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Variety distribution pattern and climatic potential productivity of spring maize in Northeast China under climate change
Abstract
This study was based on the daily meteorological data of 101 meteorological stations from 1971 to 2000 and the 0.25°×0.25° grid data from 1951 to 2100 simulated by RegCM3 under the future A1B climatic scenario published by National Climate Center, in combination with the demand of climatic condition for maize growth in Northeast China. The trajectory of agricultural climatic resources and the effects of climate change on variety distribution and climatic potential productivity of spring maize in Northeast China under future climate change were analyzed. The main agro-climatic resource factors include: the initial date daily average temperature stably passing 10°C (⩾10°C), the first frost date, the days of growing period, the ⩾10°C accumulated temperature, and the total radiation and precipitation in the growing period. The results showed that: (1) in the coming 100 years, the first date of ⩾10°C would be significantly advanced, and the first frost date would be delayed. The days of growing period would be extended, the ⩾10°C accumulated temperature and the total radiation would be significantly increased. However, no significant change was found in precipitation. (2) Due to the climate change, the early-maturing varieties will be gradually replaced by late-maturing varieties in Northeast China, and the planting boundaries of several maize varieties would be extended northward and eastward. (3) There would be a significant change in the climatic potential productivity of maize in Northeast China with the high-value gradually moving towards northeast. (4) It was an effective way to increase the climatic potential productivity of maize by appropriate adjustment of sowing date.
... Maize biomass production potential is calculated by the crop growth dynamics statistical method, which divided the production potential into three levels: photosynthetic, light-temperature, and climatic production potential [9,59]. ...
... where B q is the photosynthetic production potential of biomass (t ha −1 ), C is the unit conversion factor with a value of 10, and ∑ Q i is the total solar radiation reached above the maize canopy from sowing to physiological maturity (MJ m −2 ). f (Q) is the photosynthetic coefficient [59], as shown by Equation (8). ...
... Here, the values and meanings of µ, ϕ, α, β, ρ, γ, ω, f (L), ξ, and q are detailed in Table 2 [59,61]. Light-temperature production potential of biomass (B qt ) represents the production potential as a function of solar radiation and temperature with other environmental conditions (water, nutrients, etc.) being optimal. ...
As a key indicator of agricultural production capacity, crop production potential is critical to evaluate the impacts of climate variability on agriculture. However, less attention has been paid to the pattern of biomass accumulation in response to crop climatic production potential and its relation to grain yield formation at an intra-seasonal time scale, especially under evolving soil water limitation. In this study, we combined a mechanism-based empirical model with field experiments conducted at different growth stages of maize on the Northern China Plain (NCP) to assess the dynamic response of biomass accumulation to climatic production potential and its relation to grain yield. The results showed that the ability of climatic production potential to estimate biomass was significantly improved when a vapor pressure deficit (VPD) was incorporated, with the root mean square error (RMSE) reduced by 33.3~41.7% and 45.6~47.2% under rainfed and evolving soil water limitation conditions, respectively. Drought significantly decreased biomass accumulation mainly by decreasing the intrinsic growth rate and delaying the occurrence timing of maximum growth. Moreover, grain yield shared a nonlinear and saturating relationship with biomass across rainfed and water deficit conditions. The results imply that evolving soil water limitation changes the process of biomass accumulation but not its relationship with grain yield. These findings provide useful information to estimate crop production potential under abiotic stresses and improve the accuracy of crop yield prediction.
... The optimum temperature for spring maize growth in NES is 26 • C during vegetative phase and 24 • C during reproductive phase, respectively [40]. The response of maize yield change to maximum temperature was different between region I and region III (Table 2), which is likely attributed to local temperatures relative to optimum. ...
... Early season low temperature affects germination, seedling growth and leaf development [50]. The mean temperature at vegetative phase was 18 • C in regions I and II, and 15.5 • C in regions III (Table 5), much lower than the optimum temperature of 26 • C [40]. Furthermore, the minimum temperature at vegetative phase (Table 5) approached the low threshold of 12 • C [40]. ...
... The mean temperature at vegetative phase was 18 • C in regions I and II, and 15.5 • C in regions III (Table 5), much lower than the optimum temperature of 26 • C [40]. Furthermore, the minimum temperature at vegetative phase (Table 5) approached the low threshold of 12 • C [40]. An increase in Tmin_v would no doubt increase maize yield (Table 5). ...
Maize is the main crop in Northeast China (NEC), but is susceptible to climate variations. Using county-level data from 1980 to 2010, we established multiple linear regression models between detrended changes in maize yield and climate variables at two time windows—whole-season and vegetative and reproductive (V&R) phases. Based on climate change trends, these regression models were used to assess climate variability and change impacts on maize yield in different regions of NEC. The results show that different time windows provide divergent estimates. Climate change over the 31 years induced a 1.3% reduction in maize yield at the time window of whole-season, but an increase of 9.1% was estimated at the time window of V&R phases. The yield improvement is attributed to an increase in minimum temperature at the vegetative phase when the temperatures were much lower than the optimum. Yield fluctuations due to inter-annual climate variability were estimated to be ±9% per year at the time window of V&R phases, suggesting that the impact of climate variability on maize yield is much greater than climate change. Trends in precipitation were not responsible for the yield change, but precipitation anomalies contributed to the yield fluctuations. The impacts of warming on maize yield are regional specific, depending on the local temperatures relative to the optimum. Increase in maximum temperature led to a reduction of maize yield in western NEC, but to an increase in mid-east part of NEC. Our findings highlight the necessity of taking into account the phenological phase when assessing the climate impacts on crop yield, and the importance of buffering future crop production from climate change in NEC.
... The potential and rainfed productivity of wheat was calculated using a simple method based on radiation, temperature, and rainfall during each of the developmental stages of wheat (Huang 1985;He et al. 2014). The method first estimates the photosynthetic productivity, which is then reduced by considering the impact of suboptimal temperatures and water shortage (Huang 1985;Yuan et al. 2012). This method has been widely used in estimating crop productivity in China Yuan et al. 2012;He et al. 2014). ...
