scholarly journals Combined Impact of Climate Change and Land Qualities on Winter Wheat Yield in Central Fore-Caucasus: The Long-Term Retrospective Study

Land ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1339
Author(s):  
Vasilii Erokhin ◽  
Alexander Esaulko ◽  
Elena Pismennaya ◽  
Evgeny Golosnoy ◽  
Olga Vlasova ◽  
...  
Keyword(s):  
Climate Change ◽  
Winter Wheat ◽  
Agricultural Sector ◽  
Crop Yields ◽  
Climatic Factors ◽  
Wheat Yield ◽  
Climate Impacts ◽  
Contributing Factors ◽  
Support Tool

Progressing climate change has been increasingly threatening the agricultural sector by compromising the resilience of ecosystems and endangering food security worldwide. Altering patterns of major climatic parameters require the perspectives of agricultural production to be assessed in a holistic way to understand the interactions of climatic and non-climatic factors on crop yield. However, it is difficult to distinguish the direct influence of changing temperature and precipitation on the productivity of crops while simultaneously capturing other contributing factors, such as spatial allocation of agricultural lands, economic conditions of land use, and soil fertility. Wide temporal and spatial fluctuations of climatic impacts substantially complicate the task. In the case of the 170-year retrospective analysis of the winter wheat sector in the south of Russia, this study tackles the challenge by establishing the multiplicative function to estimate crop yields as a long-term result of a combined influence of agricultural output parameters, qualities of soils, and climate variables. It is found that within the climate–land–yield triangle, linkages tighten or weaken depending on the strength of noise effects of economic and social perturbations. Still, the overall pressure of climate change on the cultivation of winter wheat has been aggravating. The inter-territory relocation of areas under crops based on the matching of soil types, precipitation, air temperature, and erodibility of lands is suggested as a climate response option. The approach can be employed as a decision support tool when developing territory-specific land management policies to cope with adverse climate impacts on the winter wheat sector.

scholarly journals Trends of weather-related impacts on Austrian crop production under changing climate

2021 ◽  
Author(s):  
Sabina Thaler ◽  
Josef Eitzinger ◽  
Gerhard Kubu
Keyword(s):  
Climate Change ◽  
Winter Wheat ◽  
Crop Production ◽  
Crop Yields ◽  
Weather Conditions ◽  
Crop Growth ◽  
Risk Indicators ◽  
Climate Scenarios ◽  
Crop Water

<p>Weather-related risks can affect crop growth and yield potentials directly (e.g. heat, frost, drought) and indirectly (e.g. through biotic factors such as pests). Due to climate change, severe shifts of cropping risks may occur, where farmers need to adapt effectively and in time to increase the resilience of existing cropping systems. For example, since the early 21st century, Europe has experienced a series of exceptionally dry and warmer than usual weather conditions (2003, 2012, 2013, 2015, 2018) which led to severe droughts with devastating impacts in agriculture on crop yields and pasture productivity.</p><p>Austria has experienced above-average warming in the period since 1880. While the global average surface temperature has increased by almost 1°C, the warming in Austria during this period was nearly 2°C. Higher temperatures, changing precipitation patterns and more severe and frequent extreme weather events will significantly affect weather-sensitive sectors, especially agriculture. Therefore, the development of sound adaptation and mitigation strategies towards a "climate-intelligent agriculture" is crucial to improve the resilience of agricultural systems to climate change and increased climate variability. Within the project AGROFORECAST a set of weather-related risk indicators and tailored recommendations for optimizing crop management options are developed and tested for various forecast or prediction lead times (short term management: 10 days - 6 months; long term strategic planning: climate scenarios) to better inform farmers of upcoming weather and climate challenges.</p><p>Here we present trends of various types of long-term weather-related impacts on Austrian crop production under past (1980-2020) and future periods (2035-2065). For that purpose, agro-climatic risk indicators and crop production indicators are determined in selected case study regions with the help of models. We use for the past period Austrian gridded weather data set (INCA) as well as different regionalized climate scenarios of the Austrian Climate Change Projections ÖKS15. The calculation of the agro-climatic indicators is carried out by the existing AGRICLIM model and the GIS-based ARIS software, which was developed for estimating the impact of adverse weather conditions on crops. The crop growth model AQUACROP is used for analysing soil-crop water balance parameters, crop yields and future crop water demand.</p><p>Depending on the climatic region, a more or less clear shift in the various agro-climatic indices can be expected towards 2050, e.g. the number of "heat-stress-days" for winter wheat increases significantly in eastern Austria. Furthermore, a decreasing trend in maize yield is simulated, whereas a mean increase in yield of spring barley and winter wheat can be expected under selected scenarios. Other agro-climatic risk indicators analysed include pest algorithms, risks from frost occurrence, overwintering conditions, climatic crop growing conditions, field workability and others, which can add additional impacts on crop yield variability, not considered by crop models.</p>

