Editorial

4 pages, 202 KiB

Open AccessEditorial

Modeling the Adaptation of Agricultural Production to Climate Change

by Dengpan Xiao and Wenjiao Shi

Agriculture 2023, 13(2), 414; https://doi.org/10.3390/agriculture13020414 - 10 Feb 2023

Cited by 2 | Viewed by 2030

Climate change and its impacts on agricultural production and food security are a significant source of public concern around the world [...] Full article

(This article belongs to the Special Issue Modeling the Adaptations of Agricultural Production to Climate Change)

Research

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14 pages, 3790 KiB

Open AccessArticle

Modelling the Geographical Distribution Pattern of Apple Trees on the Loess Plateau, China

by Wei Xu, Yuqi Miao, Shuaimeng Zhu, Jimin Cheng and Jingwei Jin

Agriculture 2023, 13(2), 291; https://doi.org/10.3390/agriculture13020291 - 25 Jan 2023

Cited by 2 | Viewed by 1563

Abstract

The Loess Plateau, known for its fragile ecosystems, is one of the traditional apple-producing regions in China. Although some management measures are needed to enhance sustainable agriculture in response to the rising pressure of climate change, the geographic distribution of apple trees considering [...] Read more.

The Loess Plateau, known for its fragile ecosystems, is one of the traditional apple-producing regions in China. Although some management measures are needed to enhance sustainable agriculture in response to the rising pressure of climate change, the geographic distribution of apple trees considering multiple variables has not been considered. In this study, we used three software (the maximum entropy model, IDRISI, and ArcGIS) to simulate the potential distribution of suitable habitats and range shifts of apple trees in the near present and near future (i.e., the 2030s and the 2050s) under two climate scenarios (the Shared Socioeconomic Pathways (SSP)1-26 and SSP5-85), while taking a variety of environmental factors into account (e.g., temperature, precipitation, and terrain). After optimization, the class unsuitable habitat (CUH) changed the potential distribution pattern of apple trees on the Loess Plateau. Currently, the areas of lowly suitable habitat (LSH), moderately suitable habitat (MSH), highly suitable habitat (HSH), and CUH were 7.66 × 10⁴, 2.80 × 10⁴, 0.23 × 10⁴, and 18.05 × 10⁴ km², respectively. Compared to the centroid estimated under the climate of 1970–2000, the suitability range of apple trees was displaced to the northwest in both the 2030s and the 2050s in SSP5-85 (i.e., 63.88~81.30 km), causing a larger displacement in distance than SSP1-26 (i.e., 40.05~50.32 km). This study demonstrates the possible changes in the spatial distribution of apple trees on the Loess Plateau in the near future and may provide a strong basis for future policy making. Full article

(This article belongs to the Special Issue Modeling the Adaptations of Agricultural Production to Climate Change)

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19 pages, 3612 KiB

Open AccessArticle

The Prediction of Wheat Yield in the North China Plain by Coupling Crop Model with Machine Learning Algorithms

by Yanxi Zhao, Dengpan Xiao, Huizi Bai, Jianzhao Tang, De Li Liu, Yongqing Qi and Yanjun Shen

Agriculture 2023, 13(1), 99; https://doi.org/10.3390/agriculture13010099 - 29 Dec 2022

Cited by 8 | Viewed by 2123

Abstract

The accuracy prediction for the crop yield is conducive to the food security in regions and/or nations. To some extent, the prediction model for crop yields combining the crop mechanism model with statistical regression model (SRM) can improve the timeliness and robustness of [...] Read more.

The accuracy prediction for the crop yield is conducive to the food security in regions and/or nations. To some extent, the prediction model for crop yields combining the crop mechanism model with statistical regression model (SRM) can improve the timeliness and robustness of the final yield prediction. In this study, the accumulated biomass (AB) simulated by the Agricultural Production Systems sIMulator (APSIM) model and multiple climate indices (e.g., climate suitability indices and extreme climate indices) were incorporated into SRM to predict the wheat yield in the North China Plain (NCP). The results showed that the prediction model based on the random forest (RF) algorithm outperformed the prediction models using other regression algorithms. The prediction for the wheat yield at SM (the period from the start of grain filling to the milky stage) based on RF can obtain a higher accuracy (r = 0.86, RMSE = 683 kg ha⁻¹ and MAE = 498 kg ha⁻¹). With the progression of wheat growth, the performances of yield prediction models improved gradually. The prediction of yield at FS (the period from flowering to the start of grain filling) can achieve higher precision and a longer lead time, which can be viewed as the optimum period providing the decent performance of the yield prediction and about one month’s lead time. In addition, the precision of the predicted yield for the irrigated sites was higher than that for the rainfed sites. The APSIM-simulated AB had an importance of above 30% for the last three prediction events, including FIF event (the period from floral initiation to flowering), FS event (the period from flowering to the start of grain filling) and SM event (the period from the start of grain filling to the milky stage), which ranked first in the prediction model. The climate suitability indices, with a higher rank for every prediction event, played an important role in the prediction model. The winter wheat yield in the NCP was seriously affected by the low temperature events before flowering, the high temperature events after flowering and water stress. We hope that the prediction model can be used to develop adaptation strategies to mitigate the negative effects of climate change on crop productivity and provide the data support for food security. Full article

(This article belongs to the Special Issue Modeling the Adaptations of Agricultural Production to Climate Change)

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18 pages, 6283 KiB

Open AccessArticle

Assessing Drought, Flood, and High Temperature Disasters during Sugarcane Growth Stages in Southern China

by Pei Yao, Long Qian, Zhaolin Wang, Huayue Meng and Xueliang Ju

Agriculture 2022, 12(12), 2117; https://doi.org/10.3390/agriculture12122117 - 09 Dec 2022

Cited by 9 | Viewed by 1200

Abstract

As a globally important sugarcane-producing region, Southern China (SC) is severely affected by various agrometeorological disasters. This study aimed to comprehensively assess multiple sugarcane agrometeorological disasters with regards to sugarcane yield in SC. The standardized precipitation evapotranspiration index and the heat degree-days were [...] Read more.

