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Communication

Progress in Studies of Climatic Suitability of Crop Quality and Resistance Mechanisms in the Context of Climate Warming

by
Qijin He
1,2,
Guangsheng Zhou
2,3,4,* and
Jiahong Liu
1
1
College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China
2
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing 210044, China
3
State Key Laboratory of Severe Weather, Hebei Gucheng Agricultural Meteorology National Observation and Research Station, Chinese Academy of Meteorological Sciences, Beijing 100081, China
4
Joint Eco-Meteorological Laboratory of Chinese Academy of Meteorological Sciences and Zhengzhou University, Zhengzhou 450001, China
*
Author to whom correspondence should be addressed.
Agronomy 2022, 12(12), 3183; https://doi.org/10.3390/agronomy12123183
Submission received: 10 November 2022 / Revised: 7 December 2022 / Accepted: 13 December 2022 / Published: 15 December 2022
Versions Notes

Abstract

:
Crop quality is directly related to national food security and people’s living standards, and it is also key to the improvement of agricultural quality and efficiency. According to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC), climate change has already exerted a negative impact on crop quality. To address climate change scientifically, this paper reviews the latest progress in studies on the impact of climate change on crop quality, and points out limitations of existing studies: (1) Climate factors affecting crop quality are not yet clearly identified; (2) The climate change influencing mechanism and disaster-inducing critical meteorological conditions for crop quality are not clearly established; and (3) No climatic suitability model for crop quality has been constructed to reflect the synergy of multiple climate factors. To ensure food quality and security, and to adjust and optimize the industrial planning of grain crops, promote a high crop quality and yield, and address climate change scientifically, this paper argues that subsequent studies should discuss the following topics, focusing on the climatic suitability of crop quality and resistance mechanisms: (1) changing laws involving the climate sensitivity of crop quality in the context of climate change; (2) response mechanisms of crop quality to climate change; (3) crop quality monitoring and assessment modelling; (4) climatic suitability zoning of crop quality; (5) spatiotemporal evolution trends of crop quality and its responses; and (6) crop quality and its legal measures in the world.

1. Introduction

As major components of food security, crop yield and crop quality are of vital importance to social stability and sustainable development. With the socioeconomic development of humans and continuous improvement to people’s living standards, crop quality has become a topic of concern. Crop quality refers to the quality of crop products with respect to their nutritional value, edibility, processing, appearance (commodity quality), and technological aspects. Global warming is an indisputable fact; it not only has a severe impact on agroecosystems and crop yields [1,2,3,4,5,6,7,8,9,10], but also has a direct impact on crop quality [11]. The Fifth Assessment Report of the IPCC states that climate change has negative impacts on crop quality, specifically changing carbon content and nutrient intake and generating secondary products in biochemical processes. For example, increased CO2 concentrations reduce the protein content of cereals (e.g., by 10 to 14% for wheat and rice and by 15% for soybeans) and also reduce the mineral content of cereals [12]. If efforts in climate change adaptation are not accelerated, the number of malnourished children aged 5 or below will increase by 20 to 25 million (17 to 22%) by 2050 [13]. Although great progress has been made in terms of studies on the impacts of climate change on agricultural production and food security since the Fourth Assessment Report of the IPCC, such studies mainly focus on agricultural production factors, whereas few studies have examined the impact of climate change (particularly extreme weather climate events) on crop quality, the mechanisms of such impact, or related thresholds [13].
Crop quality is determined by genetic and non-genetic factors of crops. Genetic factors refer to the genetic modes that determine the characteristics of crop varieties, and non-genetic factors refer to all factors other than genetic factors, including environmental conditions, cultivation practices, and mineral nutrients. Hence, crop quality has always been the core issue in studies of crop cultivation and genetic breeding. Studies regarding the effects of non-genetic factors on crop quality were commenced relatively recently. Until the 1990s, such studies focused mainly on the effects of cultivation and fertilization on crop quality but scarcely examined the relationship between crop quality and climate. On a certain genetic basis, environmental factors (particularly climatic factors) are crucial to the generation of crop quality. Since global climate change studies emerged in the 1990s, the effects of climatic factors (e.g., light, heat, precipitation, and CO2 content) on crop quality have received increasing attention in academia [14,15]. In China, food and clothing have been a long-standing problem, so a high yield is the first consideration in the selection of crop varieties; however, studies of crop quality traits were started late, the understanding of their formation mechanisms was very limited, and no tool-kit was available to support molecular design, thus restricting the breeding of high-quality crops by genetic manipulation [16]. Existing studies on the impact of climate change on crop quality are mainly focused on the effect of climate change and sowing time adjustment on crop quality as well as crop quality models.

