A new approach to identifying the weather-crop yield functionals for assessing climate change consequences

Abstract Over the last decade, communities have become increasingly aware of the risks they face. They are threatened by natural disasters, which may be exacerbated by climate change and the movement of land masses. Growing globalization has made a pandemic due to the rapid spread of highly infectious diseases ever more likely. Societal discord breeds its own threats, not the least of which is the spread of radical ideologies giving rise to terrorism. The accelerating rate of technological change has bred its own social and economic risks. This widening spectrum of risk poses a difficult question to every community – how resilient will the community be to the extreme events it faces. In this paper, we present a new approach to answering that question. It is based on the stress testing of financial institutions required by regulators in the United States and elsewhere. It generalizes stress testing by expanding the concept of “capital” beyond finance to include the other “capitals” (e.g., human, social) possessed by a community. Through use of this approach, communities can determine which investments of its capitals are most likely to improve its resilience. We provide an example of using the approach, and discuss its potential benefits.

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Assessing the Sensitivity of Main Crop Yields to Climate Change Impacts in China

Atmosphere ◽

10.3390/atmos12020172 ◽

2021 ◽

Vol 12 (2) ◽

pp. 172

Author(s):

Yuan Xu ◽

Jieming Chou ◽

Fan Yang ◽

Mingyang Sun ◽

Weixing Zhao ◽

...

Keyword(s):

Climate Change ◽

Crop Yield ◽

Food Production ◽

Crop Yields ◽

Climatic Factors ◽

Climatic Data ◽

The South ◽

The North ◽

Output Elasticity ◽

The Impact

Quantitatively assessing the spatial divergence of the sensitivity of crop yield to climate change is of great significance for reducing the climate change risk to food production. We use socio-economic and climatic data from 1981 to 2015 to examine how climate variability led to variation in yield, as simulated by an economy–climate model (C-D-C). The sensitivity of crop yield to the impact of climate change refers to the change in yield caused by changing climatic factors under the condition of constant non-climatic factors. An ‘output elasticity of comprehensive climate factor (CCF)’ approach determines the sensitivity, using the yields per hectare for grain, rice, wheat and maize in China’s main grain-producing areas as a case study. The results show that the CCF has a negative trend at a rate of −0.84/(10a) in the North region, while a positive trend of 0.79/(10a) is observed for the South region. Climate change promotes the ensemble increase in yields, and the contribution of agricultural labor force and total mechanical power to yields are greater, indicating that the yield in major grain-producing areas mainly depends on labor resources and the level of mechanization. However, the sensitivities to climate change of different crop yields to climate change present obvious regional differences: the sensitivity to climate change of the yield per hectare for maize in the North region was stronger than that in the South region. Therefore, the increase in the yield per hectare for maize in the North region due to the positive impacts of climate change was greater than that in the South region. In contrast, the sensitivity to climate change of the yield per hectare for rice in the South region was stronger than that in the North region. Furthermore, the sensitivity to climate change of maize per hectare yield was stronger than that of rice and wheat in the North region, and that of rice was the highest of the three crop yields in the South region. Finally, the economy–climate sensitivity zones of different crops were determined by the output elasticity of the CCF to help adapt to climate change and prevent food production risks.

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Engineering Climate-Change-Resilient Crops: New Tools and Approaches

International Journal of Molecular Sciences ◽

10.3390/ijms22157877 ◽

2021 ◽

Vol 22 (15) ◽

pp. 7877

Author(s):

Fahimeh Shahinnia ◽

Néstor Carrillo ◽

Mohammad-Reza Hajirezaei

Keyword(s):

Climate Change ◽

Plant Growth ◽

Sustainable Agriculture ◽

Stress Tolerance ◽

Crop Yield ◽

Plant Science ◽

Chemical Treatments ◽

Coated Nanoparticles ◽

Rapid Climate Change ◽

Protective Resources

Environmental adversities, particularly drought and nutrient limitation, are among the major causes of crop losses worldwide. Due to the rapid increase of the world’s population, there is an urgent need to combine knowledge of plant science with innovative applications in agriculture to protect plant growth and thus enhance crop yield. In recent decades, engineering strategies have been successfully developed with the aim to improve growth and stress tolerance in plants. Most strategies applied so far have relied on transgenic approaches and/or chemical treatments. However, to cope with rapid climate change and the need to secure sustainable agriculture and biomass production, innovative approaches need to be developed to effectively meet these challenges and demands. In this review, we summarize recent and advanced strategies that involve the use of plant-related cyanobacterial proteins, macro- and micronutrient management, nutrient-coated nanoparticles, and phytopathogenic organisms, all of which offer promise as protective resources to shield plants from climate challenges and to boost stress tolerance in crops.

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Modeling impacts of climate change on crop yield and phosphorus loss in a subsurface drained field of Lake Erie region, Canada

Agricultural Systems ◽

10.1016/j.agsy.2021.103110 ◽

2021 ◽

Vol 190 ◽

pp. 103110

Author(s):

Zhaozhi Wang ◽

T.Q. Zhang ◽

C.S. Tan ◽

Lulin Xue ◽

Melissa Bukovsky ◽

...

