The role of smartphone-based weather information acquisition on climate change perception accuracy: Cross-country evidence from Kyrgyzstan, Mongolia and Uzbekistan
暂无分享,去创建一个
[1] R. Finger,et al. The adoption of pesticide-free wheat production and farmers' perceptions of its environmental and health effects , 2022, Ecological Economics.
[2] M. Disse,et al. Development and application of high resolution SPEI drought dataset for Central Asia , 2022, Scientific Data.
[3] Wanyun Shao,et al. Public awareness and perceptions of drought: A case study of two cities of Alabama , 2022, Risk, Hazards & Crisis in Public Policy.
[4] W. Y. Soh,et al. Defining drought from the perspective of Australian farmers , 2022, Climate Risk Management.
[5] V. Materia,et al. Smart farming technologies adoption: Which factors play a role in the digital transition? , 2022, Technology in Society.
[6] I. Bobojonov,et al. Mapping weather risk – A multi-indicator analysis of satellite-based weather data for agricultural index insurance development in semi-arid and arid zones of Central Asia , 2021, Climate Services.
[7] N. Sasaki,et al. Impacts of droughts and floods on croplands and crop production in Southeast Asia - An application of Google Earth Engine. , 2021, The Science of the total environment.
[8] B. Ahmed,et al. Perceived and actual risks of drought: household and expert views from the lower Teesta River Basin of northern Bangladesh , 2021, Natural Hazards.
[9] F. Santeramo,et al. Objective risk and subjective risk: The role of information in food supply chains. , 2021, Food research international.
[10] Upasna Sharma,et al. How do farmers perceive climate change? A systematic review , 2020, Climatic Change.
[11] A. van der Veen,et al. Farmers’ drought experience, risk perceptions, and behavioural intentions for adaptation: evidence from Ethiopia , 2020 .
[12] M. Uddin,et al. Determinants of adoption and adoption intensity of precision agriculture technologies: evidence from South Dakota , 2020, Precision Agriculture.
[13] Jikun Huang,et al. Does the application of ICTs facilitate rural economic transformation in China? Empirical evidence from the use of smartphones among farmers , 2020 .
[14] R. Stringer,et al. Accounting for diverse risk attitudes in measures of risk perceptions: A case study of climate change risk for small-scale citrus farmers in Indonesia , 2020, Land Use Policy.
[15] Eugenio Cavallo,et al. Drivers of farmers’ intention to adopt technological innovations in Italy: The role of information sources, perceived usefulness, and perceived ease of use , 2020 .
[16] Kelly K. Caylor,et al. Smallholder farmers' use of mobile phone services in central Kenya , 2020 .
[17] Fang Wang,et al. Impacts of Drought on Maize and Soybean Production in Northeast China During the Past Five Decades , 2020, International journal of environmental research and public health.
[18] Imran Khan,et al. Farm households’ risk perception, attitude and adaptation strategies in dealing with climate change: Promise and perils from rural Pakistan , 2020 .
[19] G. Danso-Abbeam,et al. Impact of Zai technology on farmers’ welfare: Evidence from northern Ghana , 2019, Technology in Society.
[20] S. Manteaw,et al. The determinants of mobile-phone usage among small-scale poultry farmers in Ghana , 2019 .
[21] Oliver Musshoff,et al. Understanding the adoption of smartphone apps in dairy herd management. , 2019, Journal of dairy science.
[22] O. Musshoff,et al. Smartphone adoption and use in agriculture: empirical evidence from Germany , 2019, Precision Agriculture.
[23] A. Prishchepov,et al. Climate change has likely already affected global food production , 2019, PloS one.
[24] L. Kumar,et al. Comparison between meteorological data and farmer perceptions of climate change and vulnerability in relation to adaptation. , 2019, Journal of environmental management.
[25] Marthe Wens,et al. Integrating human behavior dynamics into drought risk assessment—A sociohydrologic, agent‐based approach , 2019, WIREs Water.
[26] A. Khatri-Chhetri,et al. Spatial targeting of ICT-based weather and agro-advisory services for climate risk management in agriculture , 2019, Climatic Change.