scholarly journals Climate Change Impacts on Winter Wheat Yield in Northern China

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Xiu Geng ◽  
Fang Wang ◽  
Wei Ren ◽  
Zhixin Hao
Keyword(s):  
Climate Change ◽  
Winter Wheat ◽  
Negative Impact ◽  
Climatic Factors ◽  
Wheat Yield ◽  
Growing Season ◽  
Northern China ◽  
Series Data ◽  
Winter Wheat Yield ◽  
Impacts Of Climate Change

Exploring the impacts of climate change on agriculture is one of important topics with respect to climate change. We quantitatively examined the impacts of climate change on winter wheat yield in Northern China using the Cobb–Douglas production function. Utilizing time-series data of agricultural production and meteorological observations from 1981 to 2016, the impacts of climatic factors on wheat production were assessed. It was found that the contribution of climatic factors to winter wheat yield per unit area (WYPA) was 0.762–1.921% in absolute terms. Growing season average temperature (GSAT) had a negative impact on WYPA for the period of 1981–2016. A 1% increase in GSAT could lead to a loss of 0.109% of WYPA when the other factors were constant. While growing season precipitation (GSP) had a positive impact on WYPA, as a 1% increase in GSP could result in 0.186% increase in WYPA, other factors kept constant. Then, the impacts on WYPA for the period 2021–2050 under two different emissions scenarios RCP4.5 and RCP8.5 were forecasted. For the whole study area, GSAT is projected to increase 1.37°C under RCP4.5 and 1.54°C under RCP8.5 for the period 2021–2050, which will lower the average WYPA by 1.75% and 1.97%, respectively. GSP is tended to increase by 17.31% under RCP4.5 and 22.22% under RCP8.5 and will give a rise of 3.22% and 4.13% in WYPA. The comprehensive effect of GSAT and GSP will increase WYPA by 1.47% under RCP4.5 and 2.16% under RCP8.5.

scholarly journals Challenges in simulating economic effects of climate change on global agricultural markets

2021 ◽  
Vol 166 (3-4) ◽  
Author(s):  
Angelo C. Gurgel ◽  
John Reilly ◽  
Elodie Blanc
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Agricultural Sector ◽  
Crop Yields ◽  
Agricultural Research ◽  
Climate Change Impacts ◽  
Well Being ◽  
Climate Impacts ◽  
Agricultural Markets ◽  
Crop Models

AbstractMany approaches have been used to investigate climate change impacts on agriculture. However, several caveats remain in this field: (i) analyses focus only on a few major crops, (ii) large differences in yield impacts are observed between projections from site-based crops models and Global Gridded Crop Models (GGCMs), (iii) climate change impacts on livestock are rarely quantified, and (iv) several causal relations among biophysical, environmental, and socioeconomic aspects are usually not taken into account. We investigate how assumptions about these four aspects affect agricultural markets, food supply, consumer well-being, and land use at global level by deploying a large-scale socioeconomic model of the global economy with detailed representation of the agricultural sector. We find global welfare impacts several times larger when climate impacts all crops and all livestock compared to a scenario with impacts limited to major crops. At the regional level, food budget can decrease by 10 to 25% in developing countries, challenging food security. The role of land area expansion as a major source of adaptation is highlighted. Climate impacts on crop yields from site-based process crop models generate more challenging socioeconomic outcomes than those from GGCMs. We conclude that the agricultural research community should expand efforts to estimate climate impacts on many more crops and livestock. Also, careful comparison of the GGCMs and traditional site-based process crop models is needed to understand their major implications for agricultural and food markets.

scholarly journals Winter Wheat Yield Prediction at County Level and Uncertainty Analysis in Main Wheat-Producing Regions of China with Deep Learning Approaches