... The method first estimates the photosynthetic productivity, which is then reduced by considering the impact of suboptimal temperatures and water shortage (Huang 1985;Yuan et al. 2012). This method has been widely used in estimating crop productivity in China Yuan et al. 2012;He et al. 2014). ...
... In that sense, the method used here is better suited for regional evaluation of wheat potential Fig. 4 a The potential productivity, b the rainfed productivity, and c the gap between the two (kg ha −1 ) of wheat averaged from 1962 to 2010. d-f The rates of change (kg ha −1 per decade) of a-c in Southwest China from 1962 to 2010, respectively productivity and drought severity, particularly in areas with limited data like SWC (Yuan et al. 2012). ...
Wheat production in Southwest China (SWC) plays a vital role in guaranteeing local grain security, but it is threatened by increasingly frequent seasonal drought in recent years. In spite of the importance, the impact of past climate change on wheat potential productivity and drought severity has not been properly addressed. In this study, we employed a relatively simple resource use efficiency model to analyze the spatiotemporal changes of the potential productivity (PP) and rainfed productivity (RP) of wheat (Triticum aestivum L.) in Southwest China (SWC) from 1962 to 2010. A wheat drought severity index was defined as the relative difference between PP and RP, i.e., (PP-RP)/PP, to evaluate the changing frequency and severity of drought under warming SWC. Across the entire region from 1962 to 2010, the negative impact of decreasing sunshine hours (0.06 h day−1 per decade, p < 0.05) on PP was offset by the increase in average temperature of wheat growing season (0.22 °C per decade, p < 0.01). PP increased by 283 kg ha−1 per decade (p < 0.01), while RP did not show significant trend due to increased water stress. The gap between PP and RP has increased by 26 kg ha−1 per decade (p < 0.01). Moderate and severe drought mostly occurred in central and southern SWC. The percentage of stations experienced moderate or severe drought increased by 2.0 % (p < 0.05) per decade, and reached 52 % in recent decade. Our results, together with the uneven distribution of rainfall, indicate great potential for irrigation development to harvest water and increase wheat yield under the warming climate in SWC.
... Photosynthetic, light-temperature, and climatic potential productivity refer to the maximum crop output determined by radiation, lighttemperature, and light-temperature-precipitation conditions, respectively, when soil, seed, and other agricultural techniques are suitable (Huang, 1985;Yang et al., 2010). These measures can provide important theoretical guidance for the planning of agricultural productivity and the reasonable use of climate resources (Yuan et al., 2012). The gap between lighttemperature and climatic potential productivity indicates the yield loss due to water stress and suggests potential to increase yield by improving the water supply (Wu et al., 2006). ...
... Similarly, the spatiotemporal changes in maize potential productivity in Northeast China have also been investigated (Chen et al., 2011;Yuan et al., 2012;Qin et al., 2013). Maize climatic potential productivity has increased with rising temperature during the past 30 years (Chen et al., 2011;Yuan et al., 2012), though it varies greatly due to the variation of precipitation (Chen et al., 2011). ...
... Similarly, the spatiotemporal changes in maize potential productivity in Northeast China have also been investigated (Chen et al., 2011;Yuan et al., 2012;Qin et al., 2013). Maize climatic potential productivity has increased with rising temperature during the past 30 years (Chen et al., 2011;Yuan et al., 2012), though it varies greatly due to the variation of precipitation (Chen et al., 2011). Liu et al. (2012) analyzed maize potential yield and the yield gap in the changing climate of Northeast China, and found that the largest yield gaps are located in the southeast of Northeast China, and a simulated increase of maximum temperature results in a negative impact on the potential yield. ...
The impact of climate change on maize potential productivity and the potential productivity gap in Southwest China (SWC) are investigated in this paper. We analyze the impact of climate change on the photosynthetic, light-temperature, and climatic potential productivity of maize and their gaps in SWC, by using a crop growth dynamics statistical method. During the maize growing season from 1961 to 2010, minimum temperature increased by 0.20°C per decade (p < 0.01) across SWC. The largest increases in average and minimum temperatures were observed mostly in areas of Yunnan Province. Growing season average sunshine hours decreased by 0.2 h day−1 per decade (p < 0.01) and total precipitation showed an insignificant decreasing trend across SWC. Photosynthetic potential productivity decreased by 298 kg ha-1 per decade (p < 0.05). Both light-temperature and climatic potential productivity decreased (p < 0.05) in the northeast of SWC, whereas they increased (p < 0.05) in the southwest of SWC. The gap between light-temperature and climatic potential productivity varied from 12 to 2729 kg ha−1, with the high value areas centered in northern and southwestern SWC. Climatic productivity of these areas reached only 10%–24% of the light-temperature potential productivity, suggesting that there is great potential to increase the maize potential yield by improving water management in these areas. In particular, the gap has become larger in the most recent 10 years. Sensitivity analysis shows that the climatic potential productivity of maize is most sensitive to changes in temperature in SWC. The findings of this study are helpful for quantification of irrigation water requirements so as to achieve maximum yield potentials in SWC.
... The photothermal potential yield (YT, t·ha −1 ) was calculated by the stepwise correction method, which is recommended by the Food and Agriculture Organization of the United Nations (FAO) and has been widely used. From the perspective of energy conversion, the temperature, solar radiation, and other meteorological factors were taken into account, and the photothermal potential yield as corrected by temperature to calculate crop potential yield [35][36][37]: ...
... of the crop; and H is the energy conversion coefficient (MJ·kg −1 ). The specific parameter values are shown in Table 1 [35][36][37]. f (t) is the temperature stress coefficient, with values in 0-1 and is calculated by Equation (6) [38], where t is the average temperature ( • C) in a certain growth period of winter wheat; t min , t s , and t max are the minimum, optimum, and maximum temperature ( • C) for each growth period, respectively, and are listed in Table 2 [35]. Table 2. Minimum (t min ), optimum (t s ), and maximum (t max ) temperatures for each winter wheat developmental stage. ...