[27] A. Renwick,et al. Off-farm work, smartphone use and household income: Evidence from rural China , 2018, China Economic Review.
[28] O. Musshoff,et al. Willingness to pay for smartphone apps facilitating sustainable crop protection , 2018, Agronomy for Sustainable Development.
[29] G. Henebry,et al. Large scale climate oscillation impacts on temperature, precipitation and land surface phenology in Central Asia , 2018, Environmental Research Letters.
[30] Søren Marcus Pedersen,et al. Farm and operator characteristics affecting adoption of precision agriculture in Denmark and Germany , 2018 .
[31] Anming Bao,et al. Spatial and temporal characteristics of droughts in Central Asia during 1966-2015. , 2018, The Science of the total environment.
[32] Jim W. Hall,et al. Assessing the Impacts of Extreme Agricultural Droughts in China Under Climate and Socioeconomic Changes , 2018 .
[33] C. Peng,et al. Effect of Drought on Agronomic Traits of Rice and Wheat: A Meta-Analysis , 2018, International journal of environmental research and public health.
[34] P. Nkegbe,et al. Does the Use of Mobile Phones by Smallholder Maize Farmers Affect Productivity in Ghana? , 2018 .
[35] Ludwig Theuvsen,et al. Adoption of precision agriculture technologies by German crop farmers , 2016, Precision Agriculture.
[36] Amir AghaKouchak,et al. Probabilistic estimates of drought impacts on agricultural production , 2017 .
[37] R. Finger,et al. Risk perceptions, preferences and management strategies: evidence from a case study using German livestock farmers , 2017 .
[38] Mário Otávio Batalha,et al. Factors influencing the adoption of Farm Management Information Systems (FMIS) by Brazilian citrus farmers , 2017, Comput. Electron. Agric..
[39] A. Cullen,et al. Perception of Climate Risk among Rural Farmers in Vietnam: Consistency within Households and with the Empirical Record , 2017, Risk analysis : an official publication of the Society for Risk Analysis.
[40] A. Jordaan,et al. Communal farmers' perception of drought in South Africa: Policy implication for drought risk reduction , 2016 .
[41] David C. Rose,et al. Decision support tools for agriculture: Towards effective design and delivery , 2016 .
[42] Hao Xu,et al. Decreased vegetation growth in response to summer drought in Central Asia from 2000 to 2012 , 2016, Int. J. Appl. Earth Obs. Geoinformation.
[43] Justin L. Huntington,et al. The Evaporative Demand Drought Index. Part I: Linking Drought Evolution to Variations in Evaporative Demand , 2016 .
[44] Surabhi Mittal,et al. Socio-economic Factors Affecting Adoption of Modern Information and Communication Technology by Farmers in India: Analysis Using Multivariate Probit Model , 2016 .
[45] Feng Gao,et al. The Evaporative Stress Index as an indicator of agricultural drought in Brazil: An assessment based on crop yield impacts , 2016 .
[46] B. Sultan,et al. Perceptions of recent rainfall changes in Niger: a comparison between climate-sensitive and non-climate sensitive households , 2016, Climatic Change.
[47] S. Van Passel,et al. Determinants of risk behaviour: effects of perceived risks and risk attitude on farmer’s adoption of risk management strategies , 2016 .
[48] Xiaoquan Zhao,et al. Local Climate Experts: The Influence of Local TV Weather Information on Climate Change Perceptions , 2015, PloS one.
[49] B. Bravo‐Ureta,et al. Farmers’ perception of climate change in mediterranean Chile , 2015, Regional Environmental Change.
[50] T. Filatova,et al. Empirical Analysis of Farmers' Drought Risk Perception: Objective Factors, Personal Circumstances, and Social Influence , 2015, Risk analysis : an official publication of the Society for Risk Analysis.
[51] S. Linden. The social-psychological determinants of climate change risk perceptions: Towards a comprehensive model , 2015 .
[52] J. K. Rød,et al. Climate change, natural hazards, and risk perception: the role of proximity and personal experience , 2015 .
[53] P. Pearce,et al. Technology adoption by rural women in Queensland, Australia: Women driving technology from the homestead for the paddock , 2014 .
