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Article

Climate Change Will Lead to a Significant Reduction in the Global Cultivation of Panicum milliaceum

by
Pan Jiang
1,†,
Junyi Jiang
2,†,
Cong Yang
3,
Xinchen Gu
4,5,
Yi Huang
6,* and
Liang Liu
1,*
1
School of Economics and Management, Southwest University of Science and Technology, Mianyang 621010, China
2
Haikou Marine Geological Survey Center, China Geological Survey, Haikou 571127, China
3
School of Life Science and Engineering, Southwest University of Science and Technology, Mianyang 621010, China
4
State Key Laboratory of Hydraulic Engineering Simulation and Safety, School of Civil Engineering, Tianjin University, Tianjin 300072, China
5
State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100044, China
6
College of Ecology and Environment, Tibet University, Lhasa 850011, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work and should be considered co-first authors.
Atmosphere 2023, 14(8), 1297; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos14081297
Submission received: 8 June 2023 / Revised: 11 August 2023 / Accepted: 14 August 2023 / Published: 16 August 2023
(This article belongs to the Special Issue Climate Change and Agriculture: Impacts and Adaptation)
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Versions Notes

Abstract

:
Panicum milliaceum is a specialty crop that maintains the economic stability of agriculture in arid and barren regions of the world. Predicting the potential geographic distribution of Panicum milliaceum globally and clarifying the ecological needs of Panicum milliaceum will help to advance the development of agriculture, which is important for the maintenance of human life and health. In this study, based on 5637 global distribution records of Panicum milliaceum, we used the MaxEnt model and ArcGIS software, the Beijing Climate Center Climate System Model (BCC-CSM2-MR) was selected to predict the potential global geographic distribution of Panicum milliaceum in the present and future in combination with the environmental factor variables; we evaluated the significant factors constraining the potential geographic distribution of Panicum milliaceum by combining the contributions of environmental factor variables; and we assessed the accuracy of the MaxEnt model by using AUC values and Kappa statistics. The results showed that the MaxEnt model was highly accurate, the simulation results were credible, and the total suitable area of Panicum milliaceum in the world is 4563.82 × 104 km2. The high habitat area of Panicum milliaceum is 484.95 × 104 km2, accounting for 10.63% of the total habitat area, and is mainly distributed in the United States, the Russian Federation, France, Ukraine, Australia, Germany, etc. The soil factor (hswd) was the most important environmental factor limiting the potential geographic distribution of Panicum milliaceum, followed by the precipitation factor (Precipitation of the Driest Month, bio14) and temperature factor (Mean Temperature of the Wettest Quarter, bio8). Under four future climate change scenarios, the area of the potential geographic distribution of Panicum milliaceum decreased to different extents at different levels compared to the contemporary period. Therefore, climate change may significantly affect the global distribution pattern of Panicum milliaceum cultivation in the future and thus reshape global Panicum milliaceum production and trade patterns.

1. Introduction

The factors affecting crop growth, development, and distribution have been widely discussed and studied for decades and have become an important scientific issue [1,2,3,4]. As a special plant resource, crops are closely related to human development [1,3]. Various factors, such as climate, landscape, hydrology, and soil type, influence their growth and development [2,5]. Research has shown that among these many influences, non-climatic factors only dominate short-term biological changes. Climate change is the most important factor affecting the growth, development, and suitable habitat area distribution of crops [6,7,8], and it is generally accepted that climate change is a key driver of agricultural development [3,9]. Global warming will significantly reduce crop yields, seriously threatening global food security and sustainable development [10]. Analysis of current trends suggests that basic self-sufficiency in food must be maintained to ensure long-term world peace [11,12]. Therefore, the systematic study of the geographical distribution patterns of crops and their response to climate change can provide effective scientific tools for global food crop production and cultivation management, which could provide an important scientific basis for regional sustainable development and theoretical support for regional ecological and environmental governance and is also of great importance for ensuring global food security.
Panicum milliaceum is an annual herb of the genus Dogwood in the family Gramineae (Figure 1a), which is dehusked as yellow rice (Figure 1b). It was one of the first domesticated crops in the world and is drought-tolerant, infertile, and likes high temperatures [13,14,15]. Panicum milliaceum is rich in natural active substances with hypoglycemic, hypolipidemic, and antioxidant functions, and it is also used as both a grain and feed for medicinal and food purposes, as well as having the potential to be used as a C4 biofuel (Figure 1c,d). As a major global miscellaneous crop, it is also popular because of its high yield, low price, and nutritional richness [16,17,18]. Current research on Panicum milliaceum focuses on nutrient composition, cultivation techniques, and other aspects. The consumption of Panicum milliaceum contributes to a diverse and balanced diet in a global context where a significant proportion of the population consumes too much high-protein food [19,20,21]. As mentioned above, Panicum milliaceum has the potential to maintain global food security and plays an important role in maintaining global human health. Several studies have used ecological modeling to predict the distribution of Panicum milliaceum’s suitable habitat area and clarify its ecological requirements [3,4,22,23,24]; however, no predictions have been made regarding its suitable habitat area on a global scale.
Species distribution models (SDMs) have been widely used to explore the effects of climate change on the potential geographic distribution of species, and these models also play an important role in inferring the potential geographic distribution of species using environmental factors [24,25,26,27]. There are more than ten popular species distribution models, including Bioclim, Domain, GARP, and MaxEnt, which have been widely used in the fields of plant conservation and utilization and pest invasion [28,29,30]. Compared with other models, the MaxEnt model has the following advantages: (1) The modeling process only requires distribution records. (2) Both continuous and discrete variables can be used as variables. (3) It is easy to operate and does not require complex format conversion of species distribution data and environmental data. (4) Only a small sample of species “presence” data is needed for simulation, and the simulation effect is excellent. (5) The theoretical basis is closely related to ecology, which facilitates understanding of species suitability [6,31]. The performance of simulations and predictions varies widely because of the different theoretical underpinnings of different models. Many studies have shown that the MaxEnt model gives the best simulation results among species distribution models [32]. In summary, we considered the MaxEnt model suitable for assessing the potential global distribution of Panicum milliaceum.
Based on the above discussion, we found that although there are many studies on the potential distribution of staple crops, there are few studies on the global scale of miscellaneous food crops, and there is no study on Panicum milliaceum yet. This study will reveal for the first time the current and future distribution of Panicum milliaceum on a global scale, which will help to advance the development of agriculture and is important for maintaining human life and health and solving the global food crisis. The planting of Panicum milliaceum faces several practical problems: (1) some of the potential planting areas of Panicum milliaceum are still unknown, and (2) there is an urgent need to determine the distribution of Panicum milliaceum in response to climate change in the context of global warming. To address the above issues, the present study was carried out to predict the potential global geographic distribution of Panicum milliaceum at different times, as shown in Figure 2, and to provide a theoretical reference for the cultivation of Panicum milliaceum. The main objectives of this study were: (1) To show the current and future distribution patterns of Panicum milliaceum; (2) to demonstrate trends in the potential distribution of Panicum milliaceum in the context of climate change; and (3) to explore the relationship between the distribution of Panicum milliaceum in response to dominant climate variables.