As a globally important sugarcane-producing region, Southern China (SC) is severely affected by various agrometeorological disasters. This study aimed to comprehensively assess multiple sugarcane agrometeorological disasters with regards to sugarcane yield in SC. The standardized precipitation evapotranspiration index and the heat degree-days were employed to characterize drought, flood, and high temperature (HT) during sugarcane growth stages in three provinces in SC in the period 1970–2020. Moreover, the relationships between sugarcane climatic yield and disaster intensities were investigated. The results indicated that the most recent decade witnessed the most intensive sugarcane agrometeorological disasters; sugarcane drought and HT intensities significantly (p < 0.05) increased in one and two provinces, respectively. Central and western SC was most drought-prone, while eastern SC was most flood-prone; sugarcane HT was concentrated in southwestern SC. The mature stage exhibited the greatest monthly intensities of drought and flood; the most HT-prone growth stage varied with provinces. The relationships between drought/flood intensity and sugarcane climatic yield were significant in seven districts; the yield-reducing effect of sugarcane flood was more obvious than that of drought. In conclusion, this study provides references for agrometeorological disaster risk reduction for sugarcane in SC. Full article

(This article belongs to the Special Issue Modeling the Adaptations of Agricultural Production to Climate Change)

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15 pages, 2465 KiB

Open AccessArticle

Optimal Irrigation under the Constraint of Water Resources for Winter Wheat in the North China Plain

by Xiaoli Shi, Wenjiao Shi, Na Dai and Minglei Wang

Agriculture 2022, 12(12), 2057; https://doi.org/10.3390/agriculture12122057 - 30 Nov 2022

Cited by 3 | Viewed by 1480

Abstract

The North China Plain (NCP) has the largest groundwater depletion in the world, and it is also the major production area of winter wheat in China. For sustainable food production and sustainable use of irrigated groundwater, it is necessary to optimize the irrigation [...] Read more.

The North China Plain (NCP) has the largest groundwater depletion in the world, and it is also the major production area of winter wheat in China. For sustainable food production and sustainable use of irrigated groundwater, it is necessary to optimize the irrigation amount for winter wheat in the NCP. Previous studies on the optimal irrigation amount have less consideration of the groundwater constraint, which may result in the theoretical amount of optimal-irrigation exceeding the amount of regional irrigation availability. Based on the meteorological data, soil data, crop variety data, and field management data from field experimental stations of Tangshan, Huanghua, Luancheng, Huimin, Nangong, Ganyu, Shangqiu, Zhumadian and Shouxian, we simulated the variation of yield and water use efficiency (WUE) under different irrigation levels by using the CERES-Wheat model, and investigated the optimal irrigation amount for high yield (OI_y), water saving (OI_WUE), and the trade-off between high yield and water saving (OI_t) of winter wheat in the NCP. Based on the water balance theory, we then calculated the irrigation availability, which was taken as the constraint to explore the optimal irrigation amount for winter wheat in the NCP. The results indicated that the OI_y ranged from 80 mm to 240 mm, and the OI_WUE was 17% to 67% less than OI_y, ranging from 0 mm to 200 mm. The OI_t was between 80 mm and 240 mm, realizing the co-benefits of high yield and water saving. Finally, we determined the optimal irrigation amount (62–240 mm) by the constraint of irrigation availability. Our results can provide a realistic and scientific reference for the security of both grain production and groundwater use in the NCP. Full article

(This article belongs to the Special Issue Modeling the Adaptations of Agricultural Production to Climate Change)

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20 pages, 14371 KiB

Open AccessArticle

Characteristics of Potential Evapotranspiration Changes and Its Climatic Causes in Heilongjiang Province from 1960 to 2019

by Tangzhe Nie, Rong Yuan, Sihan Liao, Zhongxue Zhang, Zhenping Gong, Xi Zhao, Peng Chen, Tiecheng Li, Yanyu Lin, Chong Du, Changlei Dai and Hao Jiang

Agriculture 2022, 12(12), 2017; https://doi.org/10.3390/agriculture12122017 - 26 Nov 2022

Cited by 9 | Viewed by 1274

Abstract

Climate change refers to the statistically significant changes in the mean and dispersion values of meteorological factors. Characterizing potential evapotranspiration (ET₀) and its climatic causes will contribute to the estimation of the atmospheric water cycle under climate change. In this [...] Read more.

Climate change refers to the statistically significant changes in the mean and dispersion values of meteorological factors. Characterizing potential evapotranspiration (ET₀) and its climatic causes will contribute to the estimation of the atmospheric water cycle under climate change. In this study, based on daily meteorological data from 26 meteorological stations in Heilongjiang Province from 1960 to 2019, ET₀ was calculated by the Penman–Monteith formula, linear regression method and the Mann–Kendall trend test were used to reveal the seasonal and inter-annual changing trend of ET₀. The sensitivity-contribution rate method was used to clarify the climatic factors affecting ET₀. The results showed that: (1) From 1960 to 2019, the maximum temperature (T_max), minimum temperature (T_min) and average temperature (T_mean) showed an increasing trend, with climate tendency rate of 0.22 °C per decade (10a), 0.49 °C/(10a), 0.36 °C/(10a), respectively. The relative humidity (RH), wind speed (U) and net radiation (Rn) showed a decreasing trend, with a climate tendency rate of −0.42%/(10a), −0.18 m/s/(10a), −0.08 MJ/m²/(10a), respectively. (2) ET₀ showed a decreasing trend on seasonal and inter-annual scales. Inter-annually, the average climate tendency rate of ET₀ was −8.69 mm/(10a). seasonally, the lowest climate tendency rate was −6.33 mm/(10a) in spring. (3) ET₀ was negatively sensitive to T_min, and RH, while positively sensitive to T_max, T_meanU and Rn, its sensitivity coefficient of U was the highest, which was 1.22. (4) The contribution rate of U to ET₀ was the highest on an inter-annual scale as well as in spring and autumn, which were −8.96%, −9.79% and −13.14%, respectively, and the highest contribution rate to ET₀ were Rn and T_min in summer and winter, whose contribution rates were −4.37% and −11.46%, respectively. This study provides an understanding on the response of evapotranspiration to climatic change and further provides support on the optimal allocation of regional water resource and agricultural water management under climate change. Full article