2. Effects of Climate Change on Crop Quality

Most existing studies of the effects of climate change on crop quality focus on the effect of factors such as radiation, temperature, and precipitation. Studies show that corn genotypes and environmental factors are the main determinants of crop grain quality [17], and in particular, climatic factors such as light, temperature, and water have a significant effect on corn quality [18]. For example, the phytotron was used to simulate the effects of a high CO2 concentration (700 ppm) accompanied by a high temperature, and the synergy of CO2 concentration (350 ppm and 700 ppm) and moisture (relative soil moisture is, respectively, equal to 70 to 80% and 30 to 40% of the field water holding capacity), on the content of amino acids, crude protein, and crude starch in winter wheat and corn, as well as the content of amino acids, crude protein, and crude fat in soybeans [19]. Their study results showed that soil water stress serves to improve crop grain quality and that a high CO2 concentration accompanied by a high temperature is not only detrimental to the improvement of crop grain quality but also inhibits the improvement of crop grain quality under drought conditions. Normally, high atmospheric CO2 levels have a fertilizing effect on crop production (i.e., crops are able to absorb more CO2 during photosynthesis), but the other resulting climate change factors (e.g., temperature rise and precipitation reduction) reduce crop quality [20,21]. Today, climatic factors affecting crop quality are non-uniform, including temperature differences [22], accumulated temperature, and precipitation [23]. Diurnal- or monthly-scale climatic factors affect the protein content of winter wheat grains [24,25,26,27]. High temperature and radiation during the grain-filling period improve the protein content of wheat [26,28,29]. However, the effect of precipitation is uncertain, or more specifically, increased precipitation has either a promotive effect or a negative effect on the protein content of wheat [26,29]. Climate warming has significantly changed the quality indices (e.g., content of protein, nutrients, and non-nutrient elements) of wheat grains in northwest China; specifically, a 1 °C increase in the average temperature during the reproductive period brings about a 1.6% decrease in the starch content of spring wheat but a 0.8% increase in its protein content [7]. The relationship between wheat quality and related factors (e.g., meteorology, soil, and pest and disease types) in Zhumadian City, Henan Province from 2010 to 2019 indicated that moderate drought during the reproductive period can improve wheat grain quality, soil nutrient changes have no significant effect on grain quality, pest and disease control can improve wheat quality significantly, and precipitation in May has a significant effect on wheat quality [30,31]. Regarding the spatial heterogeneity of climatic elements, the effects of temperature, precipitation, and radiation on the protein content of winter wheat in the Huang-Huai-Hai region of China showed that the prediction of the protein content of winter wheat on a county-wide scale should consider spatial heterogeneity; specifically, a 1° increase in latitude brings about a 0.29% increase in protein content [32]. Studies of the predicted effect of future climate change on crop quality show that temperature rise and precipitation reduction in the coming century will improve harvest conditions for two types of corn (early and late sown corn) in France, and promote the improvement of corn grain quality, particularly for late-sown corn in northern France [33]. The quality grade of Italian grapes is expected to leap from standard-quality grapes (SQGs) to super-quality grapes (UQGs) by 2051 [34].

3. Effect of Sowing Time Adjustment on Crop Quality

The adjustment of the sowing time directly affects the climatic conditions during the reproductive period, thus affecting the quality of crops. The sowing time and varietal tests showed that the sowing time has a significant effect on corn grain quality (e.g., content of crude protein, crude starch, and soluble protein) [35] and starch physicochemical properties [36] and that the sowing time and corn variety have a significant effect on the relative content of protein, crude cellulose, starch, and lysine in waxy corn grains [23]. Advancing the sowing time serves to increase the relative content of protein and lysine, whereas deferring the sowing time serves to reduce the relative content of crude fiber and lysine; however, a moderate delay in sowing time serves to increase the relative content of starch in crop grains [23]. In the reproductive period, the conditions of light, temperature, and moisture at different stages affect the quality of waxy corn grains [37]. An appropriate sowing time can achieve optimal yield traits and quality of crops by coordinating various climatic factors [38]. Under the same sowing time, the quality of Zheng Huangnuo No. 2 corn varies significantly at different harvesting times; if the harvesting time is deferred, the crude starch content and residue rate increase, whereas the soluble sugar content decreases [39]. The crystal structure and pasting properties of waxy corn starch differ significantly during the growing season [40]. Due to significant differences in environmental conditions during the growth and development of corn at different sowing times and differences in crop varieties and their growing regions, conclusions surrounding the effect of sowing time on crop quality are inconsistent [39,40].