2. Material and Methods

2.1. Species Data Source

Information on the geographical distribution of Panicum milliaceum was obtained from field surveys and queries to the Global Biodiversity Database (http://www.gbif.org/ (accessed on 11 September 2022)), China Plantwise (http://www.iplant.cn (accessed on 11 September 2022)), and the China Digital Herbarium (https://www.cvh.ac.cn (accessed on 11 September 2022)). The distribution of Panicum milliaceum was then obtained by referring to the research methods of Zhao et al. [29] Finally, 5637 distribution points of Panicum milliaceum were obtained (Figure 3).

2.2. Sources of Environmental Variables

Climate and digital elevation data were downloaded from the WorldClim database for 19 current and 4 future emission scenarios (DEM, http://www.worldclim.org/ (accessed on 20 September 2022)). Soil data were obtained from the Harmonised World Soil Database (HWSD V1.2, https://www.fao.org/ (accessed on 20 September 2022)). UV-B radiation data were downloaded from the global UV-B download radiation dataset (gIUV, https://www.ufz.de/gluv/index.php (accessed on 20 September 2022)). The world administrative map was downloaded from the National Science and Technology Infrastructure System Science Data Centre (http://www.geodata.cn (accessed on 20 September 2022)). Twenty-four environmental variables were used in this study, including 19 climatic variables, 3 topographic variables, 1 total soil variable, and 1 UV variable. After inter-comparison, the Beijing Climate Center Climate System Model (BCC-CSM2-MR) with coupled mode 6 (CMIP6) was selected as the future climate model because the CMIP6 scenario model is closer to the real situation [33]. The spatial resolution of ArcGIS software (version 10.2) was used to unify all environmental variables. The resolution of all environmental variables was five arcmins [29,31]. As there is some correlation between environmental variables, a correlation analysis must be carried out before using them in the MaxEnt model. In this study, a Spearman correlation analysis was performed on the environmental factors, with the results shown in Figure 4. When the correlation coefficient of two environmental factors was ≥0.8, the one with the higher contribution was retained. Finally, eight environmental factors were retained for the MaxEnt model runs, as shown in Table 1.

2.3. Model Construction and Evaluation

The Panicum milliaceum distribution point and environmental factor data were imported separately into MaxEnt for modeling. Parameter settings: The test set was 25% of the distribution points, and the training set was 75%. The MaxEnt model requires the user to specify the number of parameters, namely the test training (i.e., the percentage of positions used for model development and internal testing of the test), the number of background points, the clamp (i.e., whether to restrict the prediction to the variability of the predicted environmental factor), and the canonical multiplier (i.e., to avoid over-fitting the response curve).
When species distribution models are used to predict species distributions, there are usually over- and underestimations (i.e., false positives and false negatives), so it is necessary to evaluate the accuracy of model simulations using valid evaluation metrics [26,30]. Commonly used theoretical evaluation metrics for species distribution models include overall accuracy, sensitivity, specificity, AUC values, TSS values, and kappa statistics. Different metrics have different evaluation methods and criteria, among which AUC, TSS, and Kappa are more widely used [29,34]. Therefore, to improve the credibility of the validation index, the three evaluation methods of the AUC value, TSS value, and Kappa statistic were selected to evaluate the model accuracy in this study. The accuracy of the evaluated model was classified into five levels: failure, poor, average, good, and very good. The AUC values were in the intervals (AUC ≤ 0.60, failure), (0.60 < AUC ≤ 0.70, poor), (0.70 < AUC ≤ 0.80, fair), (0.80 < AUC ≤ 0.90, good), and (0.90 < AUC ≤ 1.00, very good). The TSS values were in the intervals of (TSS ≤ 0.40, failure), (0.40 < TSS ≤ 0.55, poor), (0.55 < TSS ≤ 0.70, fair), (0.70 < TSS ≤ 0.85, good), and (0.85 < TSS ≤ 1.00, very good). The intervals for the Kappa statistic were (Kappa ≤ 0.40, failure), (0.40 < Kappa ≤ 0.55, poor), (0.55 < Kappa ≤ 0.70, fair), (0.70 < Kappa ≤ 0.85, good), and (0.85 < Kappa ≤ 1.00, very good).
In this study, different parameter configurations were selected for trial runs to evaluate the performance of the parameter configurations used to adjust the optimal parameters of the model. Firstly, based on the known distribution points of Panicum milliaceum and the corresponding environmental factors, RM was set from 0.5 to 4. Six feature combinations (FCs) were used to optimize the model parameters in order to select the best parameter combinations: L (linear features), LQ (linear features + quadratic features), H (hinge features), LQH (linear features + quadratic features + hinge features), LQHP (linear features + secondary features + hinge features + product features), and LQHPT (linear features + secondary features + hinge features + product features + threshold features). Finally, in this study, RM was set to 1, and the feature combination was LQHPT.

2.4. Classification of Potentially Suitable Areas

Liu et al. [6] and Lan et al. [34] generated ASCII raster layers based on logical values (p) of species presence probabilities, ranging from 0 to 1, with higher p values indicating a higher probability of species presence. Following the description of species “probability” in IPCC-CMIP6, suitable habitat areas were classified into four types: highly suitable habitats (0.5 ≤ p ≤ 1.0), medium-suitable habitats (0.3 ≤ p < 0.5), lowest suitable habitats (0.1 ≤ p < 0.3), and unsuitable habitats (0.0 ≤ p < 0.1).