2020 ◽  
Vol 12 (11) ◽  
pp. 1744 ◽  
Author(s):  
Xinlei Wang ◽  
Jianxi Huang ◽  
Quanlong Feng ◽  
Dongqin Yin
Keyword(s):  
Deep Learning ◽  
Winter Wheat ◽  
Uncertainty Analysis ◽  
Agricultural Sector ◽  
Crop Yields ◽  
Regional Scale ◽  
Wheat Yield ◽  
Yield Prediction ◽  
County Level ◽  
Winter Wheat Yield

Timely and accurate forecasting of crop yields is crucial to food security and sustainable development in the agricultural sector. However, winter wheat yield estimation and forecasting on a regional scale still remains challenging. In this study, we established a two-branch deep learning model to predict winter wheat yield in the main producing regions of China at the county level. The first branch of the model was constructed based on the Long Short-Term Memory (LSTM) networks with inputs from meteorological and remote sensing data. Another branch was constructed using Convolution Neural Networks (CNN) to model static soil features. The model was then trained using the detrended statistical yield data during 1982 to 2015 and evaluated by leave-one-year-out-validation. The evaluation results showed a promising performance of the model with the overall R 2 and RMSE of 0.77 and 721 kg/ha, respectively. We further conducted yield prediction and uncertainty analysis based on the two-branch model and obtained the forecast accuracy in one month prior to harvest of 0.75 and 732 kg/ha. Results also showed that while yield detrending could potentially introduce higher uncertainty, it had the advantage of improving the model performance in yield prediction.

scholarly journals Development of the methodology of estimating of agricultural crop yield potential with consideration of climate and agrophytotechnology impact

2017 ◽  
pp. 52-60
Author(s):  
V. P. Dmytrenko ◽  
L. P. Odnolyetok ◽  
О. О. Kryvoshein ◽  
A. V. Krukivska
Keyword(s):  
Winter Wheat ◽  
Crop Yield ◽  
Crop Yields ◽  
Wheat Yield ◽  
Yield Potential ◽  
Agricultural Crop ◽  
Yield Level ◽  
Winter Wheat Yield ◽  
The Mathematical Model

In the paper it is outlined the main methodological positions and the results of the approbation of new approaches to the integrated assessment of the potential of crop yields. There are considered the theoretical foundations of a joint assessment of the biological, ecological and anthropogenic components of the yield potential of agricultural crops which are based on the ecosystem concept and the mathematical model "Weather-Crop Yield" developed by V. P. Dmytrenko. In the considered approaches the peculiarities of the influence of various environmental factors on the formation of crop yields are determined by indicators of various potential yields -  general, climatic and trend (agrotechnological). Each type of yield potential can be used for evaluation of the effectiveness of the conditions of field crop growing for each factor taken into account, as well as the optimality criterion in the agrometeorological adaptation strategies and also as a criterion for the degree of sensitivity of the yield level to the conditions of crops cultivating. The developed approaches are tested on the example of estimation of long-term dynamics of winter wheat yield potential in Ukraine. According to the results of the evaluation of different factors of the potential of the productivity of winter wheat for the periods 1961-1990 and 1991-2010 the dominant importance of organizational and technological processes in comparison with the contribution of changes of agroclimatic conditions has been determined in both periods.

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

Atmosphere ◽  
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.

scholarly journals Climate Change Trends and Impacts on California Agriculture: A Detailed Review

Agronomy ◽  
2018 ◽  
Vol 8 (3) ◽  
pp. 25 ◽  
Author(s):  
Tapan Pathak ◽  
Mahesh Maskey ◽  
Jeffery Dahlberg ◽  
Faith Kearns ◽  
Khaled Bali ◽  
...  
Keyword(s):  
Climate Change ◽  
Agricultural Sector ◽  
Climate Adaptation ◽  
Crop Yields ◽  
Heat Waves ◽  
Sufficient Evidence ◽  
Future Climate Change ◽  
Future Research ◽  
Detailed Review ◽  
California Agriculture