4. Crop Quality Model

Existing crop quality models can be mainly classified into statistical models, crop growth models, and remote-sensing monitoring models. Statistical models are mathematical models that are based on one method or a combination of methods (e.g., regression analysis, principal component analysis (PCA), discriminant analysis, and variance analysis). Statistical models include a model for the relationship between apple quality and climate based on PCA and hierarchical cluster analysis [41], a model for the relationship between the protein content of spring wheat grains and the evapotranspiration deficit index (ETDI) [42], a multiple regression model for the relationship between winter wheat quality and climatic factors [43], and a winter wheat quality assessment model based on a linear mixed model, Shukla model, and AMMI model [44]. Statistical models are usually used to ascertain the effects of climatic factors on crop quality [26,27,45,46]. Statistical models (e.g., the Just–Pope production function model, spatial lag effect model, multiple linear regression model, and hierarchical linear model) have proved to be able to accurately predict the protein content of wheat [26,27,47,48]. Compared with multiple linear regression models, geographically weighted regression (GWR) models can simulate and predict the effect of the spatial heterogeneity of climatic factors on the protein content of winter wheat more accurately [32].
Based on the intrinsic law of crop growth and development, as well as the causality between crop genetic potential, environmental effects, and regulatory technologies, crop growth models can quantitatively describe and predict the crop growth and development process and its dynamic relationships with the environment and technologies. Marked by a model for plant canopy light interception and canopy photosynthesis published between the 1950s and 1960s [49,50], a series of crop physiological and ecological process simulation models were developed, including the US DSSAT model [51], Australia’s APSIM model [52], France’s STICS model [53], Holland’s GECROS model [54], the ORYZA model developed by the Philippines’ International Rice Research Institute [55], and the CropGrow model developed by Nanjing Agricultural University, China [56]. Due to various reasons (e.g., poor sharing of experimental data, limited control of experimental conditions, underdevelopment of regional simulation techniques, and lack of model researchers), existing crop growth models are yet to be improved and refined from multiple different aspects, including extreme climate effect simulation [57,58], regional productivity prediction [59], management plan design [60], and environmental effect assessment [61,62]. Hence, it is a matter of urgency to develop a comprehensive crop growth model and decision support system with a clear mechanism and high predictive ability [56]. The structural complexity of crop growth models and difficulty in the acquisition of required parameter data restrict the prediction of crop quality [63,64,65,66].
Using spectroscopy-based non-destructive crop quality monitoring technology, crop quality remote-sensing monitoring models can probe the earth surface in real time, on a large scale and non-destructively, and attain a conversion from point information to planar information, thus providing timely information support for agricultural production management and decision-making. With the development of non-destructive testing technology and aerospace technology, the spectral resolution and spatial resolution abilities of sensors have rapidly improved, making it possible to use remote-sensing information to reversely deduce the content of biochemical components in crops, monitor environmental impact factors in the process of quality formation, and then monitor the quality of crop grains. Depending on the platforms of remote-sensing data acquisition, crop quality remote-sensing monitoring models can be classified into ground-based crop quality spectral models [67,68,69] and aerospace-based crop quality remote-sensing monitoring models [70,71,72]. Although crop quality remote-sensing monitoring models overcome certain deficiencies (e.g., poor representativeness, time-consuming characterization, and high cost) of traditional laboratory test sampling methods, they are susceptible to crop varieties, climate change, and soil due to the formation mechanisms, resulting in large simulation errors.

5. Study Prospects

Agricultural production and crop quality are highly responsive and sensitive to climate change. Crop quality is the most important component of food security. At present, lots of measures have been implemented to stop the effects of climate change on crop in the agricultural ecosystem, including farm management, variety/crop change, and change of the main planting time; however, these measures mainly focus on crop production rather than quality. Both agricultural production and crop quality are affected by climate change; whilst there are numerous studies on the effects of climate change on agricultural production, there are very few studies on how specific environmental factors affect crop quality and how such effects are assessed. The decline in crop quality makes rural children in low-income countries particularly vulnerable to malnutrition [13]. Furthermore, climate warming has changed grain production regions [73,74]. As a result, crop quality might be changed in the context of global warming.