3. Results and Discussion

3.1. Model Accuracy Evaluation

The simulation results of the potential geographic distribution of Panicum milliaceum were evaluated based on three performance indicators: AUC, TSS, and the Kappa statistic, and the results are shown in Figure 5. The AUC value of the MaxEnt model was “excellent”, the Kappa statistic was “Good”, and the TSS value was “Good”. All three evaluation indices indicated that the model was well set up and that the prediction results had high confidence.

3.2. Current Potential Geographical Distributions of Panicum milliaceum

The total suitable habitat area of Panicum milliaceum was 4563.82 × 104 km2 (Figure 6), mainly in the United States, Brazil, Australia, China, India, and the Russian Federation (Figure 7). The highly suitable habitat area of Panicum milliaceum was 484.95 × 104 km2, accounting for 10.63% of the total suitable habitat area (Figure 6), and was mainly distributed in the United States, the Russian Federation, France, Ukraine, Australia, and Germany (Figure 7). The medium suitable habitat area of Panicum milliaceum was 1038.08 × 104 km2, accounting for 22.74% of the total suitable habitat area (Figure 6), and was mainly distributed in the Russian Federation, United States, China, Australia, etc. (Figure 7). The lowest suitable habitat area of Panicum milliaceum was 3040.79 × 104 km2, accounting for 66.63% of the total suitable habitat area (Figure 6), and was mainly distributed in China, the United States, the Russian Federation, Brazil, India, Australia, etc. (Figure 7). In summary, the MaxEnt model predicted a very large potential geographical range for Panicum milliaceum, much larger than the contemporary geographical range (Figure 7).

3.3. Future Potential Geographical Distributions of Panicum milliaceum

In this study, the potentially suitable habitat of Panicum milliaceum was predicted under four (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) different emission scenarios in the 2050s and 2070s, resulting in a map of the potentially suitable habitat of Panicum milliaceum under climate change scenarios (Figure 8). Compared to current climate conditions, the suitable habitat area of Panicum milliaceum was drastically reduced at all levels under all four scenarios in the 2050s and 2070s. The greatest reduction in suitable habitat at all levels was observed under SSP2-4.5 for both time periods, followed by SSP3-7.0, SSP1-2.6, and SSP5-8.5 (Figure 6 and Figure 8).
Under SSP 5-8.5, the total suitable habitat area was reduced by 13.50% (2050s) and 13.33% (2070s), the highly suitable habitat area by 50.58% (2050s) and 50.20% (2070s), the medium-suitable habitat area by 17.08% (2050s) and 17.73% (2070s), and the lowest suitable habitat area by 6.37% (2050s) and 5.95% (2070s).
Under SSP3-7.0, the total suitable habitat area was reduced by 35.30% (2050s) and 35.55% (2070s), the highly suitable habitat area by 76.71% (2050s) and 76.75% (2070s), the medium-suitable habitat area by 46.51% (2050s) and 46.62% (2070s), and the lowest suitable habitat area by 24.88% (2050s) and 25.88% (2070s).
Under SSP2-4.5, the total suitable habitat area was reduced by 44.96% (2050s) and 44.62% (2070s), the highly suitable habitat area by 87.26% (2050s) and 87.49% (2070s), the medium-suitable habitat area by 55.03% (2050s) and 55.17% (2070s), and the lowest suitable habitat area by 34.77% (2050s) and 34.77% (2070s).
Under SSP1-2.6, the total suitable habitat area was reduced by 28.82% (the 2050s) and 29.55% (2070s), the highly suitable habitat area by 20.82% (2050s) and 21.62% (2070s), the medium-suitable habitat area by 33.65% (2050s) and 34.51% (2070s), and the lowest suitable habitat area by 68.67% (2050s) and 68.66% (2070s).
In summary, climate change will have a very strong impact on the suitable habitat area of Panicum milliaceum. Under different concentration emission scenarios in the 2050s and 2070s, the loss of potentially suitable habitats for Panicum milliaceum will occur to different degrees. A large part of the highly suitable habitat will be transformed into medium-suitable habitat and low-suitable habitat; a part of the medium-suitable habitat will be transformed into low-suitable habitat and unsuitable habitat; and many low-suitable habitat areas will disappear (Figure 6 and Figure 8).

3.4. Combination of Dominant Environmental Variables Affecting the Distribution of Panicum milliaceum

The current views of researchers on determining the number of dominant factors are inconsistent, and based on the contribution rate, so are the vast majority of current decisions. The environmental factors that reach a certain level are considered dominant factors, but the choice of level is subjective, so different criteria have emerged. This study used the environmental factors with the top three contribution rates as dominant environmental factor combinations. The quantitative statistics of the contribution rate (Figure 9) showed that the contribution rate of the soil factor (hswd), precipitation of the driest month (bio14), and the mean temperature of the wettest quarter (bio8) were much higher than other variables in each period and for each concentration emission background, so they were used as the dominant environmental factor combination in this study.
The relationship between the presence probability of Panicum milliaceum and the environmental factor could be judged from the response curve of the environmental factor variable. When the presence probability of Panicum milliaceum was greater than 0.5, the corresponding environmental factor value was favorable for the growth of Panicum milliaceum. In order to more visually reflect the influence of environmental variables on the distribution of Panicum milliaceum, the response curves of the combination of dominant environmental variables of Panicum milliaceum were derived in this study (Figure 10).

