scholarly journals Critical analysis of temporal climate change and ensuing frequency of crop yield constraining temperatures

2021 ◽  
Vol 22 (3) ◽  
pp. 339-352
Author(s):  
S.K. JALOTA ◽  
PRABHJYOT KAUR ◽  
JATINDER KAUR ◽  
P.K. KINGRA ◽  
B.B. VASHISHT
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Crop Yield ◽  
Critical Analysis ◽  
Temporal Changes ◽  
Crop Growth ◽  
Future Time ◽  
Climate Parameters ◽  
Rcp 8.5 ◽  
Growth And Reproduction

Critical analysis of magnitudes andtrends of temporal changes in maximum (Tmax) and minimum (Tmin) temperatures as well as rainfall during three time series i.e. past (observed data, 1970-1990), present (observed data, 1989-2018) and future (bias corrected modelled data, 2021-2050) reveals that on the whole, inter–series mean of all the three climate parameters increased, and variation decreased in future time series. The magnitude of these trends varied with the model as well as scenario; was highest in RCP 8.5 scenario. Intra-series trends at annual and seasonal scales are dictated by inconsistent monthly trends. This study also adds that decline incrop yields of rice-wheat system with warming in future can be ascribed to increased frequency of days having yield constraining temperatures (above ceiling and below critically low during crop growth and reproduction stages) rather than their elevated magnitudes only as anticipated previously.

scholarly journals Climate change and agroecosystems: the effect of elevated atmospheric CO2 and temperature on crop growth, development, and yield

2005 ◽  
Vol 35 (3) ◽  
pp. 730-740 ◽  
Author(s):  
Nereu Augusto Streck
Keyword(s):  
Climate Change ◽  
Crop Yield ◽  
Co2 Concentration ◽  
Crop Growth ◽  
Atmospheric Co2 ◽  
C3 Plants ◽  
Elevated Atmospheric Co2 ◽  
Growth Development ◽  
Carbon Dioxide Co2 ◽  
The Impact

The amount of carbon dioxide (CO2) of the Earth´s atmosphere is increasing, which has the potential of increasing greenhouse effect and air temperature in the future. Plants respond to environment CO2 and temperature. Therefore, climate change may affect agriculture. The purpose of this paper was to review the literature about the impact of a possible increase in atmospheric CO2 concentration and temperature on crop growth, development, and yield. Increasing CO2 concentration increases crop yield once the substrate for photosynthesis and the gradient of CO2 concentration between atmosphere and leaf increase. C3 plants will benefit more than C4 plants at elevated CO2. However, if global warming will take place, an increase in temperature may offset the benefits of increasing CO2 on crop yield.

scholarly journals Impacts of climate change and climate extremes on major crops productivity in China at a global warming of 1.5 °C & 2.0 °C

2018 ◽  
Author(s):  
Yi Chen ◽  
Zhao Zhang ◽  
Fulu Tao
Keyword(s):  
Climate Change ◽  
Crop Yield ◽  
Extreme Events ◽  
Crop Production ◽  
Maize Yield ◽  
Crop Growth ◽  
Growth Duration ◽  
Growth Environment ◽  
Warming Scenario ◽  
Yield Decrease

Abstract. A new temperature goal of holding the increase in global average temperature well below 2℃ above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5 ℃ above pre-industrial levels has been established in Paris Agreement, which calls for understanding of climate risk under 1.5 ℃ & 2.0 ℃ warming scenarios. Here, we evaluated the effects of climate change on growth and productivity of three major crops (i.e., maize, wheat, rice) in China during 2106–2115 at warming scenarios of 1.5 ℃ & 2.0 ℃ using the method of ensemble simulation with well-validated MCWLA family crop models, their 10 sets of optimal crop model parameters, and 70 climate projections from four global climate models. We presented the spatial patterns of changes in crop growth duration, crop yield, impacts of heat and drought stress, as well as crop yield variability and probability of crop yield decrease. Results showed that the decrease of crop growth duration and the increase of extreme events impacts in the future would have major negative impacts on crop production, particularly for wheat in north China, rice in south China and maize across the cultivation areas. By contrast, with the moderate increases in temperature, solar radiation, precipitation, and atmospheric CO2 concentration, agricultural climate resources such as light and thermal resource could be ameliorated which enhance canopy photosynthesis, and consequently biomass accumulations and yields. The moderate climate change would slightly deteriorate maize growth environment but result in much more appropriate growth environment for wheat and rice. As a result, the wheat and rice yields could increase by 3.9 % and 4.1 %, respectively, and maize yield could increase by 0.2 %, at a warming scenario of 1.5 ℃. At the warming scenario of 2.0 ℃, wheat and rice yield would increase by 8.6 % and 9.4 %, respectively, but maize yield could decrease by 1.7 %. In general, the warming scenarios would bring more opportunities than risks for the crop development and food security in China. Moreover, although variability of crop yield would increase with the change of climate scenario from 1.5 ℃ warming to 2.0 ℃ warming, the probability of crop yield decrease would decrease. Our findings highlight that the 2.0 ℃ warming scenario would be more suitable for crop production in China, but the expected increase in extreme events impacts should be paid more attention to.