Early studies in China mainly focused on the effect of climatic conditions on crop yield and the climatic suitability of crops, whereas only a small number examined how climate affects crop quality and how such effects are assessed. Notwithstanding many studies on remote-sensing monitoring of crop quality, few studies have focused on crop quality assessment based on crop growth models. Since 2012, Chinese scientists have been actively exploring climate quality certification [75,76,77]. In 2012, Zhejiang Meteorological Bureau took the lead in exploring climate quality certification for crops in China and successively developed climate quality certification technologies and assessment models for crops such as tea, waxberry, grapevine, citrus, pears, and paddy rice [78]. Because of a limited understanding of the climatic regulatory mechanisms that affect crop quality, such certification technologies and assessment models cannot authentically reflect the quality of crops, making it difficult to provide effective guidance for high-quality and efficient crop production and climate change responses in China. This is mainly due to three reasons. First, climatic factors affecting crop quality remain unknown. Usually, existing studies seek to identify such climatic factors based on a simple correlation between the climate and the quality of specific varieties of crops obtained from specific regions, in specific climatic periods and at specific sowing times. Regarding the climatic factors affecting crop quality, conclusions vary from study to study, making it difficult to conduct a horizontal comparison of crop quality assessment results. Because of the subjectivity of indicator selection, the accuracy of assessment results has been questioned. The findings of such studies are not universally applicable. Hence, it is a matter of urgency to examine climatic factors that determine the grain-filling duration and rate, and the photosynthetic rate of corn, in order to identify climatic factors affecting crop quality. Second, the mechanisms of how crop quality is affected by climate change and meteorological disasters remain unknown. Global warming has increased the frequency and intensity of extreme climatic events (e.g., rainstorms, floods, drought, high-temperature heat waves, hailstorms, and strong typhoons) with increasing degrees of damage. Crop yield was reduced by 9 to 10% due to global drought and extreme high temperatures from 1964 to 2007 [79]. Existing studies mainly focus on specific regions and climate stages, so they can hardly reveal the climatic impacts outside of the studied areas or explain the impact of meteorological disasters (e.g., droughts). Although great progress has been made in studies on the impact of climate change on agricultural production and food security since the release of the Fourth Assessment Report of the IPCC, the impact of climate change (in particular, extreme weather/climate events) on crop quality and the influencing mechanism and related thresholds remain unknown [13]. Metabolism is the basis for the formation of quality traits [16]. It is a matter of urgency to reveal the resistance mechanisms of crop quality to climate change and extreme weather/climate events from the perspective of physiological photosynthesis. Third, no climatic suitability models for crop quality involving the synergy of all climatic factors have been developed to provide decision-making support for optimizing and adjusting industrial layout, facilitating a high quality and high yield of crops, and scientifically tackling climate change.
Crop quality is directly related to national food security and people’s living standards, and it is also a key to the improvement of agricultural quality and efficiency. To address climate change and the consequent frequent occurrences of extreme weather/climate events, subsequent studies will focus on the impact of climate change on the nutritional components (e.g., protein, starch, fat, crude fiber, amino acids, and fatty acids) of grains widely grown in China, as well as adaptive measures. Focusing on the climatic suitability of crop quality and resistance mechanisms, subsequent studies will investigate all climatic factors that determine the grain-filling duration, filling rate, and photosynthetic rate of crops, and discuss the following topics based on related data (e.g., long-term positional observation data by agricultural meteorological experiment stations, experimental data of staged crop sowing, and simulation data of crop quality responses to extreme weather/climate events): (1) Effects of climate change on crop quality and the climate sensitivity of crop quality, especially a study detecting climate-influencing factors of crop quality; (2) response mechanisms of crop quality to climate change, focusing on the resistance mechanism of crop quality to extreme weather/climate events and disaster-inducing critical meteorological conditions; (3) crop quality monitoring and assessment models, focusing not only on the role of remote-sensing information in crop quality monitoring but also on the construction of crop quality monitoring and assessment models based on climatic factors (particularly the construction of climate suitability models for crop quality with the synergy of all climate factors); (4) climatic suitability zoning of crop quality, focusing on the climatic requirements of different crop qualities; (5) spatiotemporal evolution trends of crop quality and responses in the context of climate change, focusing on sowing time adjustment and technical measures of crop quality in response to climate change; and (6) crop quality and its legal measures in the world, especially the position of crop quality in international grain production and trade, and relevant legal measures to coordinate grain production and quality, in order to ensure food security.