4. Discussion

4.1. Changes in the Potential Geographic Distribution of Panicum milliaceum under Future Climate Change Scenarios

In this study, the MaxEnt model was used to predict the changes in the potential global geographic distribution of Panicum milliaceum under future climate change scenarios using the environmental factor variables of four different emission scenarios combined with current climate conditions (Figure 11). The results showed that the potential geographic distribution of Panicum milliaceum under four emission scenarios in the 2050s and 2070s showed a reduction in area and fragmentation compared to current climatic conditions. The survival probability of Panicum milliaceum showed an increasing and then decreasing trend with increasing temperature and precipitation, probably because the temperature and precipitation values in many regions exceeded the threshold values suitable for the survival of Panicum milliaceum in the context of global warming, causing its survival probability to decrease in some regions. In particular, under the SSP2-4.5 emission scenario, the temperature and precipitation values were outside the range suitable for Panicum milliaceum survival, causing its potential suitable habitat to show a significant decreasing trend. Thomas et al. [35] studied the extinction risk of organisms in a sample area covering 20% of the Earth’s surface. They showed that 15–37% of species would be at risk of extinction under a moderate emissions concentration scenario in 2050, while others would be at little risk. Some species would benefit from climate warming, suggesting that the impact of climate warming on the potential geographic distribution of species is twofold. Not all species are at risk of extinction or loss due to climate change. However, climate change will be detrimental to Panicum milliaceum.
The suitable habitat area of Panicum milliaceum in the next two periods showed a decrease to the west in Europe, a trend to the north in Africa, and a trend to the south in Asia, North America, South America, and Oceania. Specifically, under the 2070s SSP5-8.5 emission scenario, the area of suitable habitat lost for Panicum milliaceum was the smallest, with an area of 1029.82 × 104 km2 (Figure 11). Under the 2050s SSP2-4.5 emission scenario, the loss area of suitable habitat for Panicum milliaceum was the largest, with an area of 2208.03 × 104 km2 (Figure 11). The loss area of Panicum milliaceum occurred mainly in the Russian Federation, United States, China, Brazil, and Australia (Figure 11). Under the 2050s SSP2-4.5 emissions scenario, the area of suitable habitat for Panicum milliaceum increased the least, by 42.38 × 104 km2 (Figure 11). Under the 2070s SSP5-8.5 emissions scenario, the area of suitable habitat for Panicum milliaceum increased the most, with an area of 305.99 × 104 km2 (Figure 11), and the area of increase in Panicum milliaceum occurred mainly in Australia, Brazil, and Pakistan (Figure 11). In general, there was a significant loss of low-suitable habitats, and some medium- and highly-suitable habitats were converted into unsuitable habitats, which meant that climate change will not only affect the area where Panicum milliaceum could be planted but will also have a greater impact on the suitability of different areas for planting.
The minimal reduction in the potential geographical distribution of Panicum milliaceum at all suitable habitat levels under the SSP5-8.5 emissions scenario may be related to the high temperature-loving and drought-tolerant growth habits of Panicum milliaceum (Plants of China, http://www.iplant.cn (accessed on 20 September 2022)). Prysiazhniuk et al. [36] showed a global decrease in the area planted with Panicum milliaceum, but the impact on the area planted in Ukraine was insignificant. Wei et al. [37] used the MaxEnt model to investigate the potential geographic distribution of Gymnocarpos przewalskii, a light-loving and drought-tolerant plant, in response to future climate change scenarios. It was shown that the potential geographic distribution of Gymnocarpos przewalskii in the desert region of northwest China will be significantly reduced under future climate scenarios. For larch, which also has thermophilic characteristics, Leng et al. [38] analyzed the potential geographic distribution of three larch species in northeast China under three emission scenarios using a random forest model, and the results showed that the potential geographic distribution of the three larch species will be significantly reduced by the end of the 21st century. Li et al. [32] used the MaxEnt model to analyze the changes in the potential geographic distribution of the same widely distributed Salix tetrasperma in Asia under different climate scenarios, and the results showed that the potential geographic distribution is at risk of being lost in the future due to climate change. Yu et al. [39] investigated whether the potential geographic range of the widespread Rhododendron would be reduced under future climate change scenarios. The results showed that, assuming no spontaneous species dispersal, more than half of the study subjects would become threatened species by the 1980s in response to climate change (IUCN Criterion A3). In summary, the conclusions of this study were consistent with the above cases.
Climate change indirectly affects the population composition and distribution of Panicum milliaceum by affecting ecosystems. In addition to the direct effects of climate change on the potential geographic distribution of Panicum milliaceum, irrational human activities (e.g., urban development, hydropower development, and other industrial practices) have also led to a dramatic reduction in the potential distribution area of Panicum milliaceum. Crop rotation practices and human dietary habits can change over time, which could also affect Panicum cultivation, and this will be a major factor influencing potential distributional changes of Panicum milliaceum. Only two time periods, 2050 and 2070, were used for the environmental factor variables in this study. The overall trends in potential geographical distribution over multiple periods could be examined in future research.