(This article belongs to the Special Issue Modeling the Adaptations of Agricultural Production to Climate Change)

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19 pages, 9067 KiB

Open AccessArticle

Climate Change Affects the Utilization of Light and Heat Resources in Paddy Field on the Songnen Plain, China

by Ennan Zheng, Mengting Qin, Peng Chen, Tianyu Xu and Zhongxue Zhang

Agriculture 2022, 12(10), 1648; https://doi.org/10.3390/agriculture12101648 - 09 Oct 2022

Cited by 6 | Viewed by 1484

Abstract

Efficient utilization of light and heat resources is an important part of cleaner production. However, exploring the changes in light and heat resources utilization potential in paddy under future climate change is essential to make full use of the potential of rice varieties [...] Read more.

Efficient utilization of light and heat resources is an important part of cleaner production. However, exploring the changes in light and heat resources utilization potential in paddy under future climate change is essential to make full use of the potential of rice varieties and ensure high-efficient, high-yield, and high-quality rice production, which has been seldom conducted. In our study, a process-based crop model (CERES-Rice) was calibrated and validated based on experiment data from the Songnen Plain of China, and then driven by multiple global climate models (GCMs) from the coupled model inter-comparison project (CMIP6) to predict rice growth period, yield, and light and heat resources utilization efficiency under future climate change conditions. The results indicated that the rice growth period would be shortened, especially in the high emission scenario (SSP585), while rice yield would increase slightly under the low and medium emission scenarios (SSP126 and SSP245), it decreased significantly under the high emission scenario (SSP585) in the long term (the 2080s) relative to the baseline of 2000–2019. The light and temperature resources utilization (E_RT), light utilization efficiency (E_R), and heat utilization efficiency (HUE) were selected as the light and heat resources utilization evaluation indexes. Compared with the base period, the mean E_RT in the 2040s, 2060s, and 2080s were −6.46%, −6.01%, and −6.03% under SSP126, respectively. Under SSP245, the mean E_RT were −7.89%, −8.41%, and −8.27%, respectively. Under SSP585, the mean E_RT were −6.88%, −13.69%, and −28.84%, respectively. The E_R would increase slightly, except for the 2080s under the high emission scenario. Moreover, the HUE would reduce as compared with the base period. The results of the analysis showed that the most significant meteorological factor affecting rice growth was temperature. Furthermore, under future climate conditions, optimizing the sowing date could make full use of climate resources to improve rice yield and light and heat resource utilization indexes, which is of great significance for agricultural cleaner production in the future. Full article

(This article belongs to the Special Issue Modeling the Adaptations of Agricultural Production to Climate Change)

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17 pages, 3417 KiB

Open AccessArticle

Spatial and Temporal Variability of ET_o in Xinjiang Autonomous Region of China during 1957–2017

by Yanhui Jia, Xiaojun Shen, Ruochen Yi and Ni Song

Agriculture 2022, 12(9), 1380; https://doi.org/10.3390/agriculture12091380 - 02 Sep 2022

Cited by 4 | Viewed by 1301

Abstract

This article scientifically studies the direct impact of climate problems on the time transition of reference crop evapotranspiration in the Xinjiang Autonomous Region of China from 1957 to 2017, which is conducive to formulating irrigation scheduling and adaptive capacity countermeasures. The objective of [...] Read more.

This article scientifically studies the direct impact of climate problems on the time transition of reference crop evapotranspiration in the Xinjiang Autonomous Region of China from 1957 to 2017, which is conducive to formulating irrigation scheduling and adaptive capacity countermeasures. The objective of this study is to investigate the impacts of climate change on ET_o for the cotton growing seasons. The meteorological data were collected from 48 meteorological stations in the region and analyzed using the Mann–Kendall test and linear trend. The results show the following points: (1) the ET_o decreases from low to high elevations, and with the increase in northern latitude. (2) The annual mean ET_o and average values of ET_o during the growing seasons for cotton exhibited two abrupt changes in the period 1957–2017, with the first abrupt change in 1995 to 1999 and the second abrupt change in 2006 to 2011. (3) The ET_o in Xinjiang of China demonstrates a decreasing trend during 1957–1996; a significant decreasing trend during 1997–2008; and a significant increasing trend during 2009–2017. Full article

(This article belongs to the Special Issue Modeling the Adaptations of Agricultural Production to Climate Change)

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18 pages, 2574 KiB

Open AccessArticle

Cropland Expansion Mitigates the Supply and Demand Deficit for Carbon Sequestration Service under Different Scenarios in the Future—The Case of Xinjiang

by Mingjie Shi, Hongqi Wu, Pingan Jiang, Wenjiao Shi, Mo Zhang, Lina Zhang, Haoyu Zhang, Xin Fan, Zhuo Liu, Kai Zheng, Tong Dong and Muhammad Fahad Baqa

Agriculture 2022, 12(8), 1182; https://doi.org/10.3390/agriculture12081182 - 09 Aug 2022

Cited by 11 | Viewed by 1984

Abstract

China’s double carbon initiative faces huge challenges, and understanding the carbon sequestration service of terrestrial ecosystems under future interannual regional land use change is important to respond to China’s carbon policy effectively. Previous studies have recognized the important impact of land use/land cover [...] Read more.