Author Contributions

Conceptualization, G.Z.; writing—original draft preparation, Q.H.; writing—review and editing, J.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (42130514) and the Special Program for Innovation and Development of China Meteorological Administration (CXFZ2022J051).

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Darwin, R. The impact of global warming on agriculture: A Ricardian analysis: Comment. Am. Econ. Rev. 1999, 89, 1049–1052. [Google Scholar] [CrossRef]
  2. Cantelaube, P.; Terres, J.M.; Doblas-Reyes, F.J. Influence of climate variability on European agriculture—Analysis of winter wheat production. Clim. Res. 2004, 27, 135–144. [Google Scholar] [CrossRef]
  3. Tubiello, F.N.; Soussana, J.F.; Howden, S.M. Crop and pasture response to climate change. Proc. Natl. Acad. Sci. USA 2007, 104, 19686–19690. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Battisti, D.S.; Naylor, R.L. Historical warnings of future food insecurity with unprecedented seasonal heat. Science 2009, 323, 240–244. [Google Scholar] [CrossRef] [Green Version]
  5. Piao, S.L.; Ciais, P.; Huang, Y.; Shen, Z.H.; Peng, S.S.; Li, J.S.; Zhou, L.P.; Liu, H.Y.; Ma, Y.C.; Ding, Y.H.; et al. The impacts of climate change on water resources and agriculture in China. Nature 2010, 467, 43–51. [Google Scholar] [CrossRef]
  6. Qin, Z.H.; Tang, H.J.; Li, W.J.; Zhao, S.H. Progress and directions in studying the impacts of climate change on agriculture and grain production in China. Chin. J. Agric. Resour. Reg. Plann. 2013, 34, 1–7, (In Chinese with English abstract). [Google Scholar]
  7. Wang, H.L.; Zhang, Q.; Wang, R.Y.; Gan, Y.T.; Niu, J.Y.; Zhang, K.; Zhao, F.N.; Zhao, H. Effects of air temperature increase and precipitation change on grain yield and quality of spring wheat in semiarid area of Northwest China. Chin. J. Appl. Ecol. 2015, 26, 67–75, (In Chinese with English abstract). [Google Scholar]
  8. Chen, K.Z.; Hsu, C. Managing climate change risk in China’s agricultural sector: The potential for an integrated risk management framework. J. Integr. Agric. 2014, 13, 1418–1431. [Google Scholar] [CrossRef]
  9. Cui, M.M.; Nie, C.H. Study on food security in China based on evaluation index system. Bull. Chin. Acad. Sci. 2019, 34, 910–919, (In Chinese with English abstract). [Google Scholar]
  10. Chou, J.M.; Dong, W.J.; Xu, H.; Tu, G. New ideas for research on the impact of climate change on China’s food security. Clim. Environ. Res. 2022, 27, 206–216, (In Chinese with English abstract). [Google Scholar]
  11. Chen, J.; Liu, Y.J.; Zhou, W.M.; Zhang, J.; Pan, T. Effects of climate change and crop management on changes in rice phenology in China from 1981 to 2010. J. Sci. Food Agric. 2021, 101, 6311–6319. [Google Scholar] [CrossRef] [PubMed]
  12. Xie, L.Y.; Li, Y.; Xu, Y.X.; Zhao, X.; Song, Y.L.; Jiang, T.; Lin, E.D. Updated understanding on the impacts of climate change on food production and food security. Clim. Chang. Res. 2014, 10, 235–239, (In Chinese with English abstract). [Google Scholar]
  13. IPCC. Climate Change 2014: Impacts, Adaptation, and Vulnerability; Cambridge University Press: Cambridge, UK, 2014. [Google Scholar]
  14. Wu, D.X.; Wang, G.X.; Bai, Y.F.; Liao, J.X. Effects of elevated CO2 concentration on growth, water use, yield and grain quality of wheat under two soil water levels. Agric. Ecosyst. Environ. 2004, 104, 493–507. [Google Scholar] [CrossRef]
  15. Irigoyen, J.J.; Goicoechea, N.; Antolin, M.C.; Pascual, I.; Sanchez-Diaz, M.; Aguirreolea, J.; Morales, F. Growth, photosynthetic acclimation and yield quality in legumes under climate change simulations: An updated survey. Plant Sci. 2014, 226, 22–29. [Google Scholar] [CrossRef] [PubMed]
  16. Deng, Z.Y.; Qu, L.Q.; Wu, Y.R.; Zhang, J.S.; Wang, T. Current progress and prospect of crop quality research. Sci. Sin. 2021, 51, 1405–1414, (In Chinese with English abstract). [Google Scholar] [CrossRef]
  17. Kong, L.P.; Zhang, H.Y.; Zhao, Y.M. Effects of sowing date and planting density on starch RVA properties and kernel quality of two maize varieties. J. Maize Sci. 2014, 22, 98–102, 108, (In Chinese with English abstract). [Google Scholar]
  18. Zhang, S.; Zhao, L.M.; Yi, C.F.; Gao, B.D. Effect of sowing time on seed and nutrient quality formation of spring maize. J. Inner. Mongolia. Agric. Univ. 2000, 21, 26–29, (In Chinese with English abstract). [Google Scholar]
  19. Guo, J.P.; Gao, S.H.; Liu, L. An experimental study of the impacts of meteorological condition on crops qualities and yield. Clim. Environ. Res. 2001, 6, 361–367, (In Chinese with English abstract). [Google Scholar]
  20. Moya, T.B.; Ziska, L.H.; Weldon, C.; Quilang, J.E.P.; Jones, P. Microclimate in open-top chambers: Implications for predicting climate change effects on rice production. Trans. ASAE 1997, 40, 739–747. [Google Scholar] [CrossRef]