scholarly journals Integration of a Crop Growth Model and Deep Learning Methods to Improve Satellite-Based Yield Estimation of Winter Wheat in Henan Province, China

2021 ◽  
Vol 13 (21) ◽  
pp. 4372
Author(s):  
Yi Xie ◽  
Jianxi Huang
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Crop Yield ◽  
Growth Model ◽  
Wheat Yield ◽  
Crop Growth ◽  
Henan Province ◽  
Crop Growth Model ◽  
Area Index ◽  
Growing Region

Timely and accurate regional crop-yield estimates are crucial for guiding agronomic practices and policies to improve food security. In this study, a crop-growth model was integrated with time series of remotely sensed data through deep learning (DL) methods to improve the accuracy of regional wheat-yield estimations in Henan Province, China. Firstly, the time series of moderate-resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) were input into the long short-term memory network (LSTM) model to identify the wheat-growing region, which was further used to estimate wheat areas at the municipal and county levels. Then, the leaf area index (LAI) and grain-yield time series simulated by the Crop Environment REsource Synthesis for Wheat (CERES-Wheat) model were used to train and evaluate the LSTM, one-dimensional convolutional neural network (1-D CNN) and random forest (RF) models, respectively. Finally, an exponential model of the relationship between the field-measured LAI and MODIS NDVI was applied to obtain the regional LAI, which was input into the trained LSTM, 1-D CNN and RF models to estimate wheat yields within the wheat-growing region. The results showed that the linear correlations between the estimated wheat areas and the statistical areas were significant at both the municipal and county levels. The LSTM model provided more accurate estimates of wheat yields, with higher R2 values and lower root mean square error (RMSE) and mean relative error (MRE) values than the 1-D CNN and RF models. The LSTM model has an inherent advantage in capturing phenological information contained in the time series of the MODIS-derived LAI, which is important for satellite-based crop-yield estimates.

scholarly journals Impacts of climate change and climate extremes on major crops productivity in China at a global warming of 1.5 and 2.0 °C

2018 ◽  
Vol 9 (2) ◽  
pp. 543-562 ◽  
Author(s):  
Yi Chen ◽  
Zhao Zhang ◽  
Fulu Tao
Keyword(s):  
Climate Change ◽  
Crop Yield ◽  
Crop Production ◽  
Crop Growth ◽  
Model Parameters ◽  
Climate Projections ◽  
Growth Duration ◽  
Growth Environment ◽  
Warming Scenario ◽  
Yield Decrease

Abstract. A new temperature goal of “holding the increase in global average temperature well below 2 ∘C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5 ∘C above pre-industrial levels” has been established in the Paris Agreement, which calls for an understanding of climate risk under 1.5 and 2.0 ∘C warming scenarios. Here, we evaluated the effects of climate change on growth and productivity of three major crops (i.e. maize, wheat, rice) in China during 2106–2115 in warming scenarios of 1.5 and 2.0 ∘C using a method of ensemble simulation with well-validated Model to capture the Crop–Weather relationship over a Large Area (MCWLA) family crop models, their 10 sets of optimal crop model parameters and 70 climate projections from four global climate models. We presented the spatial patterns of changes in crop growth duration, crop yield, impacts of heat and drought stress, as well as crop yield variability and the probability of crop yield decrease. Results showed that climate change would have major negative impacts on crop production, particularly for wheat in north China, rice in south China and maize across the major cultivation areas, due to a decrease in crop growth duration and an increase in extreme events. By contrast, with moderate increases in temperature, solar radiation, precipitation and atmospheric CO2 concentration, agricultural climate resources such as light and thermal resources could be ameliorated, which would enhance canopy photosynthesis and consequently biomass accumulations and yields. The moderate climate change would slightly worsen the maize growth environment but would result in a much more appropriate growth environment for wheat and rice. As a result, wheat, rice and maize yields would change by +3.9 (+8.6), +4.1 (+9.4) and +0.2 % (−1.7 %), respectively, in a warming scenario of 1.5 ∘C (2.0 ∘C). In general, the warming scenarios would bring more opportunities than risks for crop development and food security in China. Moreover, although the variability of crop yield would increase from 1.5 ∘C warming to 2.0 ∘C warming, the probability of a crop yield decrease would decrease. Our findings highlight that the 2.0 ∘C warming scenario would be more suitable for crop production in China, but more attention should be paid to the expected increase in extreme event impacts.