4.2. Influence of Environmental Variables on the Potential Geographical Distribution of Panicum milliaceum

The MaxEnt model predicted that the main environmental variables limiting the potential geographic distribution of Panicum milliaceum were the soil factor (hswd), the driest monthly precipitation (bio14), and the average temperature of the wettest season (bio8), of which the soil factor was the most important environmental factor limiting the potential geographic distribution of Panicum milliaceum under all climate scenarios. In numerous studies, soil factors have been demonstrated to limit the potential geographic distribution of Panicum milliaceum. Rasool et al. [40] explored the relationship between the growth of Panicum milliaceum and soil fertility and showed that soil fertility is an important environmental factor limiting the growth of Panicum milliaceum. Luo et al. [41] also carried out a study with findings similar to those simulated in this study, indicating that the soil factor is an important environmental factor for the distribution of Panicum milliaceum. Zhang et al. [42] conducted a study on the suitability of millets for cultivation and found that the growth suitability of Panicum milliaceum was high in the arid region of Northwest China in the terraces conveniently nourished by the driest monthly precipitation, and at the same time, the suitable content of alkaline dissolved nitrogen, effective phosphorus, and organic matter would help Panicum milliaceum to expand its distribution area due to its drought-tolerant and high-temperature-loving growth characteristics.
In this study, we showed that the probability of Panicum milliaceum’s existence showed an increasing and then decreasing trend with the increase in the driest month of precipitation and the average temperature of the wettest season. By exploring the leaf senescence and reactive oxygen metabolism of Panicum milliaceum under different water-saving cultivation modes, Huo et al. [43] found that appropriate precipitation in the arid zone of Northwest China would promote the growth and development of milliaceum and thus achieve a high yield. In order to explore the eco-climatic suitability of Panicum milliaceum cultivation, Pu et al. [44] utilized an integral regression method combined with field meteorological data and took Qingyang, Tianshui, Dingxi, and Wuwei as the study areas in China to investigate what climatic factors govern the yield and distribution of Panicum milliaceum. Their results showed that the precipitation factor (rainfall in the driest month) and the temperature factor (precipitation in the wettest season) impacted the yield and distribution of Panicum milliaceum. The precipitation factor (the driest monthly precipitation) and temperature factor (the average temperature of the wettest season) were the dominant environmental factors constraining the production and distribution of Panicum milliaceum, but the interval of the appropriate values of the dominant environmental factors varied in different areas, which was similar to the simulation results of this study. Qianyu [45] showed that the growth and yield of Panicum milliaceum increased to a certain extent with increasing temperature, and the study by Dong [46] showed that 6–25 °C was the temperature range for the germination of Panicum milliaceum. These physiological findings were similar to the simulated results of the present study, which further verified the accuracy of the results of this study. Dong et al. [9] showed that the development of Panicum milliaceum cultivation on the Loess Plateau after 6000a BP was due to the warm and humid climate. Chen et al. [1] showed that the decline of Panicum milliaceum cultivation after 4000a BP was due to the cold and dry climate. These studies further showed that the temperature factor (the average temperature of the wettest season) is a major constraint on the potential geographic distribution of Panicum milliaceum.
This study predicted the potential geographic distribution of Panicum milliaceum in China and clarified the environmental variables limiting the potential geographic distribution of Panicum milliaceum. Changes in the study area may have caused the range of environmental factors limiting the growth of Panicum milliaceum to change. Other environmental factors, such as data on vegetation cover, also have an effect on the potential geographic distribution of Panicum milliaceum. Since it is not possible to accurately predict the global vegetation cover in the future, this was not included in the prediction of the potential geographic distribution of Panicum milliaceum, so part of the potential geographic distribution area obtained in this study may not be suitable for the survival of Panicum milliaceum, and it will be necessary to take into account local hydrogeological conditions in practical applications. However, the results of this study are important for macro-planning for the rational cultivation of Panicum milliaceum globally.

4.3. Importance of Carrying Out Modeling of the Potential Geographical Distribution of Panicum milliaceum

Global climate change is already a serious threat to agricultural development and directly impacts food security. Few crises will have a greater impact and broader scope than the food crisis [3,47]. Therefore, it is important to develop potential geographic distribution projections of Panicum milliaceum through ecological niche modeling to identify changes in the context of climate change. As a major global perennial crop, Panicum milliaceum plays an important role in maintaining global food security and human life and health. The model results showed that the potential geographical distribution of Panicum milliaceum will be negatively affected in the context of climate change. The results of this study are a first step towards macro-planning for this crop and are crucial for the rational cultivation of Panicum milliaceum. Grassland restoration should be carried out in areas suitable for Panicum milliaceum, and planting should be vigorously promoted. At the same time, according to the actual situation in each planting area, education and training on agricultural pest and disease control should be carried out, quarantine of crops should be strengthened, and timely treatment of diseases and insect damage should be carried out. With scientific and effective control, the effects of climate change could be effectively reduced.

5. Conclusions

The modeling of the potential geographical distribution of Panicum milliaceum under current climatic conditions showed that it is mainly distributed in the United States in North America, Brazil in South America, Australia in Oceania, China and India in Asia, and the Russian Federation in Europe. The potential range of Panicum milliaceum will be significantly reduced under future climate change scenarios, which will be very detrimental to the future cultivation of Panicum milliaceum. In addition to the soil factor (hswd), the most important environmental factors limiting the potential geographical distribution of Panicum milliaceum are the precipitation factor (precipitation of the driest month, bio14) and the temperature factor (mean temperature of the wettest quarter, bio8). In summary, climate change is likely to significantly impact the future distribution patterns of Panicum milliaceum, thereby altering the global production and trade patterns of Panicum milliaceum.