  21. Lin, E.D.; Xiong, W.; Ju, H.; Xu, Y.L.; Li, Y.; Bai, L.P.; Xie, L.Y. Climate change impacts on crop yield and quality with CO2 fertilization in China. Philos. Trans. Biol. Sci. 2005, 360, 2149–2154. [Google Scholar]
  22. Liu, S.Y.; Dong, S.T.; Hu, C.H. The study of relationship between ecological environment and maize enzyme activity. Acta. Agric. Boreali-Sin. 2004, 4, 62–65, (In Chinese with English abstract). [Google Scholar]
  23. Zhou, W.; Cui, F.Z.; Duan, H.K.; Hao, G.H.; Yang, H.; Liu, R.R. Effects of sowing date on yield and quality of waxy maize. Crops 2020, 2, 156–161, (In Chinese with English abstract). [Google Scholar]
  24. Motzo, R.; Giunta, F.; Deidda, M. Relationships between grain-filling parameters, fertility, earliness and grain protein of durum wheat in a Mediterranean environment. Field Crops Res. 1996, 47, 129–142. [Google Scholar] [CrossRef]
  25. Pan, J.; Zhu, Y.; Cao, W.X.; Dai, T.B.; Jiang, D. Predicting the protein content of grain in winter wheat with meteorological and genotypic factors. Plant Prod. Sci. 2006, 9, 323–333. [Google Scholar] [CrossRef]
  26. Lee, B.H.; Kenkel, P.; Brorsen, B.W. Pre-harvest forecasting of county wheat yield and wheat quality using weather information. Agric. For. Meteorol. 2013, 168, 26–35. [Google Scholar] [CrossRef]
  27. Vollmer, E.; Mußhoff, O. Average protein content and its variability in winter wheat: A forecast model based on weather parameters. Earth Interact. 2018, 22, 1–24. [Google Scholar] [CrossRef]
  28. Smith, G.P.; Gooding, M.J. Models of wheat grain quality considering climate, cultivar and nitrogen effects. Agric. For. Meteorol. 1999, 94, 159–170. [Google Scholar] [CrossRef]
  29. Marta, A.D.; Grifoni, D.; Mancini, M.; Zipoli, G.; Orlandini, S. The influence of climate on durum wheat quality in Tuscany, Central Italy. Int. J. Biometeorol. 2011, 55, 87–96. [Google Scholar] [CrossRef]
  30. Zhu, T.Q.; Song, Q.H.; Meng, X.F. Influences of various growth factors on yield and grain quality in wheat—Taking Zhumadian’s wheat production in the past ten years as example. Crops 2020, 6, 80–88, (In Chinese with English abstract). [Google Scholar]
  31. Dai, T.B.; Zhao, H.; Jing, Q.; Jiang, D.; Cao, W.X. Effects of high temperature and water stress during grain filling on grain protein and starch formation in winter wheat. Acta. Ecol. Sin. 2006, 11, 3670–3676, (In Chinese with English abstract). [Google Scholar]
  32. Zhao, Y.; Li, Z.; Hu, X.; Yang, G.; Wang, B.; Duan, D.; Fu, Y.; Liang, J.; Zhao, C. Spatial heterogeneity of county-level grain protein content in winter wheat in the Huang-Huai-Hai region of China. Eur. J. Agron. 2022, 134, 126466. [Google Scholar] [CrossRef]
  33. Caubel, J.; de Cortazar-Atauri, I.G.; Vivant, A.C.; Launay, M.; de Noblet-Ducoudre, N. Assessing future meteorological stresses for grain maize in France. Agric. Syst. 2018, 159, 237–247. [Google Scholar] [CrossRef]
  34. Bonfante, A.; Alfieri, S.M.; Albrizio, R.; Basile, A.; De Mascellis, R.; Gambuti, A.; Giorio, P.; Langella, G.; Manna, P.; Monaco, E.; et al. Evaluation of the effects of future climate change on grape quality through a physically based model application: A case study for the Aglianico grapevine in Campania region, Italy. Agric. Syst. 2017, 152, 100–109. [Google Scholar] [CrossRef]
  35. Xu, J.Q.; Qian, C.; Sun, Y.K.; Zhou, Y.J. Effects of different sowing date on yield and quality of maize. Heilongjiang Agric. Sci. 2019, 8, 32–34, (In Chinese with English abstract). [Google Scholar]
  36. Yang, H.; Shi, Y.L.; Lu, D.L.; Lu, W.P. Effect of sowing date on starch physicochemical properties of summer waxy maize. J. Nucl. Agric. 2016, 30, 1754–1762, (In Chinese with English abstract). [Google Scholar]
  37. Feng, Y.Z.; Chen, H.Y.; Yu, T.Y.; Wen, H.T. Influence of sowing date on main components of waxy maize sowed in autumn in south China. Chin. J. Agrometeorol. 2006, 27, 142–146, (In Chinese with English abstract). [Google Scholar]
  38. Li, X.L.; Zhao, M.; Li, C.F.; Ge, J.Z.; Hou, H.P.; Li, Q.; Hou, L.B. Effect of sowing-date and planting density on dry matter accumulation dynamic and establishment of its simulated model in maize. Acta. Agron. Sin. 2010, 36, 2143–2153, (In Chinese with English abstract). [Google Scholar] [CrossRef]
  39. Wu, F.L.; Wang, L.; Zhao, X. Effect of harvesting period on quality and fresh yield of waxy corn Zhenghuangnuo No.2. J. Henan Agric. Sci. 2016, 45, 29–31, (In Chinese with English abstract). [Google Scholar]
  40. Lu, D.L.; Guo, H.F.; Dong, C.; Lu, W.P. Differences of physicochemical properties for waxy maize flour at fresh and maturity stages. Acta. Agron. Sin. 2010, 36, 2170–2178, (In Chinese with English abstract). [Google Scholar] [CrossRef]
  41. Mehdi, T.; Badii, G.; Rym, B.; Mounira, H.; Hmida, B.H.; Salem, L. Apple breeding in Tunisia and the actual climatic context: Quality assessment and crop adaptation. Int. J. Plant Sci. Ecol. 2015, 1, 131–137. [Google Scholar]