California is a global leader in the agricultural sector and produces more than 400 types of commodities. The state produces over a third of the country’s vegetables and two-thirds of its fruits and nuts. Despite being highly productive, current and future climate change poses many challenges to the agricultural sector. This paper provides a summary of the current state of knowledge on historical and future trends in climate and their impacts on California agriculture. We present a synthesis of climate change impacts on California agriculture in the context of: (1) historic trends and projected changes in temperature, precipitation, snowpack, heat waves, drought, and flood events; and (2) consequent impacts on crop yields, chill hours, pests and diseases, and agricultural vulnerability to climate risks. Finally, we highlight important findings and directions for future research and implementation. The detailed review presented in this paper provides sufficient evidence that the climate in California has changed significantly and is expected to continue changing in the future, and justifies the urgency and importance of enhancing the adaptive capacity of agriculture and reducing vulnerability to climate change. Since agriculture in California is very diverse and each crop responds to climate differently, climate adaptation research should be locally focused along with effective stakeholder engagement and systematic outreach efforts for effective adoption and implementation. The expected readership of this paper includes local stakeholders, researchers, state and national agencies, and international communities interested in learning about climate change and California’s agriculture.

scholarly journals Crop Yield and Soil Fertility Status of Long-Term Rice-Rice-Wheat Cropping Systems

2017 ◽  
Vol 5 (1) ◽  
pp. 42-50
Author(s):  
Nabin Rawal ◽  
Rajan Ghimire ◽  
Devraj Chalise
Keyword(s):  
Soil Fertility ◽  
Crop Yield ◽  
Management Practices ◽  
Crop Yields ◽  
Oryza Sativa L ◽  
Farmyard Manure ◽  
Wheat Yield ◽  
Nutrient Supply ◽  
Available Phosphorus

Balanced nutrient supply is important for the sustainable crop production. We evaluated the effects of nutrient management practices on soil properties and crop yields in rice (Oryza sativa L.) - rice - wheat (Triticum aestivum L.) system in a long-term experiment established at National Wheat Research Program (NWRP), Bhairahawa, Nepal. The experiment was designed as a randomized complete block experiment with nine treatments and three replications. Treatments were applied as: T1- no nutrients added, T2- N added; T3- N and P added; T4- N and K added; T5- NPK added at recommended rate for all crops. Similarly, T6- only N added in rice and NPK in wheat at recommended rate; T7- half N; T8- half NP of recommended rate for both crops; and T9- farmyard manure (FYM) @10 Mg ha-1 for all crops in rotation. Results of the study revealed that rice and wheat yields were significantly greater under FYM than all other treatments. Treatments that did not receive P (T2, T3, T7, T8) and K (T2, T4) had considerably low wheat yield than treatments that received NPK (T5) and FYM (T9). The FYM lowered soil pH and improved soil organic matter (SOM), total nitrogen (TN), available phosphorus (P), and exchangeable potassium (K) contents than other treatments. Management practices that ensure nutrient supply can increase crop yield and improve soil fertility status.Int. J. Appl. Sci. Biotechnol. Vol 5(1): 42-50

scholarly journals Fuzzy logic device for crop analysing, modeling and forecasting in the Kabardino-Balkarian Republic

2020 ◽  
Vol 15 (2) ◽  
pp. 123-133
Author(s):  
Ruslan M. Bischokov
Keyword(s):  
Meteorological Parameters ◽  
Crop Yields ◽  
Climatic Factors ◽  
Crop Productivity ◽  
Logic Device ◽  
Modeling And Forecasting ◽  
Expected Values ◽  
Predicted Values ◽  
Climatic Characteristics

Using computer fuzzy-logical models based on empirical values of climatic characteristics (rainfall, temperature and humidity) of long-term observations (1955-2018) from meteorological stations in the Kabardino-Balkarian Republic (Nalchik, Baksan, Prokhladny and Terek) and crop yields (winter wheat, spring wheat, corn, sunflower, millet, oats), dependence of crop yields on variations of climatic factors were analyzed and a specific forecast was given. Setting expected values of climatic characteristics in computer model, we received possible values of productivity for the next season. Uniformity assessment (Dixon and Smirnov - Grabbsas criterion), stability (Student and Fischers criterion), statistical importance of parameters of distribution and accidental errors were determined. Originality of the method is in the fact that in the form of input parameters of the model predictors, the previously calculated forecast values of the meteorological parameters for the next agricultural year were used, and at the output, the predicted values of crop productivity were obtained as predictants. Furthermore, recommendations on adoption of management decisions were developed.

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