scholarly journals Spatial Patterns of Crop Yield Change by Emitted Pollutant

2019 ◽  
Vol 7 (2) ◽  
pp. 101-112 ◽  
Author(s):  
Drew Shindell ◽  
Greg Faluvegi ◽  
Prasad Kasibhatla ◽  
Rita Van Dingenen
Keyword(s):  
Crop Yield ◽  
Spatial Patterns ◽  
Yield Change

Analysing and simulating spatial patterns of crop yield in Guizhou Province based on artificial neural networks

2020 ◽  
pp. 030913332095663 ◽  
Author(s):  
Boyi Liang ◽  
Hongyan Liu ◽  
Timothy A Quine ◽  
Xiaoqiu Chen ◽  
Paul D Hallett ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Crop Yield ◽  
Spatial Patterns ◽  
Crop Production ◽  
Guizhou Province ◽  
Crop Species ◽  
Environmental Controls ◽  
Artificial Neural ◽  
Propagation Network

The area of karst terrain in China covers 3.63×106 km2, with more than 40% in the southwestern region over the Guizhou Plateau. Karst comprises exposed carbonate bedrock over approximately 1.30×106 km2 of this area, which suffers from soil degradation and poor crop yield. This paper aims to gain a better understanding of the environmental controls on crop yield in order to enable more sustainable use of natural resources for food production and development. More precisely, four kinds of artificial neural network were used to analyse and simulate the spatial patterns of crop yield for seven crop species grown in Guizhou Province, exploring the relationships with meteorological, soil, irrigation and fertilization factors. The results of spatial classification showed that most regions of high-level crop yield per area and total crop yield are located in the central-north area of Guizhou. Moreover, the three artificial neural networks used to simulate the spatial patterns of crop yield all demonstrated a good correlation coefficient between simulated and true yield. However, the Back Propagation network had the best performance based on both accuracy and runtime. Among the 13 influencing factors investigated, temperature (16.4%), radiation (15.3%), soil moisture (13.5%), fertilization of N (13.5%) and P (12.4%) had the largest contribution to crop yield spatial distribution. These results suggest that neural networks have potential application in identifying environmental controls on crop yield and in modelling spatial patterns of crop yield, which could enable local stakeholders to realize sustainable development and crop production goals.

Predicting spatial patterns of within-field crop yield variability

2018 ◽  
Vol 219 ◽  
pp. 106-112 ◽  
Author(s):  
Bernardo Maestrini ◽  
Bruno Basso
Keyword(s):  
Crop Yield ◽  
Spatial Patterns ◽  
Yield Variability ◽  
Field Crop

scholarly journals MACROECONOMIC IMPACT INDUCED BY CROP YIELD CHANGE ASSOCIATED WITH CLIMATE CHANGE

2017 ◽  
Vol 73 (5) ◽  
pp. I_397-I_405
Author(s):  
Shinichiro FUJIMORI ◽  
Toshichika IIZUMI ◽  
Tomoko HASEGAWA ◽  
Junya TAKAKURA ◽  
Kiyoshi TAKAHASHI ◽  
...  
Keyword(s):  
Climate Change ◽  
Crop Yield ◽  
Yield Change ◽  
Macroeconomic Impact

scholarly journals GLOBAL MARKET AND ECONOMIC WELFARE IMPLICATIONS OF CHANGES IN AGRICULTURAL YIELDS DUE TO CLIMATE CHANGE

2020 ◽  
Vol 11 (01) ◽  
pp. 2050005 ◽  
Author(s):  
KATHERINE CALVIN ◽  
BRYAN K. MIGNONE ◽  
HAROON S. KHESHGI ◽  
ABIGAIL C. SNYDER ◽  
PRALIT PATEL ◽  
...  
Keyword(s):  
Climate Change ◽  
Crop Yield ◽  
Crop Yields ◽  
Climate System ◽  
Integrated Assessment Model ◽  
Economic Welfare ◽  
Cropland Area ◽  
Economic Relationships ◽  
Yield Change ◽  
Welfare Implications

The economic welfare effects of climate change on global agriculture will be mediated by several complex biophysical and economic processes. For a given emissions scenario, these include: (1) the response of the climate system to anthropogenic forcing, (2) the response of crop yields to climate system and carbon dioxide changes, given baseline improvements in crop yields, (3) the response of agricultural markets to crop yield changes, and (4) the economic welfare implications of such market responses. In this paper, we use information about the first two processes from available climate-crop model comparison studies to analyze implications for the third and fourth processes. Applying the range of crop yield changes in a Global Integrated Assessment Model (GCAM) highlights several important economic relationships. First, we find a consistent relationship between global cropland area and yield change that is approximately orthogonal to the relationship between regional cropland area and yield change. Second, we find that the change in economic welfare, expressed as total surplus change per unit economic output, peaks during the 21st century. Third, we find that, at the global level, changes in yield affect both producer surplus and consumer surplus. Specifically, surplus changes to producers and consumers are always opposite in sign, although which economic actors gain or lose varies with the sign of yield change for any given commodity. Taken together, these results contribute to a growing body of research on climate-induced changes on agriculture by highlighting several economic relationships that are robust to differences in the underlying biophysical responses.