[54] Chris Funk,et al. Using constructed analogs to improve the skill of National Multi-Model Ensemble March–April–May precipitation forecasts in equatorial East Africa , 2014 .
[55] I. Bobojonov,et al. Impacts of climate change on farm income security in Central Asia: An integrated modeling approach , 2014 .
[56] I. Adolwa,et al. Evaluation of Information and Communication Technology Utilization by Small Holder Banana Farmers in Gatanga District, Kenya , 2014 .
[57] Qi Hu,et al. Temperature Changes in Central Asia from 1979 to 2011 Based on Multiple Datasets , 2014 .
[58] S. Ogutu,et al. Impact of Information and Communication Technology-Based Market Information Services on Smallholder Farm Input Use and Productivity: The Case of Kenya , 2013 .
[59] O. Ayinde,et al. Analysis of Climate Change and Rural Farmers’ Perception in North Central Nigeria , 2013 .
[60] Ortwin Renn,et al. The Risk Perception Paradox—Implications for Governance and Communication of Natural Hazards , 2013, Risk analysis : an official publication of the Society for Risk Analysis.
[61] M. Hoerling,et al. Anatomy of an Extreme Event , 2013 .
[62] L. Stringer,et al. Is rainfall really changing? Farmers’ perceptions, meteorological data, and policy implications , 2013 .
[63] Dennis Fitzgerald,et al. Do people “personally experience” global warming, and if so how, and does it matter? , 2013 .
[64] N. K. Kumar,et al. Attitude of Farmers towards Kisan Call Centres , 2012 .
[65] Erik Bohlin,et al. An analysis of mobile Internet access in Thailand: Implications for bridging the digital divide , 2012, Telematics Informatics.
[66] Surabhi Mittal,et al. How mobile phones contribute to growth of small farmers? Evidence from India , 2012 .
[67] Glenn D. Pederson,et al. ICT-based market information and adoption of agricultural seed technologies: Insights from Uganda , 2012 .
[68] B. Shiferaw,et al. Agricultural technology, crop income, and poverty alleviation in Uganda , 2011 .
[69] Kelvin Balcombe,et al. The Determinants of Technology Adoption by UK Farmers Using Bayesian Model Averaging: The Cases of Organic Production and Computer Usage , 2011 .
[70] Jenny C. Aker,et al. Dial 'A' for Agriculture: A Review of Information and Communication Technologies for Agricultural Extension in Developing Countries , 2011 .
[71] Steven W. Martin,et al. Factors Influencing the Selection of Precision Farming Information Sources by Cotton Producers , 2011, Agricultural and Resource Economics Review.
[72] Wang-Sheng Lee. Propensity score matching and variations on the balancing test , 2011, Empirical Economics.
[73] James A. Larson,et al. Intensity of Precision Agriculture Technology Adoption by Cotton Producers , 2011, Agricultural and Resource Economics Review.
[74] Luis H. Gutiérrez,et al. Determinants of ICT Usage among Low-Income Groups in Colombia, Mexico, and Peru , 2010, Inf. Soc..
[75] V. Singh,et al. A review of drought concepts , 2010 .
[76] J. Popke,et al. Climate Change, Drought, and Jamaican Agriculture: Local Knowledge and the Climate Record , 2010 .
[77] C. Ringler,et al. Perception of and adaptation to climate change by farmers in the Nile basin of Ethiopia , 2010, The Journal of Agricultural Science.
[78] Gudbrand Lien,et al. Probabilities for decision analysis in agriculture and rural resource economics: The need for a paradigm change , 2010 .
[79] S. Vicente‐Serrano,et al. A Multiscalar Drought Index Sensitive to Global Warming: The Standardized Precipitation Evapotranspiration Index , 2009 .
[80] M. Fay,et al. Adapting to Climate Change in Eastern Europe and Central Asia , 2010 .
[81] G. Henebry,et al. Climate and environmental change in arid Central Asia: impacts, vulnerability, and adaptations. , 2009 .
[82] J. Soussana,et al. Adapting agriculture to climate change , 2007, Proceedings of the National Academy of Sciences.