  42. Mkhabela, M.; Bullock, P.; Gervais, M.; Finlay, G.; Sapirstein, H. Assessing indicators of agricultural drought impacts on spring wheat yield and quality on the Canadian prairies. Agric. For. Meteorol. 2010, 150, 399–410. [Google Scholar] [CrossRef]
  43. Brankovic, G.R.; Dodig, D.; Zoric, M.Z.; Surlan-momirovic, G.G.; Dragicevic, V.; Duric, N. Effects of climatic factors on grain vitreousness stability and heritability in durum wheat. Turk. J. Agric. For. 2014, 38, 429–440. [Google Scholar] [CrossRef]
  44. Stagnari, F.; Onofri, A.; Codianni, P.; Pisante, M. Durum wheat varieties in N-deficient environments and organic farming: A comparison of yield, quality and stability performances. Plant Breed. 2013, 132, 266–275. [Google Scholar] [CrossRef]
  45. Kandiannan, K.; Karthikeyan, R.; Krishnan, R.; Kailasam, C.; Balasubramanian, T.N. A crop–weather model for prediction of rice (Oryza sativa L.) yield using an empirical-statistical technique. J. Agron. Crop Sci. 2002, 188, 59–62. [Google Scholar] [CrossRef]
  46. Kristensen, K.; Schelde, K.; Olesen, J.E. Winter wheat yield response to climate variability in Denmark. J. Agric. Sci. 2011, 149, 33–47. [Google Scholar] [CrossRef]
  47. Li, Z.; Taylor, J.; Yang, H.; Casa, R.; Jin, X.; Li, Z.; Song, X.; Yang, G. A hierarchical interannual wheat yield and grain protein prediction model using spectral vegetative indices and meteorological data. Field Crops Res. 2020, 248, 107711. [Google Scholar] [CrossRef]
  48. Xu, X.; Teng, C.; Zhao, Y.; Du, Y.; Zhao, C.; Yang, G.; Jin, X.; Song, X.; Gu, X.; Casa, R.; et al. Prediction of wheat grain protein by coupling multisource remote sensing imagery and ECMWF data. Remote Sen. 2020, 12, 1349. [Google Scholar] [CrossRef]
  49. De Wit, C.T. Photosynthesis of leaf canopies. In Agricultural Research Reports 663; Centre for agricultural publications and Documentation: Wageningen, The Netherlands, 1965. [Google Scholar]
  50. Duncan, W.G.; Loomis, R.S.; Williams, W.A.; Hanau, R. A model for simulating photosynthesis in plant communities. Hilgardia 1967, 38, 181–205. [Google Scholar] [CrossRef]
  51. Jones, J.W.; Hoogenboom, G.; Porter, C.H.; Boote, K.J.; Batchelor, W.D.; Hunt, L.A.; Wilkens, P.W.; Singh, U.; Gijsman, A.J.; Ritchie, J.T. The DSSAT cropping system model. Eur. J. Agron. 2003, 18, 235–265. [Google Scholar] [CrossRef]
  52. Keating, B.A.; Carberry, P.S.; Hammer, G.L.; Probert, M.E.; Robertson, M.J.; Holzworth, D.; Huth, N.I.; Hargreaves, J.N.G.; Meinke, H.; Hochman, H.; et al. An overview of APSIM, a model designed for farming systems simulation. Eur. J. Agron. 2003, 18, 267–288. [Google Scholar] [CrossRef] [Green Version]
  53. Brisson, N.; Gary, C.; Justes, E.; Roche, R.; Mary, B.; Ripoche, D.; Zimmer, D.; Sierra, J.; Bertuzzi, P.; Burger, P.; et al. An overview of the crop model STICS. Eur. J. Agron. 2003, 18, 309–332. [Google Scholar] [CrossRef]
  54. Yin, X.; van Laar, H.H. Crop Systems Dynamics: An Ecophysiological Simulation Model for Genotype-by-Environment Interactions; Wageningen Academic Publishers: Wageningen, The Netherlands, 2005. [Google Scholar]
  55. Li, T.; Angeles, O.; Marcaida, M.; Manalo, E.; Manalili, M.P.; Radanielson, A.; Mohanty, S. From ORYZA2000 to ORYZA (v3): An improved simulation model for rice in drought and nitrogen-deficient environments. Agric. For. Meteorol. 2017, 237, 246–256. [Google Scholar] [CrossRef] [PubMed]
  56. Zhu, Y.; Tang, L.; Liu, L.L.; Liu, B.; Zhang, X.H.; Qiu, X.L.; Tian, Y.C.; Cao, W.X. Research progress on the crop growth model CropGrow. Sci. Agric. Sin. 2020, 53, 3235–3256, (In Chinese with English abstract). [Google Scholar]
  57. Asseng, S.; Ewert, F.; Martre, P.; Rötter, 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]
  58. Matiu, M.; Ankerst, D.P.; Menzel, A. Interactions between temperature and drought in global and regional crop yield variability during 1961–2014. PLoS ONE 2017, 12, e0178339. [Google Scholar]
  59. Zhang, X.H.; Xu, H.; Jiang, L.; Zhao, J.Q.; Zuo, W.J.; Qiu, X.L.; Tian, Y.C.; Cao, W.X.; Zhu, Y. Selection of appropriate spatial resolution for the meteorological data for regional winter wheat potential productivity simulation in China based on WheatGrow model. Agronomy 2018, 8, 198. [Google Scholar] [CrossRef] [Green Version]
  60. Xiao, D.; Tao, F. Contributions of cultivars, management and climate change to winter wheat yield in the North China Plain in the past three decades. Eur. J. Agron. 2014, 52, 112–122. [Google Scholar] [CrossRef]
  61. Liu, L.L.; Wang, E.L.; Zhu, Y.; Tang, L.; Cao, W.X. Effects of warming and autonomous breeding on the phenological development and grain yield of double-rice systems in China. Agric. Ecosyst. Environ. 2013, 165, 28–38. [Google Scholar] [CrossRef]
  62. Folberth, C.; Skalsky, R.; Moltchanova, E.; Balkovic, J.; Azevedo, L.B.; Obersteiner, M.; van der Velde, M. Uncertainty in soil data can outweigh climate impact signals in global crop yield simulations. Nat. Commun. 2016, 7, 11872. [Google Scholar] [CrossRef] [Green Version]