Timely sowing and harvesting play important roles in agricultural production. The appropriate management decisions are necessary to cope with climate change and ensure high and stable crop yields. This study analyzed the effects of sowing date on the growth process of winter wheat and quantified the effects of climate resources and photothermal potential yield on theoretical yield at different stages of winter wheat. The analysis was based on the data from winter wheat interval sowing experiments conducted at the Hebei Gucheng Agricultural Meteorology National Observation and Research Station (Gucheng station) in north China (115°40′ E, 39°08′ N) during 2017–2019. The results showed that: (1) with the delay in sowing date, the growth process of winter wheat significantly advanced, the proportion of vegetative growth period significantly reduced (0.19% for per day delay), the proportion of reproductive growth period (RGP) significantly increased (0.12% for per day delay), and the prewintering light and temperature resources significantly reduced (12.2 °C·d accumulated temperature and 19.0 MJ·m−2 solar radiation for per day delay); (2) the theoretical yield of winter wheat showed a significant exponential relationship with the photothermal potential yield of the whole growth period: the minimum photothermal potential for yield formation was 26.6 t·ha−1, and the maximum theoretical yield was 12.6 t·ha−1; and (3) the wheat yield and yield stability were highest when the RGP photothermal potential yield was 16.0 t·ha−1 and the prewintering active accumulated temperature was 400 °C·d. This study also proposed a method to estimate the suitable sowing and harvesting dates to achieve high and stable yield of winter wheat, showing that the suitable sowing dates of winter wheat at Gucheng station from 1997 to 2021 ranged from 1 to 15 October, with no significant interannual variation; the suitable harvesting period ranged from 5 June to 10 July and showed a trend of gradual advance with the delay of the year. The results of the study provide a reference for sowing date adjustment of crops to adapt to climate change.
... In this study, the RZSM declined in all layers, which is in agreement with previous findings (Moiwo et al., 2012). In addition, the northward extension and eastward migration in previous studies were consistent with the boundary migration in this study, which also confirmed the reliability of the water shortage boundary (Yuan et al., 2012;Zhao et al., 2015). ...
... Figure 7(a) showed the spatial distribution of the precipitation change rate from 1961 to 2010. In general, the precipitation showed a decreasing trend, but the dropping rate was weak, and the trend was consistent with the results of previous studies (Fang et al., 2018;Yuan et al., 2012). Some areas (north and southeast of NEC) showed a weak upward trend (0-0.5 mm/year), but the area was mainly forest. ...
Soil moisture (SM) is the most direct and important source of crop water requirement. The change in SM levels and the maize water requirement (MWR) will, under the influence of climate change, lead to changes in the areas that are suitable for maize cultivation in Northeast China (NEC). To quantitatively investigate these changes, the study analyzed the spatiotemporal changes in the SM and MWR in NEC from 1961 to 2010 using Global Land Data Assimilation System (GLDAS) SM products, meteorological data and land use data. The following conclusions were reached: 1) In the past 50years, there has been an obvious trend of soil drying in NEC, and the equivalent water thickness at the depth of 0–200 cm has decreased by 71.06 mm (12.78%), which is more serious in agricultural areas (77.09 mm, 13.86%). 2) From 1961 to 2010, the air temperature in NEC increased by about 1.8 °C in 50 years.. 3) The shortage in maize water calculated by SM and MWR showed that areas unsuitable for maize cultivation increased by approximately 66,250 km², an area close to one-third of the dry farmland area in NEC; and 4) the slightly decreased precipitation and increased air temperature were the major driving factors for the decrease in the suitable maize cultivation area. If the maize was planted in unsuitable areas for a long time, it may lead to excessive use of groundwater and surface water. Therefore, these results provide a base for decision-making regarding adjustments to the cultivation structure in NEC.
... Climate change has a profound impact on sustainable economic and social development and poses a serious threat to food security, ecological security and water resources security 7,8 . Climate change will increase the instability of agricultural production, increase the uctuation of output, and change the layout and structure of agricultural production [9][10][11] . Climate change has aggravated the comprehensive land pressure, seriously affected the global food security, and increased the disaster risk and loss of agricultural production, especially in some vulnerable regions 12 . ...
... The increase of average temperature has a negative effect on the average yield of maize in China, but a signi cant positive effect on the yield of maize in eastern Northeast China 56 . With the warming of climate, the planting boundary of maize varieties in Northeast China has been moving northward and eastward, and the high value area of its climatic productivity potential has been moving northward 10,11,14 . The light temperature production potential and climate production potential of maize in Northeast China have a regional change trend affected by temperature 57 . ...
Climate warming has a great impact on grain production in northeast China, but there are few studies on the temporal and spatial variation characteristics of annual Tmean (mean temperature), the impact of meteorological factors on Tmean, and the impact of Tmean increase on grain production in northeast China. This study found that annual Tmean decreased from southeast to northwest in Northeast China, and there were regional differences in spatial distribution. The annual Tmean isoline in Northeast China moves obviously from southeast to northwest. The annual warming trend of Tmean was significant from 1971 to 2000, and moderated from 2001 to 2020. In recent 50 years, Tmean had obvious periodic changes. In the mid-late 1980s, annual Tmean had a sudden warming change, and since then it has been rising continuously. Sunshine hours, average wind speed, evaporation and average air pressure had a very significant correlation with Tmean. In conclusion, the climate change in northeast China in the past 50 years has an obvious warming and drying trend, and there are regional differences in the warming and drying. The warming and drying climate has brought challenges to agricultural production and food security in Northeast China. However, the negative effects of grain production reduction caused by warming and drying climate can be avoided to a certain extent if we deal with it properly.
... The potential light-temperature productivity of summer corn (PTP, unit kg ha −1 ), determined by radiation and temperature, represents the maximum potential crop yield under stress-free condition [23]. In this study, we first estimated the photosynthetic productivity (PP, unit kg ha −1 ) by using an empirical model, which has been widely used in calculating potential crop productivity in China [34,35]: ...