China’s double carbon initiative faces huge challenges, and understanding the carbon sequestration service of terrestrial ecosystems under future interannual regional land use change is important to respond to China’s carbon policy effectively. Previous studies have recognized the important impact of land use/land cover (LULC) planning on carbon sequestration in terrestrial ecosystem services (ESs). However, exploring trends in carbon sequestration under sustainable development scenarios that combine economic and ecological development, particularly the mechanisms that balance the supply and demand of carbon sequestration, still requires in-depth exploration in different geographical contexts. In this study, we present the LULC simulation framework from 2000 to 2030 for four different development scenarios in the Xinjiang region, located in an important Belt and Road region, including business as usual (BAU), rapid economic development (RED), ecological land protection (ELP), and sustainable development with both economic and ecological development (SD). Our results suggest that both the supply and demand of carbon stock in Xinjiang will increase in 2025 and 2030, with the demand exceeding the supply. However, our scenario planning mitigates the supply and demand deficit situation for carbon sequestration in the context of future cropland expansion in different scenarios. In summary, our study’s findings will enrich the study of carbon sequestration under future scenarios in the Belt and Road region. Xinjiang should pay more attention to the dynamic changes in landscape type structure and its carbon storage supply and demand caused by cultivated land expansion. Among the four scenarios, the spatial difference between carbon storage supply and demand based on the SD scenario is the smallest, which is more in line with the high-quality development of regional ecological security in Xinjiang. Full article

(This article belongs to the Special Issue Modeling the Adaptations of Agricultural Production to Climate Change)

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16 pages, 5794 KiB

Open AccessArticle

A Gas Diffusion Analysis Method for Simulating Surface Nitrous Oxide Emissions in Soil Gas Concentrations Measurement

by K. M. T. S. Bandara, Kazuhito Sakai, Tamotsu Nakandakari and Kozue Yuge

Agriculture 2022, 12(8), 1098; https://doi.org/10.3390/agriculture12081098 - 26 Jul 2022

Cited by 1 | Viewed by 1934

Abstract

The detection of low gas concentrations from the soil surface demands expensive high-precision devices to estimate nitrous oxide (N₂O) flux. As the prevalence of N₂O concentration in the soil atmosphere is higher than its surface, the present study aimed [...] Read more.

The detection of low gas concentrations from the soil surface demands expensive high-precision devices to estimate nitrous oxide (N₂O) flux. As the prevalence of N₂O concentration in the soil atmosphere is higher than its surface, the present study aimed to simulate N₂O surface flux (CF) from soil gas measured in a soil-interred silicone diffusion cell using a low-cost device. The methodological steps included the determination of the diffusion coefficient of silicone membrane (D_slcn), the measurement of the temporal variations in the N₂O gas in the soil (C_si) and on the surface (MF), and the development of a simulation process for predicting CF. Two experiments varying the procedure and periods of soil moisture saturation in each fertilized soil sample were conducted to detect C_si and MF. Using D_slcn and C_si, the variations in the soil gas (C_soil) were predicted by solving the diffusion equation using the implicit finite difference analysis method. Similarly, using six soil gas diffusivity models, the CF values were simulated from C_soil. For both experiments, statistical tests confirmed the good agreement of CF with MF for soil gas diffusivity models 4 and 5. We suggest that the tested simulation method is appropriate for predicting N₂O surface emissions. Full article

(This article belongs to the Special Issue Modeling the Adaptations of Agricultural Production to Climate Change)

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13 pages, 5228 KiB

Open AccessArticle

Spatiotemporal Change of Heat Stress and Its Impacts on Rice Growth in the Middle and Lower Reaches of the Yangtze River

by Shuai Zhang

Agriculture 2022, 12(8), 1097; https://doi.org/10.3390/agriculture12081097 - 26 Jul 2022

Cited by 2 | Viewed by 1292

Abstract

Heat stress will restrict rice yield in the middle and lower reaches of the Yangtze River. An understanding of the meteorological conditions of heat stress of rice production is important for improving the accuracy of the phenology simulation. Based on the observations of [...] Read more.

Heat stress will restrict rice yield in the middle and lower reaches of the Yangtze River. An understanding of the meteorological conditions of heat stress of rice production is important for improving the accuracy of the phenology simulation. Based on the observations of phenology and heat stress of rice agrometeorological stations in this region, as well as meteorological observations and future scenarios, this study analyzed the spatiotemporal change of heat stress and its impacts on rice growth in this region from 1990 to 2009. The results showed that the heat stress frequency of early rice increased in this region from 2000 to 2009, and that of late rice and single-season rice decreased. Moreover, rice phenology will advance under heat stress conditions. The spatiotemporal consistency of the observations and the meteorological index of heat stress shows that the change in heat stress is attributed to climate changes and extreme meteorological events. Under future climate scenarios, it is found that the frequency of heat stress will increase, which will have a serious impact on rice production. The results suggest that positive and effective measures should be taken to adapt to climate change for rice production. Full article

(This article belongs to the Special Issue Modeling the Adaptations of Agricultural Production to Climate Change)

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20 pages, 2507 KiB

Open AccessArticle

Development of Data-Driven Models to Predict Biogas Production from Spent Mushroom Compost

by Reza Salehi, Qiuyan Yuan and Sumate Chaiprapat

Agriculture 2022, 12(8), 1090; https://doi.org/10.3390/agriculture12081090 - 24 Jul 2022

Cited by 5 | Viewed by 1861

Abstract

In this study, two types of data-driven models were proposed to predict biogas production from anaerobic digestion of spent mushroom compost supplemented with wheat straw as a nutrient source. First, a k-nearest neighbours (k-NN) model (k = 1–10) was [...] Read more.