Author Contributions

All authors contributed to the study’s conception and design. P.J. and J.J.: conception, software, methodology, original draft writing, review and editing. C.Y. and X.G.: investigation, data resource management, visualization. Y.H.: supervision, review. L.L.: review, financial support. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the Sichuan Science and Technology Program (Project Numbers: MZGC20230094 (Pan Jiang) and 2023JDR0278 (Liang Liu)), the Sichuan County Economic Development Research Center Project (Project Numbers: xy2023030 (Pan Jiang)), and the Comprehensive Survey of Natural Resources in Haichengwen Coastal Zone (Project Numbers: DD20230414 (Junyi Jiang)).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data and materials can be obtained from the author on reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Chen, F.H.; Dong, G.H.; Zhang, D.J.; Liu, X.Y.; Jia, X.; An, C.B.; Ma, M.M.; Xie, Y.W.; Barton, L.; Ren, X.Y.; et al. Agriculture facilitated permanent human occupation of the Tibetan Plateau after 3600 B.P. Science 2015, 347, 248–250. [Google Scholar] [CrossRef] [PubMed]
  2. Liu, L.; Wang, R.; Zhang, Y.; Mou, Q.; Gou, Y.; Liu, K.; Huang, N.; Ouyang, C.; Hu, J.; Du, B. Simulation of potential suitable distribution of Alnus cremastogyne Burk. In China under climate change scenarios. Ecol. Indic. 2021, 133, 108396. [Google Scholar] [CrossRef]
  3. Yue, Y.; Zhang, P.; Shang, Y. The potential global distribution and dynamics of wheat under multiple climate change scenarios. Sci. Total Environ. 2019, 688, 1308–1318. [Google Scholar] [CrossRef] [PubMed]
  4. Cao, B.; Bai, C.; Zhang, M.; Lu, Y.; Gao, P.; Yang, J.; Xue, Y.; Li, G. Future landscape of renewable fuel resources: Current and future conservation and utilization of main biofuel crops in China. Sci. Total Environ. 2021, 806, 150946. [Google Scholar] [CrossRef]
  5. Liu, L.; Zhang, Y.; Huang, Y.; Zhang, J.; Mou, Q.; Qiu, J.; Wang, R.; Li, Y.; Zhang, D. Simulation of potential suitable distribution of original species of Fritillariae Cirrhosae Bulbus in China under climate change scenarios. Environ. Sci. Pollut. Res. 2021, 29, 22237–22250. [Google Scholar] [CrossRef]
  6. Liu, L.; Guan, L.; Zhao, H.; Huang, Y.; Mou, Q.; Liu, K.; Chen, T.; Wang, X.; Zhang, Y.; Wei, B.; et al. Modeling habitat suitability of Houttuynia cordata Thunb (Ceercao) using MaxEnt under climate change in China. Ecol. Inform. 2021, 63, 101324. [Google Scholar] [CrossRef]
  7. Brooks, T.M.; Mittermeier, R.A.; Da Fonseca, G.A.B.; Gerlach, J.; Hoffmann, M.; Lamoreux, J.F.; Mittermeier, C.G.; Pilgrim, J.D.; Rodrigues, A.S.L. Global Biodiversity Conservation Priorities. Science 2006, 313, 58–61. [Google Scholar] [CrossRef]
  8. Yang, J.; Huang, Y.; Jiang, X.; Chen, H.; Liu, M.; Wang, R. Potential geographical distribution of the edangred plant Isoetes under human activities using MaxEnt and GARP. Glob. Ecol. Conserv. 2022, 38, e02186. [Google Scholar] [CrossRef]
  9. Dong, G.H.; Li, R.; Lu, M.X.; Zhang, D.; James, N. Evolution of human-environmental interactions in China from the Late Paleolithic to the Bronze Age. Prog. Phys. Geogr. 2020, 44, 233–250. [Google Scholar] [CrossRef]
  10. Asseng, S.; Ewert, F.; Martre, P.; Rotter, R.P.; Lobell, D.B.; Cammarano, D.; Kimball, B.A.; Ottman, M.J.; Wall, G.W.; White, J.W.; et al. Rising temperatures reduce global wheat production. Nat. Clim. Chang. 2015, 5, 143–147. [Google Scholar] [CrossRef]
  11. Singh, P. A review paper on climate change, agriculture and food security. Asian J. Res. Soc. Sci. Humanit. 2021, 11, 883–887. [Google Scholar] [CrossRef]
  12. Naylor, R.L. The Bryson synthesis: The forging of climate change narratives during the World Food Crisis. Sci. Context 2023, 34, 375–391. [Google Scholar] [CrossRef] [PubMed]
  13. Elith, J.H.; Graham, C.P.H.; Anderson, R.P.; Dudík, M.; Ferrier, S.; Guisan, A.; Hijmans, R.J.; Huettmann, F.; Leathwick, J.R.; Lehmann, A.; et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 2006, 29, 129–151. [Google Scholar] [CrossRef]
  14. Larson, G.; Piperno, D.R.; Allaby, R.G.; Purugganan, M.D.; Andersson, L.; Arroyo-Kalin, M.; Barton, L.; Vigueira, C.C.; Denham, T.; Dobney, K.; et al. Current perspectives and the future of domestication studies. Proc. Natl. Acad. Sci. USA 2014, 111, 6139–6146. [Google Scholar] [CrossRef]
  15. Yang, X.; Wan, Z.; Perry, L.; Lu, H.; Wang, Q.; Zhao, C.; Li, J.; Xie, F.; Yu, J.; Cui, T.; et al. Early millet use in northern China. Proc. Natl. Acad. Sci. USA 2012, 109, 3726–3730. [Google Scholar] [CrossRef] [PubMed]
  16. Piłat, B.; Ogrodowska, D.; Zadernowski, R. Nutrient content of puffed proso millet (Panicum miliaceum L.) and amaranth (Amaranthus cruentus L.) grains. Czech J. Food Sci. 2016, 34, 362–369. [Google Scholar] [CrossRef]
  17. Li, W.; Xu, Z.; Shi, M.; Chen, J. Prediction of potential geographical distribution patterns of Salix tetrasperma Roxb. in Asia under different climate scenarios. Acta Ecol. Sin. 2019, 39, 3224–3234. [Google Scholar] [CrossRef]
  18. Wang, J.; Yu, Y.; Gao, X.; Jiang, X.; Huang, M.; Ye, H.; Wu, J.; Zhang, J.; Sun, X.; Wu, Q. Succession patterns of aroma components during brewing process of broomcorn millet (Panicum miliaceum L.) Huangjiu. Food Res. Int. 2022, 154, 110982. [Google Scholar] [CrossRef]
  19. Zhang, D.-Z.; Panhwar, R.B.; Liu, J.-J.; Gong, X.-W.; Liang, J.-B.; Liu, M.; Lu, P.; Gao, X.-L.; Feng, B.-L. Morphological diversity and correlation analysis of phenotypes and quality traits of proso millet (Panicum miliaceum L.) core collections. J. Integr. Agric. 2019, 18, 958–969. [Google Scholar] [CrossRef]
  20. Mani, V.; Upadhyaya, H.D. Diversity and trait-specific sources for productivity and nutritional traits in the global proso millet (Panicum miliaceum L.) germplasm collection. Crop J. 2018, 6, 451–463. [Google Scholar]