... The PTP was then determined by considering the impact of suboptimal temperatures in each growth stage of maize [34]: ...
Global warming and dimming/brightening have significant implications for crop systems and exhibit regional variations. It is important to clarify the changes in regional thermal and solar radiation resources and estimate the impacts on potential crop production spatially and temporally. Based on daily observation data during 1961–2015 in the North China Plain (NCP), the impacts of climate change associated with climate warming and global dimming/brightening on potential light–temperature productivity (PTP) of summer maize were assessed in this study. Results show that the NCP experienced a continuous warming and dimming trend in maize growing season during the past 55 years, and both ATT10 and solar radiation had an abrupt change in the mid-1990s. The period of 2000–2015 was warmer and dimmer than any other previous decade. Assuming the maize growing season remains unchanged, climate warming would increase PTP of summer maize by 4.6% over the period of 1961–2015, which mainly occurred in the start grain filling–maturity stage. On the other hand, as negative contribution value of solar radiation to the PTP was found in each stage, dimming would offset the increase of PTP due to warming climate, and lead to a 15.6% reduction in PTP in the past 55 years. This study reveals that the changes in thermal and solar radiation have reduced the PTP of summer maize in the NCP. However, the actual maize yield could benefit more from climate warming because solar radiation is not a limiting factor for the current low production level.
... Projections of climate variations that may drive changes in the cropped areas have been widely reported using global climate model (also known as general circulation model; GCM) or regional climate model outputs, generated for the Assessment Report (AR) of the Intergovernmental Panel on Climate Change (IPCC). It has been suggested that early maturing maize varieties will be gradually replaced by middle and late maturing varieties, and that the planting boundaries of several maize varieties may extend northward and eastward to different degrees in northeastern China under some future climate scenarios (e.g., A1B, A2, B1 and RCP4.5 scenarios of the IPCC) Yuan et al., 2012;Hu and Liu, 2013). ...
... The areas suitable for rice and maize cultivation under RCP8.5 shifted northward, with a gradual disappearance of suitable areas in some parts of southern China, and increase in suitable areas from northwestern to northern China. This is consistent with previous studies that showed a northward shift and an eastward expansion of the planting boundaries for various rice and maize varieties under the SRES A1B and A2 scenarios, which are similar to RCP8.5 (Yuan et al., 2012;Li et al., 2015a). ...
Predictions of changes in the distribution of areas suitable for the cultivation of rice and maize in China under future climate change scenarios may provide scientific support for the optimization of crop production and measures to mitigate climate change. We conducted a spatial grid-based analysis using projections of future climate generated by the National Center for Atmospheric Research Community Climate System Model version 4 for two representative concentration pathway scenarios (RCP2.6 and RCP8.5), adopted by the fifth phase of the Coupled Model Intercomparison Project to study the areas suitable for the cultivation of rice and maize in China. We investigated the migration of the centers of gravity of the cultivation areas based on climatic and hydrological factors from 2021 to 2100. The results indicated that, under RCP2.6, the areas suitable for the cultivation of rice were located throughout China, except for on the Qinghai–Tibetan Plateau, while the areas suitable for the cultivation of maize were located in northern, southwestern, central, eastern, parts of northeastern and some northern parts of western China. The distributions of both crops under RCP2.6 showed little change over time. In contrast, the areas suitable for the cultivation of rice and maize under RCP8.5 shifted northward and expanded from northwestern to northern China, as a result of greater warming in northern China and the faster warming trend under RCP8.5. This scenario would require much stronger climate mitigation policies to maintain the stable development of agriculture and to slow down the future migration of crop cultivation areas in China. The distribution of areas suitable for the cultivation of rice and maize should be studied further to design appropriate adaptation strategies for dealing with future climate change.
... The increase in temperature has delayed autumn frosts and led to earlier sowing and later harvest, thus prolonging the potential growing season (Tao et al. 2006(Tao et al. , 2014Chen et al. 2012;Yuan et al. 2012). In addition, warmer conditions have shifted the maize planting boundary northwards (Liu et al. 2013c). ...
... The benefits of higher mean temperature stemmed from earlier sowing in the seeding phase and later harvest in the maturity phase. This extended the maize growing season and enhanced the yield (Tao et al. 2006(Tao et al. , 2014Chen et al. 2011;Olesen et al. 2011;Yuan et al. 2012). Although not significant, analysis Fig. 7. Spatial variation of (a-d) years with drought stress days and (e-h) average drought stress days excluding the zero values during 1961-2010 in NFR for different maize growth phases, where drought stress days means the days with water deficit, and years with drought stress days represent the number of years that the drought stress days were larger than 0. Colour online. ...
Drought risk is considered to be among the main limiting factors for maize (Zea maysL.) production in the Northeast Farming Region of China (NFR). Maize yield data from 44 stations over the period 1961–2010 were combined with data from weather stations to evaluate the effects of climatic factors, drought risk and irrigation requirement on rain-fed maize yield in specific maize growth phases. The maize growing season was divided into four growth phases comprising seeding, vegetative, flowering and maturity based on observations of phenological data from 1981 to 2010. The dual crop coefficient was used to calculate crop evapotranspiration and soil water balance during the maize growing season. The effects of mean temperature, solar radiation, effective rainfall, water deficit, drought stress days, actual crop evapotranspiration and irrigation requirement in different growth phases were included in the statistical model to predict maize yield. During the period 1961–2010, mean temperature increased significantly in all growth phases in NFR, while solar radiation decreased significantly in southern NFR in the seeding, vegetative and flowering phases. Effective rainfall increased in the seeding and vegetative phases, reducing water deficit over the period, whereas decreasing effective rainfall over time in the flowering and maturity phases enhanced water deficit. An increase in days with drought stress was concentrated in western NFR, with larger volumes of irrigation needed to compensate for increased dryness. The present results indicate that higher mean temperature in the seeding and maturity phases was beneficial for maize yield, whereas excessive rainfall would damage maize yield, in particular in the seeding and flowering phases. Drought stress in any growth stage was found to reduce maize yield and water deficit was slightly better than other indicators of drought stress for explaining yield variability. The effect of drought stress was particularly strong in the seeding and flowering phases, indicating that these periods should be given priority for irrigation. The yield-reducing effects of both drought and intense rainfall illustrate the importance of further development of irrigation and drainage systems for ensuring the stability of maize production in NFR.