In this study, two types of data-driven models were proposed to predict biogas production from anaerobic digestion of spent mushroom compost supplemented with wheat straw as a nutrient source. First, a k-nearest neighbours (k-NN) model (k = 1–10) was constructed. The optimal k value was determined using the cross-validation (CV) method. Second, a support vector machine (SVM) model was developed. The linear, quadratic, cubic, and Gaussian models were examined as kernel functions. The kernel scale was set to 6.93, while the box constraint (C) was optimized using the CV method. Results demonstrated that R² for the k-NN model (k = 2) was 0.9830 at 35 °C and 0.9957 at 55 °C. The Gaussian-based SVM model (C = 1200) provided an R² of 0.9973 at 35 °C and 0.9989 at 55 °C, which are slightly better than those achieved by k-NN. The Gaussian-based SVM model produced RMSE of 0.598 at 35 °C and 0.4183 at 55 °C, which are 58.4% and 49.5% smaller, respectively, than those produced by the k-NN. These findings imply that SVM modeling can be considered a robust technique in predicting biogas production from AD processes as they can be implemented without requiring prior knowledge of biogas production kinetics. Full article

(This article belongs to the Special Issue Modeling the Adaptations of Agricultural Production to Climate Change)

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16 pages, 2048 KiB

Open AccessFeature PaperArticle

Modeling Adaptive Strategies on Maintaining Wheat-Corn Production and Reducing Net Greenhouse Gas Emissions under Climate Change

by Xiaopei Yi, Naijie Chang, Wuhan Ding, Chi Xu, Jing Zhang, Jianfeng Zhang and Hu Li

Agriculture 2022, 12(8), 1089; https://doi.org/10.3390/agriculture12081089 - 24 Jul 2022

Cited by 1 | Viewed by 1812

Abstract

Climate change has posed serious challenges to food production and sustainable development. We evaluated crop yields, N₂O emissions, and soil organic carbon (SOC) in a typical wheat–corn rotation system field on the North China Plain on a 50-year scale using the [...] Read more.

Climate change has posed serious challenges to food production and sustainable development. We evaluated crop yields, N₂O emissions, and soil organic carbon (SOC) in a typical wheat–corn rotation system field on the North China Plain on a 50-year scale using the Denitrification–Decomposition (DNDC) model and proposed adaptive strategies for each climate scenarios. The study showed a good consistency between observations and simulations (R² > 0.95 and nRMSE < 30%). Among the twelve climate scenarios, we explored ten management practices under four climate scenarios (3 °C temperature change: P/T−3 and P/T+3; 30% precipitation change: 0.7P/T and 1.3P/T), which have a significant impact on crop yields and the net greenhouse effect. The results revealed that changing the crop planting time (CP) and using cold-resistant (CR) varieties could reduce the net greenhouse effect by more than 1/4 without sacrificing crop yields under P/T−3. Straw return (SR) minimized the negative impact on yields and the environment under P/T+3. Fertigation (FG) and Drought-Resistant (DR) varieties reduced the net greenhouse effect by more than 8.34% and maintained yields under 0.7P/T. SR was most beneficial to carbon sequestration, and yields were increased by 3.87% under 1.3P/T. Multiple adaptive strategies should be implemented to balance yields and reduce the environmental burden under future climate change. Full article

(This article belongs to the Special Issue Modeling the Adaptations of Agricultural Production to Climate Change)

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22 pages, 3065 KiB

Open AccessArticle

The Impacts of Climate Change on Water Resources and Crop Production in an Arid Region

by Samira Shayanmehr, Jana Ivanič Porhajašová, Mária Babošová, Mahmood Sabouhi Sabouni, Hosein Mohammadi, Shida Rastegari Henneberry and Naser Shahnoushi Foroushani

Agriculture 2022, 12(7), 1056; https://doi.org/10.3390/agriculture12071056 - 19 Jul 2022

Cited by 12 | Viewed by 4711

Abstract

Climate change is one of the most pressing global issues of the twenty-first century. This phenomenon has an increasingly severe impact on water resources and crop production. The main purpose of this study is to evaluate the impact of climate change on water [...] Read more.

Climate change is one of the most pressing global issues of the twenty-first century. This phenomenon has an increasingly severe impact on water resources and crop production. The main purpose of this study is to evaluate the impact of climate change on water resources, crop production, and agricultural sustainability in an arid environment in Iran. To this end, the study constructs a new integrated climate-hydrological-economic model to assess the impact of future climate change on water resources and crop production. Furthermore, the agricultural sustainability is evaluated using the multicriteria decision making (MCDM) technique in the context of climate change. The findings regarding the prediction of climate variables show that the minimum and maximum temperatures are expected to increase by about 5.88% and 6.05%, respectively, while precipitation would decrease by approximately 30.68%. The results of the research reveal that water availability will decrease by about 13.79–15.45% under different climate scenarios. Additionally, the findings show that in the majority of cases crop production will reduce in response to climate scenarios so that rainfed wheat will experience the greatest decline (approximately 59.95%). The results of the MCDM model show that climate change can have adverse effects on economic and environmental aspects and, consequently, on the sustainability of the agricultural system of the study area. Our findings can inform policymakers on effective strategies for mitigating the consequences of climate change on water resources and agricultural production in dry regions. Full article

(This article belongs to the Special Issue Modeling the Adaptations of Agricultural Production to Climate Change)

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21 pages, 7942 KiB

Open AccessArticle

Responses of Soybean Water Supply and Requirement to Future Climate Conditions in Heilongjiang Province

by Na Li, Tangzhe Nie, Yi Tang, Dehao Lu, Tianyi Wang, Zhongxue Zhang, Peng Chen, Tiecheng Li, Linghui Meng, Yang Jiao and Kaiwen Cheng

Agriculture 2022, 12(7), 1035; https://doi.org/10.3390/agriculture12071035 - 15 Jul 2022

Cited by 3 | Viewed by 1403

Abstract

Understanding future changes in water supply and requirement under climate change is of great significance for long-term water resource management and agricultural planning. In this study, daily minimum temperature (T_min), maximum temperature (T_max), solar radiation (Rad [...] Read more.

