adaptation measures in the smallholder fruits and vegetables farming sector.

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Untitled

Cited by 225 publications

References 27 publications

A system’s approach to assess the exposure of agricultural production to climate change and variability

A system’s approach to assess the exposure of agricultural production to climate change and variability

Climate change and hydrology at the prairie margin: Historic and prospective future flows of Canada's Red Deer and other Rocky Mountain rivers

Effect of Global-GAP Policy on Climate Change Perceptions of Smallholder French Beans Farmers in Central and Eastern Regions, Kenya

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