... Maize (Zea mays L.) is a critical crop for sustaining human life in terms of its role as a major grain commodity, feed commodity, and significant bioethanol energy source (Li et al., 2011). A number of studies have documented that climate change in China has affected maize phenology (Li et al., 2014), yield (Tao et al., 2006;Li et al., 2011), productivity (Zhao et al., 2011Yuan et al., 2012), and agricultural climatic resource utilization (Guo et al., 2013). For example, Xiong et al. (2012) reported that warming trends have negatively affected maize production in China. ...
... Estimation of the climatic potential productiv-ity of a crop is fundermental for research on comprehensive grain production capacity, which can provide important theoretical guidelines for the distribution of agricultural production, adjustment to agricultural structure, and reasonable use of climate resources (Yuan et al., 2012). ...
Crop yields are affected by climate change and technological advancement. Objectively and quantitatively evaluating the attribution of crop yield change to climate change and technological advancement will ensure sustainable development of agriculture under climate change. In this study, daily climate variables obtained from 553 meteorological stations in China for the period 1961–2010, detailed observations of maize from 653 agricultural meteorological stations for the period 1981–2010, and results using an Agro-Ecological Zones (AEZ) model, are used to explore the attribution of maize (Zea mays L.) yield change to climate change and technological advancement. In the AEZ model, the climatic potential productivity is examined through three step-by-step levels: photosynthetic potential productivity, photosynthetic thermal potential productivity, and climatic potential productivity. The relative impacts of different climate variables on climatic potential productivity of maize from 1961 to 2010 in China are then evaluated. Combined with the observations of maize, the contributions of climate change and technological advancement to maize yield from 1981 to 2010 in China are separated. The results show that, from 1961 to 2010, climate change had a significant adverse impact on the climatic potential productivity of maize in China. Decreased radiation and increased temperature were the main factors leading to the decrease of climatic potential productivity. However, changes in precipitation had only a small effect. The maize yields of the 14 main planting provinces in China increased obviously over the past 30 years, which was opposite to the decreasing trends of climatic potential productivity. This suggests that technological advancement has offset the negative effects of climate change on maize yield. Technological advancement contributed to maize yield increases by 99.6%–141.6%, while climate change contribution was from −41.4% to 0.4%. In particular, the actual maize yields in Shandong, Henan, Jilin, and Inner Mongolia increased by 98.4, 90.4, 98.7, and 121.5 kg hm−2 yr−1 over the past 30 years, respectively. Correspondingly, the maize yields affected by technological advancement increased by 113.7, 97.9, 111.5, and 124.8 kg hm−2 yr−1, respectively. On the contrary, maize yields reduced markedly under climate change, with an average reduction of −9.0 kg hm−2 yr−1. Our findings highlight that agronomic technological advancement has contributed dominantly to maize yield increases in China in the past three decades.
... The influence of temperature on grain yield is dependent on geographic location and its climate background, as shown in Table 1, the station significantly influenced grain yield changes, accounting for a larger proportion of total changes. For example, temperature increased significantly in the Northeast Plain, with the largest warming amplitudes among all agricultural areas in the country (Yuan et al., 2012). GY at Hailun showed a significant relationship with T mean , with the largest sensitivity (Fig. 4). ...
It is important to investigate the response and adaptation mechanisms of crop traits and yield to ongoing climate change under genotype (G), environment (E), and management (M) interactions in order to develop climate-resilient cultivars and agricultural systems. Here, using long-term trial data at six comprehensive agricultural experiment stations across China's Maize Belt, we investigated the changes in maize traits and yield during 1998-2019 under the interactions of G×E × M. The first-order difference, multi-collinearity detection, Pearson correlation, stepwise linear and a machine learning method were employed to construct the best statistical regression models between maize traits and climate variables and disentangle the relative contributions of climate, cultivar and management to grain yield changes. The results showed a warming trend but a decreasing trend in solar radiation at all the investigated stations. Climate variations affected maize traits and subsequently grain yield, and there were trade-offs among maize traits. With local agricultural management practices, about 20.5-73.8%, 1.2-42.7% and 2.3-37.0% of the yield changes were associated with climate change, cultivar shifts and management, respectively. The stations with higher latitudes exhibited greater variations in yield due to climate variations, compared to the stations with lower latitudes. Wind speed, radiation, and rainfall was found to affect maize yield in Northeast China by affecting evapotranspiration, soil moisture and drought occurrence. The ideal cultivars were characterized by a relatively long grain-filling stage, large hundred-grain weight and number of rows. This study gains new insights into the response and adaptation mechanisms of maize traits and yield to ongoing climate change, providing evidence for designing cultivar ideotypes and optimizing agricultural management in different environments.
... Yield factors were significantly negatively correlated with the GWF, GYWF and TWF in every province in Northeast China, indicating that the maize water footprint is largely influenced by agricultural management. According to Zhuo et al.'s research, it is believed that an increase in the crop yield can help to reduce the per capita crop consumption WF across the nation [46], so it can improve the level of agricultural management, increase the crop yield and then reduce the water footprint. ...