scholarly journals Systemic Risk in Global Agricultural Markets and Trade Liberalization under Climate Change: Synchronized Crop-Yield Change and Agricultural Price Volatility

2020 ◽  
Vol 12 (24) ◽  
pp. 10680
Author(s):  
Yoji Kunimitsu ◽  
Gen Sakurai ◽  
Toshichika Iizumi
Keyword(s):  
Climate Change ◽  
Trade Liberalization ◽  
Crop Yield ◽  
Systemic Risk ◽  
Price Volatility ◽  
Agricultural Trade ◽  
Future Climate Change ◽  
Random Disturbance ◽  
Yield Change ◽  
Agricultural Price

Climate change will increase simultaneous crop failures or too abundant harvests, creating global synchronized yield change (SYC), and may decrease stability in the portfolio of food supply sources in agricultural trade. This study evaluated the influence of SYC on the global agricultural market and trade liberalization. The analysis employed a global computable general equilibrium model combined with crop models of four major grains (i.e., rice, wheat, maize, and soybeans), based on predictions of five global climate models. Simulation results show that (1) the SYC structure was statistically robust among countries and four crops, and will be enhanced by climate change, (2) such synchronicity increased the agricultural price volatility and lowered social welfare levels more than expected in the random disturbance (non-SYC) case, and (3) trade liberalization benefited both food-importing and exporting regions, but such effects were degraded by SYC. These outcomes were due to synchronicity in crop-yield change and its ranges enhanced by future climate change. Thus, SYC is a cause of systemic risk to food security and must be considered in designing agricultural trade policies and insurance systems.

Climate change impacts on winter wheat yield change – which climatic parameters are crucial in Pannonian lowland?

2012 ◽  
Vol 151 (6) ◽  
pp. 757-774 ◽  
Author(s):  
B. LALIC ◽  
J. EITZINGER ◽  
D. T. MIHAILOVIC ◽  
S. THALER ◽  
M. JANCIC
Keyword(s):  
Climate Change ◽  
Winter Wheat ◽  
Water Deficit ◽  
Crop Yield ◽  
Wheat Yield ◽  
Maximum Temperature ◽  
Climatic Parameters ◽  
Yield Change ◽  
The Future ◽  
Winter Wheat Yield

SUMMARYOne of the main problems in estimating the effects of climate change on crops is the identification of those factors limiting crop growth in a selected environment. Previous studies have indicated that considering simple trends of either precipitation or temperature for the coming decades is insufficient for estimating the climate impact on yield in the future. One reason for this insufficiency is that changes in weather extremes or seasonal weather patterns may have marked impacts.The present study focuses on identifying agroclimatic parameters that can identify the effects of climate change and variability on winter wheat yield change in the Pannonian lowland. The impacts of soil type under past and future climates as well as the effect of different CO2 concentrations on yield formation are also considered. The Vojvodina region was chosen for this case study because it is a representative part of the Pannonian lowland.Projections of the future climate were taken from the HadCM3, ECHAM5 and NCAR-PCM climate models with the SRES-A2 scenario for greenhouse gas (GHG) emissions for the 2040 and 2080 integration periods. To calibrate and validate the Met&Roll weather generator, four-variable weather data series (for six main climatic stations in the Vojvodina region) were analysed. The grain yield of winter wheat was calculated using the SIRIUS wheat model for three different CO2 concentrations (330, 550 and 1050 ppm) dependent on the integration period. To estimate the effects of climatic parameters on crop yield, the correlation coefficient between crop yield and agroclimatic indices was calculated using the AGRICLIM software. The present study shows that for all soil types, the following indices are the most important for winter wheat yields in this region: (i) the number of days with water and temperature stress, (ii) the accumulated precipitation, (iii) the actual evapotranspiration (ETa) and (iv) the water deficit during the growing season. The high positive correlations between yield and the ETa, accumulated precipitation and the ratio between the ETa and reference evapotranspiration (ETr) for the April–June period indicate that water is and will remain a major limiting factor for growing winter wheat in this region. Indices referring to negative impact on yield are (i) the number of days with a water deficit for the April–June period and (ii) the number of days with maximum temperature above 25 °C (summer days) and the number of days with maximum temperature above 30 °C (tropical days) in May and June. These indices can be seen as indicators of extreme weather events such as drought and heat waves.

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