  21. Liu, C.; Yuan, Y.; Liu, J.; Wang, H.; Ma, Q.; Zhou, Y.; Liu, C.; Gong, X.; Feng, B. Comparative transcriptome and physiological analysis unravel proso millet (Panicum miliaceum L.) source leaf adaptation to nitrogen deficiency with high nitrogen use efficiency. Environ. Exp. Bot. 2022, 199, 104891. [Google Scholar] [CrossRef]
  22. Santana, P.A.J.; Kumar, L.; Da Silva, R.S.; Pereira, J.L.; Picanço, M.C. Assessing the impact of climate change on the worldwide distribution of Dalbulus maidis (DeLong) using MaxEnt. Pest Manag. Sci. 2019, 75, 2706–2715. [Google Scholar] [CrossRef] [PubMed]
  23. Yu, X.; Tao, X.; Liao, J.; Liu, S.; Xu, L.; Yuan, S.; Zhang, Z.; Wang, F.; Deng, N.; Huang, J.; et al. Predicting potential cultivation region and paddy area for ratoon rice production in China using Maxent model. Field Crop. Res. 2021, 275, 108372. [Google Scholar] [CrossRef]
  24. Huang, Y.; Zeng, Y.; Jiang, P.; Chen, H.; Yang, J. Prediction of Potential Geographic Distribution of Endangered Relict Tree Species Dipteronia sinensis in China Based on MaxEnt and GIS. Pol. J. Environ. Stud. 2022, 31, 3597–3609. [Google Scholar] [CrossRef]
  25. Liao, J.; Yang, C.; Shao, Q.; Sun, Q.; Han, Y. Construction of an ecological model of Sambucus javanica blume in China under different climate scenarios based on maxent model. Plant Ecol. 2023, 224, 221–237. [Google Scholar] [CrossRef]
  26. Wang, R.; Jiang, C.; Guo, X.; Chen, D.; You, C.; Zhang, Y.; Wang, M.; Li, Q. Potential distribution of Spodoptera frugiperda (J.E. Smith) in China and the major factors influencing distribution. Glob. Ecol. Conserv. 2019, 21, e00865. [Google Scholar] [CrossRef]
  27. Peng, G.; He, S.; Zhao, Q.; Li, G.; Zhang, X.; Guo, M.; Wang, S.; Niu, J.; Wang, Z. Prediction of potentially suitable distribution areas of Thesium chinense Turcz. in China against the background of climate change. Plant Ecol. 2023, 258, 13–23. [Google Scholar]
  28. Xian, X.; Zhao, H.; Wang, R.; Huang, H.; Chen, B.; Zhang, G.; Liu, W.; Wan, F. Climate change has increased the global threats posed by three ragweeds (Ambrosia L.) in the Anthropocene. Sci. Total Environ. 2023, 859, 160252. [Google Scholar] [CrossRef]
  29. Zhao, H.; Xian, X.; Yang, N.; Zhang, Y.; Liu, H.; Wan, F.; Guo, J.; Liu, W. Insights from the biogeographic approach for biocontrol of invasive alien pests: Estimating the ecological niche overlap of three egg parasitoids against Spodoptera frugiperda in China. Sci. Total Environ. 2023, 862, 160785. [Google Scholar] [CrossRef]
  30. Wang, R.; Yang, H.; Luo, W.; Wang, M.; Lu, X.; Huang, T.; Zhao, J.; Li, Q. Predicting the potential distribution of the Asian citrus psyllid, Diaphorina citri (Kuwayama), in China using the MaxEnt model. PeerJ 2019, 7, e7323. [Google Scholar] [CrossRef]
  31. Yang, J.-T.; Jiang, X.; Chen, H.; Jiang, P.; Liu, M.; Huang, Y. Predicting the Potential Distribution of the Endangered Plant Magnolia wilsonii Using MaxEnt under Climate Change in China. Pol. J. Environ. Stud. 2022, 31, 4435–4445. [Google Scholar] [CrossRef]
  32. Li, J.; Fan, G.; He, Y. Predicting the current and future distribution of three Coptis herbs in China under climate change conditions, using the MaxEnt model and chemical analysis. Sci. Total Environ. 2019, 698, 134141. [Google Scholar] [CrossRef] [PubMed]
  33. Eyring, V.; Bony, S.; Meehl, G.A.; Senior, C.A.; Stevens, B.; Stouffer, R.J.; Taylor, K.E. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 2016, 9, 1937–1958. [Google Scholar] [CrossRef]
  34. Lan, R.; Chen, J.; Pan, J.; Chen, R.; Lin, H.; Li, Z.; Xue, Q.; Liu, C.; Huang, Y. Simulation of Potential Suitable Distribution of Endangered Medicinal of Paeonia rockii under Climate Change Scenarios. Pol. J. Environ. Stud. 2023, 32, 2181–2197. [Google Scholar] [CrossRef]
  35. Thomas, C.D.; Cameron, A.; Green, R.E.; Bakkenes, M.; Beaumont, L.J.; Collingham, Y.C.; Erasmus, B.F.N.; de Siqueira, M.F.; Grainger, A.; Hannah, L.; et al. Extinction risk from climate change. Nature 2004, 427, 145–148. [Google Scholar] [CrossRef]
  36. Prysiazhniuk, L.M.; Nochvina, O.V.; Shytikova, Y.V.; Mizerna, N.A.; Hryniv, S.M. Ecological plasticity and stability of common millet (Panicum miliaceum L.) productivity in different environmental conditions of Ukraine. Plant Var. Stud. Prot. 2021, 17, 146–154. [Google Scholar] [CrossRef]
  37. Wei, B.; Sun, F.F.; Ma, X.; Huang, T.T.; Ma, S.M. Songmei Possible response of the suitable distribution areas of endangered desert Plant Gymnocarpos przewalskii to future climate change scenario. J. Shihezi Univ. (Nat. Sci.) 2019, 37, 490–497. [Google Scholar]
  38. Leng, W.; He, H.S.; Bu, R.; Dai, L.; Hu, Y.; Wang, X. Predicting the distributions of suitable habitat for three larch species under climate warming in Northeastern China. For. Ecol. Manag. 2008, 254, 420–428. [Google Scholar] [CrossRef]
  39. Yu, F.; Wang, T.; Groen, T.A.; Skidmore, A.K.; Yang, X.; Ma, K.; Wu, Z. Climate and land use changes will degrade the distribution of Rhododendrons in China. Sci. Total Environ. 2019, 659, 515–528. [Google Scholar] [CrossRef]
  40. Rasool, A.; Hafiz, S.W.; Padder, S.A. Exogenous selenium treatment alleviates salinity stress in Proso Millet (Panicum miliaceum L.) by enhancing the antioxidant defence system and regulation of ionic channels. Plant Growth Regul. 2022, 100, 3. [Google Scholar] [CrossRef]
  41. Luo, Y.; Liu, C.; Dang, K.; Gong, X.; Feng, B. Cultivar sensitivity of broomcorn millet (Panicum miliaceum L.) to nitrogen availability is associated with differences in photosynthetic physiology and nitrogen uptake. Plant Physiol. Biochem. 2022, 182, 90–103. [Google Scholar] [CrossRef] [PubMed]
  42. Zhang, Y.; Chang, Q.R.; Zhao, Y.T.; Sun, L.P. Suitability analysis of farmland for corn millet growing in Fugu County. Agric. Res. Arid. Areas 2015, 33, 187–193. [Google Scholar]
  43. Huo, X.J. Leaf Senescence and Reactive Oxygen Metabolism of Different Water-Saving Cultivation Mode in Millet. Northwest University of Agriculture and Forestry Technology. 2014. Available online: https://kns.cnki.net/KCMS/detail/detail.aspx?dbname=CMFD201501&filename=1014429959.nh (accessed on 20 March 2023).