Using the water footprint (WF) approach to evaluate the water-use efficiency in agricultural production is crucial for assessing the sustainable use of water resources and mitigating water scarcity and pollution. This study calculated the blue, grey, green and total water footprints of spring maize production in Northeast China in 2019 and 2020 and compared the water footprint values at the provincial and municipal scales. In addition, this study analyzed the spatial variation and drivers of the water footprint. The results show that the average water footprints of spring maize production in Northeast China in 2019 and 2020 were 1.78 m3kg−1 and 2.00 m3kg−1, out of which the grey water footprint contributed the most, accounting for 55.19% and 49.85% of the total water footprint, respectively, while the blue water footprint contributed the least, accounting for only 17.44% and 18.68% of the total water footprint. At the provincial level, the water footprint of spring maize production in Northeast China was spatially clustered, with the lowest total water footprint in Heilongjiang Province and the highest total water footprint in Jilin Province. The spatial distribution difference of the spring maize unit yield was the fundamental factor explaining the difference in the water footprint. The precipitation, surface water resources, average temperature, effective irrigated area and the proportion of effective irrigated area also had impacts on the water footprint. This study provides a scientific basis for optimizing the distribution of spring maize production in Northeast China, formulating appropriate sustainable water resource management plans, improving water-use efficiency and realizing sustainable water resource management in Northeast China.
... The diversion of water and the establishment of the corresponding irrigation district construction should be made in the area of the Nengjiang River and the Nilchi Reservoir, where surface water resources are relatively abundant. The well irrigation should be taken in other areas within this study according to the local water resource conditions to prevent overexploitation [58]. In agricultural production, farmers should make adjustments according to the actual irrigation water utilization coefficient of the local agricultural production. ...
Appropriate irrigation schedules could minimize the existing imbalance between agricultural water supply and crop water requirements (ETc), which is severely impacted by climate change. In this study, different hydrological years (a wet year, normal year, dry year, and an extremely dry year) in Heilongjiang Province were calculated by hydrological frequency methods. Then, the single crop coefficient method was used to calculate the maize ETc, based on the daily meteorological data of 26 meteorological stations in Heilongjiang Province from 1960 to 2020. Afterward, the CROPWAT model was used to calculate the effective precipitation (Pe) and irrigation water requirement (Ir), and formulate the irrigation schedules of maize in Heilongjiang Province under different hydrological years. The results showed that ETc and Ir decreased first and then increased from west to east. The Pe and crop water surplus deficit index increased first and then decreased from west to east in Heilongjiang Province. Meanwhile, the average values of the Ir in were 171.14 mm, 232.79 mm, 279.08 mm, and 334.47 mm in the wet year, normal year, dry year, and extremely dry year, respectively. Heilongjiang Province was divided into four irrigation zones according to the Ir of different hydrological years. Last, the irrigation quotas for the wet year, normal year, dry year, and extremely dry year were 0~180 mm, 20~240 mm, 60~300 mm, and 80~430 mm, respectively. This study provides reliable support for maize irrigation practices in Heilongjiang Province, China.
... Then, Lp, Q, Cp, D, Rp, and Z were taken as the principal measured parameters in this study, and were calculated by using this measurement system. Determination of accumulated temperature is an essential index of crop growth and development and it is the sum of the daily average air temperature in the continuous period when daily average air temperature ≥10°C in a year [22][23][24] . It was calculated as the mean of the highest and lowest air temperature in one day [25] . ...
... Furthermore, the difference in trends of HDD or CDD among the sites highlighted the spatial variability of extremes, which, in turn, indicated local differences in past climate warming. Consequently, it is helpful to identify the local cold and heat stress characteristics when adjusting agricultural practices to achieve higher production with climate warming (Yuan et al. 2012;Zhao et al. 2014). Table 1 gives the estimated regression coefficients for the model. ...
Northeast China (NEC), an important maize region located at high northern latitudes, is undergoing pronounced climate warming. This warming highlights the importance of taking the effects of brief extreme high and low temperature events into account when addressing the impacts of climate warming on crop yields. The spatiotemporal variability of heat and cold stress and their impact on yield were determined by combining climate data, maize phenological observations and yield records from 1981 to 2018 for 17 counties in the major maize cropping area of NEC. Spatially, more severe heat stress during the vegetative phase occurred in the western border and cold stress occurred in the central regions. Both stresses during the reproductive phase and growing degree days (GDD) showed a northeast–southwest gradient. Temporally, we found overall increased (0.5 °C•d/yr) heat stress during the vegetative phase, which was predominantly more than five times that during the reproductive phase. The cold stress during the vegetative phase was alleviated by an average of –1.3 °C•d/yr, particularly in the central regions. In contrast, exposure to a cold environment during the reproductive phase was intensified, with an average of 0.3 °C•d/yr, though a few downward trends mainly occurred near the borders of three provinces. The increasing trend in GDDr was 3.6 °C•d/yr, almost twice as high as that in the vegetative phase. The impact of increased heat stress contributed an average yield loss of 0.10 t/ha/10a, while reduced cold stress during the vegetative phase coupled with increased GDD increased yield by 0.42 t/ha/10a. Although cold stress during the reproductive phase had an insignificant impact on yield, its intensity together with the increase in heat stress, especially in the vegetative phase, should be considered when developing appropriate adaptations to increase maize yield in the face of ongoing warming.
... Accumulated temperature is an essential index of crop growth and development [12] . It is the sum of the daily average air temperature in a continuous period when the daily average air temperature was equal to or greater than 10°C in a year [13,14] . The accumulated temperature was calculated as the mean of the highest and lowest air temperature in one day [15] . ...
... Grain production is an important link in ensuring food security. As such, grasping the changing patterns of potential climatic productivity is crucial to ensuring national and even global food security [57][58][59][60]. ...
This study examines the spatial and temporal impacts of climate change on grain production in China. This is achieved by establishing a spatial error model consisting of four indicators: the climate, air pollution, economic behavior, and agricultural technology, covering 31 provinces in China from 2004 to 2020. These indicators are used to validate the spatial impacts of climate change on grain production. Air pollution data are used as instrumental variables to address the causality between climate and grain production. The regression results show that: First, climatic variables all have a non-linear “increasing then decreasing” effect on food production. Second, SO2, PM10, and PM2.5 have a negative impact on grain production. Based on the model, changes in the climatic production potential of grain crops can be calculated, and the future spatial layout of climate production can also be predicted by using random forests. Studies have shown that the median value of China’s grain production potential is decreasing, and the low value is increasing.