Understanding future changes in water supply and requirement under climate change is of great significance for long-term water resource management and agricultural planning. In this study, daily minimum temperature (T_min), maximum temperature (T_max), solar radiation (Rad), and precipitation for 26 meteorological stations under RCP4.5 and RCP8.5 of MIRCO5 for the future period 2021–2080 were downscaled by the LARS-WG model, daily average relative humidity (RH) was estimated using the method recommended by FAO-56, and reference crop evapotranspiration (ET₀), crop water requirement (ET_c), irrigation water requirement (I_r), effective precipitation (P_e), and coupling degree of ET_c and P_e (CD) for soybean during the growth period were calculated by the CROPWAT model in Heilongjiang Province, China. The spatial and temporal distribution of these variables and meteorological factors were analyzed, and the response of soybean water supply and requirement to climate change was explored. The result showed that the average T_min, T_max, and Rad under RCP4.5 and RCP8.5 increased by 0.2656 and 0.5368 °C, 0.3509 and 0.5897 °C, and 0.0830 and 0.0465 MJ/m², respectively, while the average RH decreased by 0.0920% and 0.0870% per decade from 2021 to 2080. The annual average ET₀, ET_c, P_e, and I_r under RCP4.5 for 2021–2080 were 542.89, 414.35, 354.10, and 102.44 mm, respectively, and they increased by 1.92%, 1.64%, 2.33%, and −2.12% under the RCP8.5, respectively. The ranges of CD under RCP4.5 and RCP8.5 were 0.66–0.95 and 0.66–0.96, respectively, with an average value of 0.84 for 2021–2080. Spatially, the CD showed a general trend of increasing first and then decreasing from west to east. In addition, ET₀, ET_c, and P_e increased by 9.55, 7.16, and 8.77 mm per decade, respectively, under RCP8.5, while I_r decreased by 0.65 mm per decade. Under RCP4.5 and RCP8.5, ET_c, P_e, and I_r showed an overall increasing trend from 2021 to 2080. This study provides a basis for water resources management policy in Heilongjiang Province, China. Full article

(This article belongs to the Special Issue Modeling the Adaptations of Agricultural Production to Climate Change)

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24 pages, 2500 KiB

Open AccessArticle

Simulating the Impacts of Climate Change on Maize Yields Using EPIC: A Case Study in the Eastern Cape Province of South Africa

by Dennis Junior Choruma, Frank Chukwuzuoke Akamagwuna and Nelson Oghenekaro Odume

Agriculture 2022, 12(6), 794; https://doi.org/10.3390/agriculture12060794 - 31 May 2022

Cited by 6 | Viewed by 2844

Abstract

Climate change has been projected to impact negatively on African agricultural systems. However, there is still an insufficient understanding of the possible effects of climate change on crop yields in Africa. In this study, a previously calibrated Environmental Policy Integrated Climate (EPIC) model [...] Read more.

Climate change has been projected to impact negatively on African agricultural systems. However, there is still an insufficient understanding of the possible effects of climate change on crop yields in Africa. In this study, a previously calibrated Environmental Policy Integrated Climate (EPIC) model was used to assess the effects of future climate change on maize (Zea mays L.) yield in the Eastern Cape Province of South Africa. The study aimed to compare maize yields obtained from EPIC simulations using baseline (1980–2010) weather data with maize yields obtained from EPIC using statistically downscaled future climate data sets for two future periods (mid-century (2040–2069) and late century (2070–2099)). We used three general circulation models (GCMs): BCC-CSM1.1, GFDL-ESM2M and MIROC-ES under two Representative Concentration Pathways (RCPs), RCP 4.5 and RCP 8.5, to drive the future maize yield simulations. Simulation results showed that for all three GCMs and for both future periods, a decrease in maize production was projected. Maize yield was projected to decrease by as much as 23.8% for MIROC, RCP 8.5, (2070–2099). The temperature was projected to rise by over 50% in winter under RCP 8.5 for both future periods. For both future scenarios, rainfall was projected to decrease in the summer months while increasing in the winter months. Overall, this study provides preliminary evidence that local farmers and the Eastern Cape government can utilise to develop local climate change adaptation strategies. Full article

(This article belongs to the Special Issue Modeling the Adaptations of Agricultural Production to Climate Change)

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15 pages, 8417 KiB

Open AccessArticle

North Expansion of Winter Wheat Planting Area in China under Different Emissions Scenarios

by Maowei Wu, Yang Xu, Jingyun Zheng and Zhixin Hao

Agriculture 2022, 12(6), 763; https://doi.org/10.3390/agriculture12060763 - 27 May 2022

Cited by 3 | Viewed by 2105

Abstract

Suitable planting areas for winter wheat in north China are expected to shift northwardly due to climate change, however, increasing extreme events and the deficient water supply are threatening the security of planting systems. Thus, based on predicted climate data for 2021–2050 under [...] Read more.

Suitable planting areas for winter wheat in north China are expected to shift northwardly due to climate change, however, increasing extreme events and the deficient water supply are threatening the security of planting systems. Thus, based on predicted climate data for 2021–2050 under the Shared Socioeconomic Pathways (SSP1-2.6, SSP3-7.0, and SSP5-8.5) emission scenarios, as well as historical data from 1961–1990, we use four critical parameters of percentages of extreme minimum temperature years (POEMTY), first day of the overwintering period (FD), sowing date (SD), and precipitation before winter (PBW), in order to determine the planting boundary of winter wheat. The results show that the frequency of extreme minimum temperature occurrences is expected to decrease in the North winter wheat area, which will result in a northward movement of the western part of northern boundary by 73, 94, and 114 km on average, in addition to FD delays ranging from 6.0 to 10.5 days. Moreover, agrometeorological conditions in the Huang-Huai winter wheat area are expected to exhibit more pronounced changes than the rest of the studied areas, especially near the southern boundary, which is expected to retreat by approximately 213, 215, and 233 km, northwardly. The north boundary is expected to move 90–140 km northward. Therefore, the change in southern and northern boundaries will lead the potential planting areas of the entire North winter wheat area to increase by 10,700 and 28,000 km² on average in the SSP3-7.0 and SSP5-8.5 scenarios, respectively, but to decrease by 38,100 km² in the SSP1-2.6 scenario; however, the lack of precipitation remains a limitation for extending planting areas in the future. Full article

(This article belongs to the Special Issue Modeling the Adaptations of Agricultural Production to Climate Change)

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14 pages, 4659 KiB

Open AccessArticle

Maximum Entropy Niche-Based Modeling for Predicting the Potential Suitable Habitats of a Traditional Medicinal Plant (Rheum nanum) in Asia under Climate Change Conditions

by Wei Xu, Shuaimeng Zhu, Tianli Yang, Jimin Cheng and Jingwei Jin

Agriculture 2022, 12(5), 610; https://doi.org/10.3390/agriculture12050610 - 26 Apr 2022

Cited by 7 | Viewed by 1983

Abstract

Rheum nanum, a perennial herb, is a famous traditional Chinese medicinal plant that has great value in modern medicine. In order to determine the potential distribution of R. nanum in Asia, we specifically developed the potential distribution maps for three periods [...] Read more.