scholarly journals Effect of Climate Variables on Yield of Major Crop in Samastipur District of Bihar: A time Series Analysis

2020 ◽  
Vol 65 (4) ◽  
Author(s):  
Meera Kumari
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Crop Yield ◽  
Minimum Temperature ◽  
Time Series Data ◽  
Annual Rainfall ◽  
Series Data ◽  
Climate Variables ◽  
Climatic Variable ◽  
Climate Variable

Climate change influences crop yield vis-a-vis crop production to a greater extent in Bihar. Climate change and its impacts are well recognizing today and it will affect both physical and biological system. Therefore, this study has been planned to assess the effect of climate variables on yield of major crops, adaptation measures undertaken in Samastipur district of Bihar. Secondary data on yield of maize and wheat crops were collected for the period from 1999-2019 to describe the effects of climate variable namely rainfall, maximum and minimum temperature on yield of maize and wheat. Analysis of time series data on climate variables indicated that annual rainfall was positively related to yields while maximum and minimum temperature had a negative but significant impact on maize and wheat yields. It actually revealed that other factors, such as; type of soil, soil fertility and method of farming may also be responsible for crop yield. Trend in cost as well as income of farmers indicated that income and cost of cultivation has no significant relationship with climate variable. On the basis of above observation it may be concluded that level of income of farmers changed due to change in the other factors rather than change in climatic variable over the period under study as cost of cultivation increases with increased in the price of input over the period but not due to change in climatic variable.

scholarly journals Peyzaj Planlama Aşamasında Önemli Etkenlerden Olan Sıcaklık, Yağış Ve İklim Tiplerinde, Küresel İklim Değişikliğine Bağlı Olarak Meydana Gelebilecek Değişiklikler: Mersin Kent Örneği

2020 ◽  
Vol 8 (12) ◽  
pp. 2695-2701
Author(s):  
Mehmet Cetin
Keyword(s):  
Climate Change ◽  
Global Climate Change ◽  
Landscape Planning ◽  
Global Climate ◽  
Current Status ◽  
Precipitation Regime ◽  
Climate Parameters ◽  
Living Things ◽  
Rcp 8.5 ◽  
Change Climate

Global climate change is seen as a process that will directly or indirectly affect living things and ecosystems all over the world. In this process, determining the changes in climate parameters and climate types in advance is of great importance in terms of the measures that can be taken and the preparation for the process. In this study, it is aimed to determine the changes that may be caused by global climate change in some climate parameters and climate types in Mersin, which is one of the important cities of our country. Within the scope of the study, the current status of temperature, precipitation and climate types (according to De Martone and Emberger climate classification) and RCP 4.5 and RCP 8.5. It is aimed to compare the possible situations in 2050 and 2070 in the light of scenarios. The results of the study show that temperature, precipitation and climate types will change significantly throughout Mersin province. Today, the temperature varying between -0.4°C and 19°C will change between 4.9°C and 24°C throughout the province in 2070 according to the RCP 8.5 scenario, that is, there will be an increase of around 5°C in the temperature change interval, the precipitation regime will change, Climate types are predicted to shift towards arid climates. This situation shows that climate factors, one of the most important planning criteria, will change significantly in landscape planning studies. Since landscape planning studies continue their effects for many years, it is recommended to take this into consideration in order to make a healthy planning. The results of this study should be used in the planning studies for Mersin province.

scholarly journals Analogy-Based Crop Yield Forecasts Based on Temporal Similarity of Leaf Area Index

2021 ◽  
Vol 13 (16) ◽  
pp. 3069
Author(s):  
Yadong Liu ◽  
Junhwan Kim ◽  
David H. Fleisher ◽  
Kwang Soo Kim
Keyword(s):  
Time Series ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Crop Yield ◽  
Satellite Data ◽  
Growing Season ◽  
Environmental Data ◽  
Area Index ◽  
Current Season ◽  
Wide Range