[83] M. Mendola. Agricultural technology adoption and poverty reduction: A propensity-score matching analysis for rural Bangladesh , 2007 .
[84] Sascha O. Becker,et al. Mhbounds - Sensitivity Analysis for Average Treatment Effects , 2007 .
[85] Mon-Chi Lio,et al. ICT and agricultural productivity: evidence from cross-country data , 2006 .
[86] Howard Kunreuther,et al. Disaster Mitigation and Insurance: Learning from Katrina , 2006 .
[87] Marco Caliendo,et al. Some Practical Guidance for the Implementation of Propensity Score Matching , 2005, SSRN Electronic Journal.
[88] Peter Meso,et al. Towards a model of consumer use of mobile information and communication technology in LDCs: the case of sub‐Saharan Africa , 2005, Inf. Syst. J..
[89] T. DiPrete,et al. 7. Assessing Bias in the Estimation of Causal Effects: Rosenbaum Bounds on Matching Estimators and Instrumental Variables Estimation with Imperfect Instruments , 2004 .
[90] Jeffrey P. Walker,et al. THE GLOBAL LAND DATA ASSIMILATION SYSTEM , 2004 .
[91] E. Rogers,et al. Diffusion of innovations , 1964, Encyclopedia of Sport Management.
[92] Jeffrey A. Smith,et al. Does Matching Overcome Lalonde's Critique of Nonexperimental Estimators? , 2000 .
[93] Viswanath Venkatesh,et al. Why Don't Men Ever Stop to Ask for Directions? Gender, Social Influence, and Their Role in Technology Acceptance and Usage Behavior , 2000, MIS Q..
[94] Petra E. Todd,et al. Matching As An Econometric Evaluation Estimator , 1998 .
[95] G. Chichilnisky. An Axiomatic Approach to Choice Under Uncertainty with Catastrophic Risks , 1996 .
[96] D. Rubin,et al. Constructing a Control Group Using Multivariate Matched Sampling Methods That Incorporate the Propensity Score , 1985 .
[97] D. Rubin,et al. The central role of the propensity score in observational studies for causal effects , 1983 .
[98] W. Haenszel,et al. Statistical aspects of the analysis of data from retrospective studies of disease. , 1959, Journal of the National Cancer Institute.
[99] S. Paparrizos,et al. Co-producing climate information services with smallholder farmers in the Lower Bengal Delta: How forecast visualization and communication support farmers’ decision-making , 2021, Climate Risk Management.
[100] Shi-chang Kang,et al. Impacts of climate change on the discharge and glacier mass balance of the different glacierized watersheds in the Tianshan Mountains, Central Asia , 2018 .
[101] V. K. Hariharan,et al. Mobile-based climate services impact on farmers risk management ability in India , 2018 .
[102] B. Gramig,et al. Climate change beliefs, risk perceptions, and adaptation behavior among Midwestern U.S. crop farmers , 2017 .
[103] C. Martius,et al. Income and irrigation water use efficiency under climate change: An application of spatial stochastic crop and water allocation model to Western Uzbekistan , 2016 .
[104] E. Lioubimtseva. A multi-scale assessment of human vulnerability to climate change in the Aral Sea basin , 2014, Environmental Earth Sciences.
[105] P. Jones,et al. Global warming and changes in drought , 2014 .
[106] A. Dai. Increasing drought under global warming in observations and models , 2013 .
[107] Dionysis Bochtis,et al. Conceptual model of fleet management in agriculture , 2010 .
[108] Ann-Renée Blais,et al. A Domain-Specific Risk-Attitude Scale: Measuring Risk Perceptions and Risk Behaviors , 2002 .
[109] Paul R. Rosenbaum,et al. Overt Bias in Observational Studies , 2002 .
[110] Donald A. Wilhite,et al. The Enigma of Drought , 1993 .
[111] Global agro-ecological zone V4 – Model documentation , 2022 .
[112] J. Angrist,et al. Journal of Economic Perspectives—Volume 15, Number 4—Fall 2001—Pages 69–85 Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments , 2022 .