  44. Pu, J.Y.; Yao, X.Y.; Xin, C.Y.; Yuan, Y.P. A Study on eco-climate suitability of broomcorn millet (Panicum Miliaceum L.) in Gansu. Agric. Res. Arid. Areas 2010, 28, 223–226. [Google Scholar]
  45. Zhang, Q.Y. Effect of Elevated CO2 on Growth and Yield of Broomcorn Millet under Different Water Conditions. Shanxi Agricultural University. 2017. Available online: https://kns.cnki.net/KCMS/detail/detail.aspx?dbname=CMFD201901&filename=1018050440.nh (accessed on 20 March 2022).
  46. Dong, Y. Construction of a comprehensive evaluation system for cold tolerance germplasm resources of panicum miliaceum during the sprouting stage. JiangSu Agric. Sci. 2022, 50, 82–89. [Google Scholar] [CrossRef]
  47. Huan, X.; Wei, X.; Zhang, J.; Li, J.; Zhang, X.; Shao, K.; Ge, Y.; Yang, X.; Lu, H. Discovery of the Earliest Rice Paddy in the Mixed Rice–Millet Farming Area of China. Land 2022, 11, 831. [Google Scholar] [CrossRef]
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Figure 1. Photographs of various products of Panicum milliaceum: (a) unprocessed; (b) after shelling; (c) processed into food; (d) processed into saleable products.
Figure 1. Photographs of various products of Panicum milliaceum: (a) unprocessed; (b) after shelling; (c) processed into food; (d) processed into saleable products.
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Figure 2. Flowchart displaying the steps of the present study.
Figure 2. Flowchart displaying the steps of the present study.
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Figure 3. Global occurrence records of Panicum milliaceum.
Figure 3. Global occurrence records of Panicum milliaceum.
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Figure 4. Heat map for correlation analysis of environmental variables.
Figure 4. Heat map for correlation analysis of environmental variables.
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Figure 5. AUC, Kappa, and TSS values of MaxEnt.
Figure 5. AUC, Kappa, and TSS values of MaxEnt.
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Figure 6. Suitable areas for Panicum milliaceum under different climate change scenarios.
Figure 6. Suitable areas for Panicum milliaceum under different climate change scenarios.
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Figure 7. Current global geographical distributions of Panicum milliaceum.
Figure 7. Current global geographical distributions of Panicum milliaceum.
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Figure 8. The potential global geographic distribution of Panicum milliaceum in the 1950s and 1970s was predicted using MaxEnt for SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5.
Figure 8. The potential global geographic distribution of Panicum milliaceum in the 1950s and 1970s was predicted using MaxEnt for SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5.
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Figure 9. Environmental variables and their contributions of Panicum milliaceum in (a) 2050s, SSP1-2.6; (b) 2050s, SSP2-4.5g; (c) 2050s, SSP3-7.0; (d) 2050s, SSP5-8.5; (e) 2070s, SSP1-2.6; (f) 2070s, SSP2-4.5g; (g) 2070s, SSP3-7.0; (h) 2070s, SSP5-8.5; (i) current.
Figure 9. Environmental variables and their contributions of Panicum milliaceum in (a) 2050s, SSP1-2.6; (b) 2050s, SSP2-4.5g; (c) 2050s, SSP3-7.0; (d) 2050s, SSP5-8.5; (e) 2070s, SSP1-2.6; (f) 2070s, SSP2-4.5g; (g) 2070s, SSP3-7.0; (h) 2070s, SSP5-8.5; (i) current.
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Figure 10. Response curves of the existence probability of Panicum milliaceum. The red line is the trend line and the blue is the environmental variables.
Figure 10. Response curves of the existence probability of Panicum milliaceum. The red line is the trend line and the blue is the environmental variables.
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Figure 11. Changes in the potential geographical distribution of Panicum milliaceum under climate change scenarios in the future.
Figure 11. Changes in the potential geographical distribution of Panicum milliaceum under climate change scenarios in the future.
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Table 1. Variables used in the model.
Table 1. Variables used in the model.
VariablesDescriptionSignificance of the Indicators
hswdOverall data on soil worldwideReflects the overall effect of soil factors
bio2Monthly average of the diurnal temperature differenceReflecting the characteristics of temperature differences
bio8Average temperature of the wettest quarterReflects whether water and heat are synchronized
bio13Wettest monthly precipitationReflects extreme moisture conditions
bio14Driest monthly precipitationReflects extreme moisture conditions
bio15Coefficient of variation in precipitationReflects rainfall and seasonal distribution
bio18Warmest quarter precipitationReflects whether water and heat are synchronized
bio19Coldest quarterly precipitationReflects whether water and heat are synchronized
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Jiang, P.; Jiang, J.; Yang, C.; Gu, X.; Huang, Y.; Liu, L. Climate Change Will Lead to a Significant Reduction in the Global Cultivation of Panicum milliaceum. Atmosphere 2023, 14, 1297. https://0-doi-org.brum.beds.ac.uk/10.3390/atmos14081297

AMA Style

Jiang P, Jiang J, Yang C, Gu X, Huang Y, Liu L. Climate Change Will Lead to a Significant Reduction in the Global Cultivation of Panicum milliaceum. Atmosphere. 2023; 14(8):1297. https://0-doi-org.brum.beds.ac.uk/10.3390/atmos14081297

Chicago/Turabian Style

Jiang, Pan, Junyi Jiang, Cong Yang, Xinchen Gu, Yi Huang, and Liang Liu. 2023. "Climate Change Will Lead to a Significant Reduction in the Global Cultivation of Panicum milliaceum" Atmosphere 14, no. 8: 1297. https://0-doi-org.brum.beds.ac.uk/10.3390/atmos14081297

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