Rheum nanum, a perennial herb, is a famous traditional Chinese medicinal plant that has great value in modern medicine. In order to determine the potential distribution of R. nanum in Asia, we specifically developed the potential distribution maps for three periods (current, 2050s: 2041–2060, and 2070s: 2061–2080) using MaxEnt and ArcGIS, and these were based on the current and future climate data under two climate scenarios (RCP2.6 and RCP6.0). To predict the potential impacts of global warming, we measured the area of suitable habitats, habitat suitability changes, and habitat core changes. We found that bio16 (i.e., the precipitation of the wettest quarter) and bio1 (i.e., the annual mean temperature) were the most important climate factors that influenced the distribution of R. nanum. The areas of high suitable habitats (HH) and middle suitable habitats (MH) in the current period were 156,284.7 ± 0.99 km² and 361,875.0 ± 3.61 km², respectively. The areas of HH and MH in 2070RCP6.0 were 27,309.0 ± 0.35 km² and 123,750 ± 2.36 km², respectively. The ranges of 82.0–90.3° E, 43.8–46.5° N were the mostly degraded areas of the 2050s and 2070s, and RCP6.0 had a larger decrease in habitable area than that found in RCP2.6. All the HH cores shifted south, and the shift distance of HH in 2070RCP6.0 was 115.65 km. This study provides a feasible approach for efficiently utilizing low-number occurrences, and presents an important attempt at predicting the potential distribution of species based on a small sample size. This may improve our understanding of the impacts of global warming on plant distribution and could be useful for relevant agricultural decision-making. Full article

(This article belongs to the Special Issue Modeling the Adaptations of Agricultural Production to Climate Change)

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16 pages, 3752 KiB

Open AccessArticle

Improving Winter Wheat Yield Forecasting Based on Multi-Source Data and Machine Learning

by Yuexia Sun, Shuai Zhang, Fulu Tao, Rashad Aboelenein and Alia Amer

Agriculture 2022, 12(5), 571; https://doi.org/10.3390/agriculture12050571 - 19 Apr 2022

Cited by 9 | Viewed by 2463

Abstract

To meet the challenges of climate change, population growth, and an increasing food demand, an accurate, timely and dynamic yield estimation of regional and global crop yield is critical to food trade and policy-making. In this study, a machine learning method (Random Forest, [...] Read more.

To meet the challenges of climate change, population growth, and an increasing food demand, an accurate, timely and dynamic yield estimation of regional and global crop yield is critical to food trade and policy-making. In this study, a machine learning method (Random Forest, RF) was used to estimate winter wheat yield in China from 2014 to 2018 by integrating satellite data, climate data, and geographic information. The results show that the yield estimation accuracy of RF is higher than that of the multiple linear regression method. The yield estimation accuracy can be significantly improved by using climate data and geographic information. According to the model results, the estimation accuracy of winter wheat yield increases dramatically and then flattens out over months; it approached the maximum in March, with R² and RMSE reaching 0.87 and 488.59 kg/ha, respectively; this model can achieve a better yield forecasting at a large scale two months in advance. Full article

(This article belongs to the Special Issue Modeling the Adaptations of Agricultural Production to Climate Change)

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19 pages, 6692 KiB

Open AccessArticle

Projections of Drought Characteristics Based on the CNRM-CM6 Model over Africa

by Isaac Kwesi Nooni, Daniel Fiifi Tawia Hagan, Waheed Ullah, Jiao Lu, Shijie Li, Nana Agyemang Prempeh, Gnim Tchalim Gnitou and Kenny Thiam Choy Lim Kam Sian

Agriculture 2022, 12(4), 495; https://doi.org/10.3390/agriculture12040495 - 31 Mar 2022

Cited by 4 | Viewed by 2423

Abstract

In a warming climate, drought events are projected to increase in many regions across the world, which would have detrimental impacts on water resources for agriculture activity and human life. Thus, projecting drought changes, especially the frequency of future drought events, is very [...] Read more.

In a warming climate, drought events are projected to increase in many regions across the world, which would have detrimental impacts on water resources for agriculture activity and human life. Thus, projecting drought changes, especially the frequency of future drought events, is very important for the African continent. This study investigates the future changes in drought events based on the France Centre National de Recherches Météorologiques (CNRM-CM6) model in the Coupled Model Intercomparison Project phase six (CMIP6) datasets for four shared socio-economic pathways (SSP): SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5; and three time slices: near future (2020–2039), mid-century (2050–2069), and end-of-century (2080–2099), relative to a historical baseline period (1995–2014). The interannual variability and trends of the self-calibrating Palmer Drought Severity Index (scPDSI) based on the Penman–Monteith methods for measuring potential evapotranspiration (PET) are used to estimate future droughts. The temporal analysis shows that the drought frequency, intensity, and affected area will increase throughout the 21st century. Among the scenarios, SSP3-7.0 and SSP5-8.5 project a larger upward trend in drought characteristics than SSP1-2.6 and SSP2-4.5. The spatial pattern shows drought frequency decreases in humid regions and increases in non-humid regions across Africa. For all SSP scenarios, the projected wetting trend per decade ranges from 0.05 to 0.25, while the drying trend per decade ranges from −0.05 to 0.25. A regional trend analysis revealed key differences in spatial pattern, with varied trend projections of wetter and drier conditions in humid and non-humid regions under all SSP scenarios. Drier conditions are expected to intensify in Southern Africa under all SSP scenarios but are projected to be more intense under either SSP3-7.0 and SSP5-8.5. In general, the projected wetter trends in humid areas may favor agricultural production and ecological conservation, and drier trends in non-humid regions may call for the possible adoption of tailor-made drought adaptation strategies and development programmes to minimize impacts. Full article