Seasonal forecasts of crop yield are important components for agricultural policy decisions and farmer planning. A wide range of input data are often needed to forecast crop yield in a region where sophisticated approaches such as machine learning and process-based models are used. This requires considerable effort for data preparation in addition to identifying data sources. Here, we propose a simpler approach called the Analogy Based Crop-yield (ABC) forecast scheme to make timely and accurate prediction of regional crop yield using a minimum set of inputs. In the ABC method, a growing season from a prior long-term period, e.g., 10 years, is first identified as analogous to the current season by the use of a similarity index based on the time series leaf area index (LAI) patterns. Crop yield in the given growing season is then forecasted using the weighted yield average reported in the analogous seasons for the area of interest. The ABC approach was used to predict corn and soybean yields in the Midwestern U.S. at the county level for the period of 2017–2019. The MOD15A2H, which is a satellite data product for LAI, was used to compile inputs. The mean absolute percentage error (MAPE) of crop yield forecasts was <10% for corn and soybean in each growing season when the time series of LAI from the day of year 89 to 209 was used as inputs to the ABC approach. The prediction error for the ABC approach was comparable to results from a deep neural network model that relied on soil and weather data as well as satellite data in a previous study. These results indicate that the ABC approach allowed for crop yield forecast with a lead-time of at least two months before harvest. In particular, the ABC scheme would be useful for regions where crop yield forecasts are limited by availability of reliable environmental data.

scholarly journals Effect of Temperature on Sowing Dates of Wheat under Arid and Semi-Arid Climatic Regions and Impact Quantification of Climate Change through Mechanistic Modeling with Evidence from Field

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 927
Author(s):  
Jamshad Hussain ◽  
Tasneem Khaliq ◽  
Muhammad Habib ur Rahman ◽  
Asmat Ullah ◽  
Ishfaq Ahmed ◽  
...  
Keyword(s):  
Climate Change ◽  
Grain Yield ◽  
Arid Region ◽  
Field Experiments ◽  
Climatic Conditions ◽  
Seasonal Temperature ◽  
Sowing Dates ◽  
Rcp 8.5 ◽  
Semi Arid Region ◽  
Semi Arid

Rising temperature from climate change is the most threatening factor worldwide for crop production. Sustainable wheat production is a challenge due to climate change and variability, which is ultimately a serious threat to food security in Pakistan. A series of field experiments were conducted during seasons 2013–2014 and 2014–2015 in the semi-arid (Faisalabad) and arid (Layyah) regions of Punjab-Pakistan. Three spring wheat genotypes were evaluated under eleven sowing dates from 16 October to 16 March, with an interval of 14–16 days in the two regions. Data for the model calibration and evaluation were collected from field experiments following the standard procedures and protocols. The grain yield under future climate scenarios was simulated by using a well-calibrated CERES-wheat model included in DSSAT v4.7. Future (2051–2100) and baseline (1980–2015) climatic data were simulated using 29 global circulation models (GCMs) under representative concentration pathway (RCP) 8.5. These GCMs were distributed among five quadrants of climatic conditions (Hot/Wet, Hot/Dry, Cool/Dry, Cool/Wet, and Middle) by a stretched distribution approach based on temperature and rainfall change. A maximum of ten GCMs predicted the chances of Middle climatic conditions during the second half of the century (2051–2100). The average temperature during the wheat season in a semi-arid region and arid region would increase by 3.52 °C and 3.84 °C, respectively, under Middle climatic conditions using the RCP 8.5 scenario during the second half-century. The simulated grain yield was reduced by 23.5% in the semi-arid region and 35.45% in the arid region under Middle climatic conditions (scenario). Mean seasonal temperature (MST) of sowing dates ranged from 16 to 27.3 °C, while the mean temperature from the heading to maturity (MTHM) stage was varying between 12.9 to 30.4 °C. Coefficients of determination (R2) between wheat morphology parameters and temperature were highly significant, with a range of 0.84–0.96. Impacts of temperature on wheat sown on 15 March were found to be as severe as to exterminate the crop before heading. The spikes and spikelets were not formed under a mean seasonal temperature higher than 25.5 °C. In a nutshell, elevated temperature (3–4 °C) till the end-century can reduce grain yield by about 30% in semi-arid and arid regions of Pakistan. These findings are crucial for growers and especially for policymakers to decide on sustainable wheat production for food security in the region.

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