... The climatic production potential of crops refers to the crop yields when light, heat and water resources are optimally matched under ideal conditions [1], and it is one of the important criteria for evaluating agroclimatic resources [2]. The responses of the climatic production potential of crops to climate change differ significantly among regions and crops [3][4][5]. ...
Understanding the impacts of regional climate change on crop production will benefit strategic decisions for future agricultural adaptation in China. In this study, the climatic production potential of potato over the past 61 years in Inner Mongolia was simulated based on long-term observed data and the step-by-step correction method. The results show that the annual average potential for potato climatic production in Inner Mongolia is 19,318 kg·hm−2, fluctuating between the highest value (25,623 kg·hm−2) and the lowest value (15,354 kg·hm−2). Over the past 61 years, the climatic production potential exhibited an insignificant decreasing trend, with large interannual fluctuation, especially since 2000. The high-value areas of the climatic production potential were mainly located in the central and southern regions. The climatic production potential of potato in most areas showed a decreasing trend. The influence of radiation changes on the potato climatic production potential was not obvious in most areas. The effects of temperature changes on the climatic production potential of potato were mostly negative, and were most obvious in the central and western regions and in the southeastern region. The change in precipitation in most parts of western Inner Mongolia, Hohhot, Chifeng and eastern Xingan League had a positive effect on the climatic production potential of potato. However, the change in precipitation in southern Ulanchabu, eastern Chifeng, Hulunbuir and western and eastern regions had a negative effect on the climatic production potential of potato. The main limiting factor for the climatic production potential of potato in Inner Mongolia is precipitation. Our findings have important implications for local potato production to cope with ongoing climate change in China.
... From the potentially suitable planting area of the four main crops simulated by the model, the suitable area is smaller than the distribution map based on remote sensing and survey data (Chen et al., 2016). The area of high suitability was also small, as found in previous studies (Yuan et al., 2012). These results are mainly due to the fact that the MaxEnt model reflects the basic niche of species (Phillips et al., 2006), so an ideal distribution is almost impossible. ...
Making full use of agricultural resource endowment, determining the planting suitability of areas for different crops according to the environment and human activities, and optimizing planting structure are important ways to ensure stable increases in crop yield and improve food production capacity. Taking Songhua River Basin (SRB) as an example, this study used geographic distribution information on different crops and the Maximum Entropy (MaxEnt) model to determine the degree of suitability of land in SRB for cropping, and to optimize the layout of crop planting structure. The results showed that the main factors affecting land suitability for different crops, with a combined contribution >80%, were population density, Distance from road to cultivated land, normalized difference vegetation index, and total phosphorus. Under the joint influence of the environment and human activity, the total unsuitable area of the four crops became much more extensive, with the unsuitable area of soybean being the largest (173 thousand km²) and the smallest for wheat (128 thousand km²). The highly suitable area was largest for wheat (2 thousand km²), while the other three crops were less than 2 thousand km². Suitable distribution areas for all four crops were mainly located in the center of the basin (Songnen Plain) and in a wedge in the northeast corner (Sanjiang Plain). The relationships between different crops and environment and human activities revealed that crop suitability distribution is mainly determined by human activities, rather than the environment. These results provide a scientific basis for optimizing crop layout and improving the planting system, ensuring the security of food production.
... The asynchrony in the change of agricultural resources and yields has caused changes in resource use efficiency. From 1951 to 2100, if the adaptation measures of climate change haven't been taken, the use efficiency of climate resources would show a decreasing trend, and regions with the highest resource use efficiencies moved northeastward in NEC (Yuan 2012;Xu 2014). In Heilongjiang Province, the potential of heat use efficiency (HUE) decreased 0.025 kg ha -1 °C -1 yr -1 from 1987 to 2017 (Zhao et al. 2019). ...
Northeast China (NEC) is one of the major maize production areas in China. Agro-climatic resources have obviously changed, which will seriously affect crop growth and development in this region. It is important to investigate the contribution of climate change adaptation measures to the yield and resource use efficiency to improve our understanding of how we can effectively ensure high yield and high efficiency in the future. In this study, we divided the study area into five accumulated temperature zones (ATZs) based on growing degree days (GDD). Based on the meteorological data, maize data (from agro-meteorological stations) and the validated APSIM-Maize Model, we first investigated the spatial distributions and temporal trends of maize potential yield of actual planted cultivars, and revealed the radiation use efficiency (RUE) and heat resource use efficiency (HUE) from 1981 to 2017. Then according to the potential growing seasons and actual growing seasons, we identified the utilization percentages of radiation (PR) resource and heat resource (PH) for each ATZ under potential production from 1981 to 2017. Finally, we quantified the contributions of cultivar changings to yield, PR and PH of maize. The results showed that during the past 37 years, the estimated mean potential yield of actual planted cultivars was 13 649 kg ha–1, ranged from 11 205 to 15 257 kg ha–1, and increased by 140 kg ha–1 per decade. For potential production, the mean values of RUE and HUE for the actual planted maize cultivars were 1.22 g MJ–1 and 8.58 kg (°C d)–1 ha–1. RUE showed an increasing tendency, while HUE showed a decreasing tendency. The lengths of the potential growing season and actual growing season were 158 and 123 d, and increased by 2 and 1 d per decade. PR and PH under potential production were 82 and 86%, respectively and showed a decreasing tendency during the past 37 years. This indicates that actual planted cultivars failed to make full use of climate resources. However, results from the adaptation assessments indicate that, adoption of cultivars with growing season increased by 2–11 d among ATZs caused increase in yield, PR and PH of 0.6–1.7%, 1.1–7.6% and 1.5–8.9%, respectively. Therefore, introduction of cultivars with longer growing season can effectively increase the radiation and heat utilization percentages and potential yield.
... These researches often focus on vegetation and crops. For example, Yuan et al. (2012) analyzed the variety distribution and climatic productivity potential of spring maize in Northeast China under climate change conditions in the future. The climate resource-carrying capacity refers to the maximum number of people that can be carried by a unit area of land, given the current climate production potential (Wu et al. 2018). ...