(This article belongs to the Special Issue Modeling the Adaptations of Agricultural Production to Climate Change)

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20 pages, 41812 KiB

Open AccessFeature PaperArticle

Future Projection for Climate Suitability of Summer Maize in the North China Plain

by Yanxi Zhao, Dengpan Xiao, Huizi Bai, Jianzhao Tang and Deli Liu

Agriculture 2022, 12(3), 348; https://doi.org/10.3390/agriculture12030348 - 28 Feb 2022

Cited by 9 | Viewed by 2464

Abstract

Climate change has and will continue to exert significant effects on social economy, natural environment, and human life. Research on the climatic suitability of crops is critical for mitigating and adapting to the negative impacts of climate change on crop production. In the [...] Read more.

Climate change has and will continue to exert significant effects on social economy, natural environment, and human life. Research on the climatic suitability of crops is critical for mitigating and adapting to the negative impacts of climate change on crop production. In the study, we developed the climate suitability model of maize and investigated the climate suitability of summer maize during the base period (1981–2010) and two future periods of 2031–2060 (2040s) and 2071–2100 (2080s) in the North China Plain (NCP) based on BCC-CSM2-MR model (BCC) from the Coupled Model Comparison Program (CMIP6) under two Shared Socioeconomic Pathways (SSP) 245 and SSP585. The phenological shift of maize under future climate scenarios was simulated by the Agricultural Production Systems Simulator (APSIM). The results showed that the root mean square errors (RMSE) between observations and projections for sunshine suitability (S_S), temperature suitability (S_T), precipitation suitability (S_P), and integrated climate suitability (S_Z) during the whole growth period were 0.069, 0.072, 0.057, and 0.040, respectively. Overall, the BCC projections for climate suitability were in suitable consistency with the observations in the NCP. During 1981–2010, the S_P, S_T, and S_Z were high in the north of the NCP and low in the south. The S_P, S_T, and S_Z showed a downward trend under all the future climate scenarios in most areas of NCP while the S_S increased. Therein, the change range of S_P and S_S was 0–0.1 under all the future climate scenarios. The S_T declined by 0.1–0.2 in the future except for the decrease of more than 0.3 under the SSP585 scenario in the 2080s. The decrease in S_Z in the 2040s and 2080s under both SSP scenarios varied from 0 to 0.2. Moreover, the optimum area decreases greatly under future scenarios while the suitable area increases significantly. Adjusting sowing data (SD) would have essential impacts on climate suitability. To some extent, delaying SD was beneficial to improve the climate suitability of summer maize in the NCP, especially under the SSP585 scenario in the 2080s. Our findings can not only provide data support for summer maize production to adapt to climate change but also help to propose agricultural management measures to cope with future climate change. Full article

(This article belongs to the Special Issue Modeling the Adaptations of Agricultural Production to Climate Change)

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20 pages, 6394 KiB

Open AccessEditor’s ChoiceArticle

Multiscale Assessments of Three Reanalysis Temperature Data Systems over China

by Xiaolong Huang, Shuai Han and Chunxiang Shi

Agriculture 2021, 11(12), 1292; https://doi.org/10.3390/agriculture11121292 - 19 Dec 2021

Cited by 18 | Viewed by 2561

Abstract

Temperature is one of the most important meteorological variables for global climate change and human sustainable development. It plays an important role in agroclimatic regionalization and crop production. To date, temperature data have come from a wide range of sources. A detailed understanding [...] Read more.

Temperature is one of the most important meteorological variables for global climate change and human sustainable development. It plays an important role in agroclimatic regionalization and crop production. To date, temperature data have come from a wide range of sources. A detailed understanding of the reliability and applicability of these data will help us to better carry out research in crop modelling, agricultural ecology and irrigation. In this study, temperature reanalysis products produced by the China Meteorological Administration Land Data Assimilation System (CLDAS), the U.S. Global Land Data Assimilation System (GLDAS) and the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis version5 (ERA5)-Land are verified against hourly observations collected from 2265 national automatic weather stations (NAWS) in China for the period 2017–2019. The above three reanalysis systems are advanced and widely used multi-source data fusion and re-analysis systems at present. The station observations have gone through data Quality Control (QC) and are taken as “true values” in the present study. The three reanalysis temperature datasets were spatial interpolated using the bi-linear interpolation method to station locations at each time. By calculating the statistical metrics, the accuracy of the gridded datasets can be evaluated. The conclusions are as follows. (1) Based on the evaluation of temporal variability and spatial distribution as well as correlation and bias analysis, all the three reanalysis products are reasonable in China. (2) Statistically, the CLDAS product has the highest accuracy with the root mean square error (RMSE) of 0.83 °C. The RMSEs of the other two reanalysis datasets produced by ERA5-Land and GLDAS are 2.72 °C and 2.91 °C, respectively. This result indicates that the CLDAS performs better than ERA5-Land and GLDAS, while ERA5-Land performs better than GLDAS. (3) The accuracy of the data decreases with increasing elevation, which is common for all of the three products. This implies that more caution is needed when using the three reanalysis temperature data in mountainous regions with complex terrain. The major conclusion of this study is that the CLDAS product demonstrates a relatively high reliability, which is of great significance for the study of climate change and forcing crop models. Full article

(This article belongs to the Special Issue Modeling the Adaptations of Agricultural Production to Climate Change)

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