  63. Chen, H.; Lin, J.; Yang, K.; Wang, P.J.; Yang, F.Y.; Chen, H.L.; Chen, T.; Li, L.C.; Li, L.R.; Chen, F.Z. Construction and application of climate quality evaluation model for Indian jujube. J. Appl. Meteor. Sci. 2021, 32, 443–455, (In Chinese with English abstract). [Google Scholar]
  64. Liu, Y.; Tang, L.; Qiu, X.; Liu, B.; Zhu, Y. Impacts of 1.5 and 2.0 °C global warming on rice production across China. Agric. For. Meteorol. 2020, 284, 107900. [Google Scholar] [CrossRef]
  65. Osman, R.; Zhu, Y.; Ma, W.; Zhang, D.; Ding, Z.; Liu, L.; Tang, L.; Liu, B.; Cao, W. Comparison of wheat simulation models for impacts of extreme temperature stress on grain quality. Agric. For. Meteorol. 2020, 288, 107995. [Google Scholar] [CrossRef]
  66. Sun, J.S.; Xiao, J.F.; Duan, A.W.; Zhang, S.M.; Zhang, J.Y. The effect of water consuming law and water stress on growth, development and yield of summer maize. J. Maize Sci. 1999, 2, 46–49, 52, (In Chinese with English abstract). [Google Scholar]
  67. Hansen, P.M.; Jorgensen, J.R.; Thomsen, A. Predicting grain yield and protein content in winter wheat and spring barley using repeated canopy reflectance measurements and partial least squares regression. J. Agric. Sci. 2002, 139, 307–318. [Google Scholar] [CrossRef]
  68. Xue, L.H.; Zhu, Y.; Zhang, X.; Cao, W.X. Predicting wheat grain quality with canopy reflectance spectra. Acta. Agron. Sin. 2004, 30, 1036–1041, (In Chinese with English abstract). [Google Scholar]
  69. Tang, Y.L.; Huang, J.F.; Wang, R.C. Study on estimating the contents of crude protein and crude starch in rice panicle and paddy by hyperspectral. Sci. Agric. Sin. 2004, 37, 1282–1287, (In Chinese with English abstract). [Google Scholar]
  70. Zhao, C.J.; Liu, L.Y.; Wang, J.H.; Huang, W.J.; Song, X.Y.; Li, C.J. Predicting grain protein content of winter wheat using remote sensing data based on nitrogen status and water stress. Int. J. Appl. Earth Obs. Geoinf. 2005, 7, 1–9. [Google Scholar] [CrossRef]
  71. Li, W.G.; Wang, J.H.; Zhao, C.J.; Liu, L.Y.; Song, X.Y.; Tong, Q.X. A model for predicting protein content in winter wheat grain based on Land-Sat TM image and nitrogen accumulation. J. Remote Sens. 2008, 12, 506–514, (In Chinese with English abstract). [Google Scholar]
  72. Wang, J.H.; Li, C.J.; Liu, L.Y.; Huang, W.J.; Zhao, C.J. Progress of remote sensing monitoring and forecasting crop quality. Sci. Agric. Sin. 2008, 41, 2633–2640, (In Chinese with English abstract). [Google Scholar]
  73. He, Q.; Zhou, G. Climate-associated distribution of summer maize in China from 1961 to 2010. Agric. Ecosyst. Environ. 2016, 232, 326–335. [Google Scholar] [CrossRef]
  74. He, Q.; Zhou, G.; Lv, X.; Zhou, M. Climatic suitability and spatial distribution for summer maize cultivation in China at 1.5 and 2.0 °C global warming. Sci. Bull. 2019, 64, 690–697. [Google Scholar] [CrossRef] [Green Version]
  75. Yang, D.; Jin, Z.F.; Ding, Y.Y.; Huang, H.L.; Wang, Z.H. Method and application of climate quality evaluation for juicy peach. Chin. J. Ecol. 2018, 301, 2532–2540, (In Chinese with English abstract). [Google Scholar]
  76. Yao, J.Y.; Yu, H.M.; Que, L.J.; Li, Y.C. Soybean climatic quality assessment: Technical models. Chin. Agric. Sci. Bull. 2019, 35, 134–138, (In Chinese with English abstract). [Google Scholar]
  77. Xie, Y.Y.; Wang, P.J.; Zhu, L.J.; Chen, X.; Huang, Y. Climate quality evaluation model for navel orange in Ganzhou. Chin. J. Ecol. 2019, 38, 2265–2274, (In Chinese with English abstract). [Google Scholar]
  78. Yang, M.F.; Zhang, L.; Ji, C.R. The climate quality of flat peach in Shihezi: Evaluation method. Chin. Agric. Sci. Bull. 2019, 35, 97–101, (In Chinese with English abstract). [Google Scholar]
  79. Lesk, C.; Rowhani, P.; Ramankutty, N. Influence of extreme weather disasters on global crop production. Nature 2016, 529, 84–87. [Google Scholar] [CrossRef]
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He, Q.; Zhou, G.; Liu, J. Progress in Studies of Climatic Suitability of Crop Quality and Resistance Mechanisms in the Context of Climate Warming. Agronomy 2022, 12, 3183. https://doi.org/10.3390/agronomy12123183

AMA Style

He Q, Zhou G, Liu J. Progress in Studies of Climatic Suitability of Crop Quality and Resistance Mechanisms in the Context of Climate Warming. Agronomy. 2022; 12(12):3183. https://doi.org/10.3390/agronomy12123183

Chicago/Turabian Style

He, Qijin, Guangsheng Zhou, and Jiahong Liu. 2022. "Progress in Studies of Climatic Suitability of Crop Quality and Resistance Mechanisms in the Context of Climate Warming" Agronomy 12, no. 12: 3183. https://doi.org/10.3390/agronomy12123183

APA Style

He, Q., Zhou, G., & Liu, J. (2022). Progress in Studies of Climatic Suitability of Crop Quality and Resistance Mechanisms in the Context of Climate Warming. Agronomy, 12(12), 3183. https://doi.org/10.3390/agronomy12123183

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