Keyword(s):

Climate Change ◽

Crop Yield ◽

Lake Erie ◽

Phosphorus Loss ◽

Impacts Of Climate Change

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ARMA time series modelling of remote sensing imagery: A new approach for climate change studies

International Journal of Remote Sensing ◽

10.1080/01431160110109552 ◽

2002 ◽

Vol 23 (24) ◽

pp. 5225-5248 ◽

Cited By ~ 21

Author(s):

J. M. Piwowar ◽

E. F. Ledrew

Keyword(s):

Climate Change ◽

Remote Sensing ◽

Time Series ◽

New Approach ◽

Remote Sensing Imagery ◽

Time Series Modelling

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Modeling the Impact of Climate Changes on Crop Yield: Irrigated vs. Non-Irrigated Zones in Mississippi

Remote Sensing ◽

10.3390/rs13122249 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2249

Author(s):

Sadia Alam Shammi ◽

Qingmin Meng

Keyword(s):

Climate Change ◽

Crop Yield ◽

Crop Yields ◽

Negative Impact ◽

Positive Impact ◽

Information Criterion ◽

Growing Season ◽

Maximum Temperature ◽

Linear Regression Models ◽

The Impact

Climate change and its impact on agriculture are challenging issues regarding food production and food security. Many researchers have been trying to show the direct and indirect impacts of climate change on agriculture using different methods. In this study, we used linear regression models to assess the impact of climate on crop yield spatially and temporally by managing irrigated and non-irrigated crop fields. The climate data used in this study are Tmax (maximum temperature), Tmean (mean temperature), Tmin (minimum temperature), precipitation, and soybean annual yields, at county scale for Mississippi, USA, from 1980 to 2019. We fit a series of linear models that were evaluated based on statistical measurements of adjusted R-square, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). According to the statistical model evaluation, the 1980–1992 model Y[Tmax,Tmin,Precipitation]92i (BIC = 120.2) for irrigated zones and the 1993–2002 model Y[Tmax,Tmean,Precipitation]02ni (BIC = 1128.9) for non-irrigated zones showed the best fit for the 10-year period of climatic impacts on crop yields. These models showed about 2 to 7% significant negative impact of Tmax increase on the crop yield for irrigated and non-irrigated regions. Besides, the models for different agricultural districts also explained the changes of Tmax, Tmean, Tmin, and precipitation in the irrigated (adjusted R-square: 13–28%) and non-irrigated zones (adjusted R-square: 8–73%). About 2–10% negative impact of Tmax was estimated across different agricultural districts, whereas about −2 to +17% impacts of precipitation were observed for different districts. The modeling of 40-year periods of the whole state of Mississippi estimated a negative impact of Tmax (about 2.7 to 8.34%) but a positive impact of Tmean (+8.9%) on crop yield during the crop growing season, for both irrigated and non-irrigated regions. Overall, we assessed that crop yields were negatively affected (about 2–8%) by the increase of Tmax during the growing season, for both irrigated and non-irrigated zones. Both positive and negative impacts on crop yields were observed for the increases of Tmean, Tmin, and precipitation, respectively, for irrigated and non-irrigated zones. This study showed the pattern and extent of Tmax, Tmean, Tmin, and precipitation and their impacts on soybean yield at local and regional scales. The methods and the models proposed in this study could be helpful to quantify the climate change impacts on crop yields by considering irrigation conditions for different regions and periods.

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Developing resilience to hydrologic extremes in irrigated agriculture through risk transfer mechanisms in the context of southeastern Brazil

10.5194/egusphere-egu21-6781 ◽

2021 ◽

Author(s):

Marcos Roberto Benso ◽

Gabriela Chiquito Gesualdo ◽

Eduardo Mario Mendiondo ◽

Lars Ribbe ◽

Alexandra Nauditt

Keyword(s):

Climate Change ◽

Land Use ◽

Crop Yield ◽

Cash Flow ◽

Synthetic Data ◽

Southeastern Brazil ◽

Irrigated Agriculture ◽

Risk Transfer ◽

Hydrologic Extremes ◽

Transfer Mechanisms

<p>In the last decades, we have witnessed increasing losses on crop yield due to an increase in magnitude and frequency of hydrological extremes such as droughts and floods. These hazards promote systematic and regressive impacts on the economy and human behavior. Risk transfer mechanisms are key to cope with the economic impacts of these events, therefore safeguarding income to farmers and building resilience to the overall sector. The index-based insurance establishes an index that can be monitored in real or near-real-time, which is associated with losses to a specific agent. While the manifestation of the causality hazard to exposure and exposure to damage and its mathematical representation in cash flow equations is a hard task, incorporating an objective and transparent index adds up a new challenge to this modeling framework. Moreover, past events that have been used as the main guide to evaluating expected losses given risk can no longer offer an accurate risk estimation due to environmental changes. This work aims to tackle the hydrologic extremes risk transfer modeling in irrigated agriculture to obtain optimized premium values and parameters of an insurance fund for irrigated agriculture in Southeastern Brazil. This study will be developed in the Piracicaba, Jundia&#237;, and Capivari river basin, also known as PCJ catchment in the states of S&#227;o Paulo and Minas Gerais, Brazil. The region, with approximately 5 million inhabitants, is considered one of the most important in Brazil due to its economic development, which represents about 7% of the National Gross Domestic Product (GDP). The Hydrologic Risk Transfer Model of the Hydraulic and Sanitation department of the University of S&#227;o Paulo (MTRH-SHS) will be used to obtain optimized premium values. The main index variable is streamflow fitted to extreme value theory distribution for low and high flows. To evaluate climate change and land-use change scenarios, Regional Climate Models (RCMs) and land use projections will be related to streamflow in a hierarchical Bayesian framework. Synthetic data will be then simulated according to scenarios previously defined in a Monte Carlo approach. The hazard-damage function will be obtained by total crop yield and revenue per municipality, then the relationship between the index and expected losses is determined in an empirical equation. Finally, a cash flow computation is run with synthetic data obtaining optimized premiums in a way to minimize fund storage values. We expect to provide further evidence of the feasibility of actuarially fair premium values for the agents in the sector considering global phenomena of climate change and land-use change. Results will support climate change adaptation plans and policy as well as contribute to methods for estimating risk in a changing environment.</p>

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