It is important to evaluate the impact of undesirable energy output on the climate-carrying capacity of the power grid-based economy to promote the green development. Three indicators-climate natural capacity, urban climate pressure, and urban coordinated development capacity-are used as input factors to study the climate-carrying capacity. The Nemerow index method and comprehensive evaluation method based on entropy weight are employed to calculate inputs. Pollution emissions such as carbon dioxide emissions, waste gas, wastewater, and solid waste pollution are included as energy undesirable outputs, and industry output value is included as a desirable output to calculate the non-radial directional distance of the output of climate-carrying capacity that combines desirable and undesirable outputs. The total factor non-radial directional distance function and energy-environmental non-radial directional distance function are used to obtain the efficiency index of total factor climate-carrying capacity and the efficiency of climate-carrying capacity performance, respectively. These two indices are included in the analysis to estimate the impact of energy undesirable output on climate-carrying capacity. Results from empirical analysis showed that when two types of undesirable outputs, namely waste gas and wastewater outputs, in Shanghai are constrained, the efficiency and performance efficiency of climate environmental carrying capacity are both lower than 0.8, indicating that undesirable outputs had a substantial influence on the climate-carrying capacity. In Shanghai, the major approach to improve the regional climate-carrying capacity is to improve energy efficiency and reduce undesirable outputs of power grid-based economy.
... Previous results show that the increase of temperature in recent years has led to an advance in the sowing date of maize, an increase in the total accumulated temperature of the maize growing season, an extension of the growing period, and a shift in the planting boundary to the north and expansion to the east. To realize the effective utilization of heat resources and improve yield, many new cultivars have emerged, cultivation measures have changed, and early cultivars have been gradually replaced by middle-late cultivars [48,49]. These problems extend the growth period length and increase the maize leaf number, leading to an increase of ET c increases. ...
Climate change will have a significant effect on crop water requirement (ET c). The spatial and temporal variations of water requirement of maize under climate change are essential elements when conducting a global water security assessment. In this paper, annual reference crop evapotranspiration (ET 0) and the crop water requirement of maize were calculated by the single crop coefficient method. The crop water surplus deficit index (CWSDI) and coupling degree of ET c and effective precipitation (P e) were calculated to analyze the relationship between ET c , ET 0 , and P e. The result shows that maize average annual ET 0 , ET c , and precipitation were 552.97, 383.05, and 264.97 mm, respectively. Moreover, ET 0 , ET c , and P e decreased by 3.28, 2.56, and 6.25 mm every decade from 1960 to 2015. The ET c decreased less than P e did, which led to the decreasing of both CWSDI and the coupling degree of ET c and P e. The tendency of ET 0 , ET c decreased first and then increased, while P e and CWSDI increased first and then decreased, from west to east of the Heilongjiang Province. In addition, the highest ET 0 , ET c , and lowest CWSDI and P e were found in the western part of Heilongjiang Province. This study indicated that even though the water deficit in the western region was alleviated and the water deficit in the eastern region grew gradually serious from 1960 to 2015, the drought situation in western Heilongjiang Province should still be taken seriously.
... The accumulated temperature model includes temperatures of three fundamental points, and they differ among growth stages. We determined the temperatures of three fundamental points at different stages according to previously published Optimizing parameters of a non-linear accumulated temperature model and method to calculate linear... data for spring maize (Wang et al. 2005;Yuan et al. 2012;Xu et al. 2014) (Table 2). ...
Accumulated temperature is an important factor for modeling crop growth. It is stable in theory, but in practice, the accumulated temperature needed for crops during different growth stages differs markedly among different years, regions, and varieties, so the stability is relative and the instability is absolute. Therefore, it is useful to establish a general model to calculate accumulated temperature that is relatively stable and applicable to different maize varieties. In this study, we analyzed the stability of accumulated temperature and the parameters of the non-linear accumulated temperature model (NLM). The NLM was optimized to improve its application range. A linear accumulated temperature model (LM) was also optimized based on the most important factor affecting the stability of accumulated temperature. We compared different methods for calculating accumulated temperature. We found that the accumulated temperature needed for crops during different growth stages differed markedly among different years, regions, and varieties. The main reason for the instability of calculated accumulated temperature values was temperature strength for a certain variety. Therefore, the calculation method was revised by adding a quadratic function, generating the temperature revision model after revision (TRM). The parameter Q of the NLM is a thermal-sensitive parameter. There were strong correlations between Q and mean active accumulated temperature or effective accumulated temperature for different varieties during emergence to maturity, indicating that Q was related to the maturity type. Consequently, we proposed two general accumulated temperature models, AARM and EARM, in which the parameters of NLM were denoted by the active accumulated temperature or effective accumulated temperature. Comparing the different models, the TRM generated minimal bias but AARM and EARM had a wider application range for many varieties on a large scale. AARM had better simulation effect, while EARM was more stable. The applicability of the optimized models was improved. The results provide a new approach for optimization of agrometeorological indexes and upscaling of accumulated temperature models for other crops.
... Lobell et al. [31][32] discussed the effects of climate change on global crop production during 1980-2008. Yuan et al. [33] analysed and predicted the change in the agricultural climate resources and the effects of climate change on the variety distribution and climatic potential productivity of spring maize from 1951 to 2100 under the future A1B climatic scenario in Northeast China. Baldos and Hertel [34] examined how agricultural productivity and climate change affect the future of global food security, and the results showed that global food security has improved in 2006-2050, mainly due to the growth in agricultural productivity. ...
Water resources are indispensable for all social-economic activities and ecosystem functions. In addition, changes in water resources have great significance for agricultural production. This paper uses five global climate models from CMIP5 to evaluate the future spatiotemporal variation in water resources in China under four RCP scenarios. The results show that the available precipitation significantly decreases due to evapotranspiration. Comparing the four RCP scenarios, the national average of the available precipitation is the highest under the RCP 2.6 and 4.5 scenarios, followed by that under the RCP 8.5 scenario. In terms of spatial distribution, the amount of available precipitation shows a decreasing trend from southeast to northwest. Regarding temporal changes, the available precipitation under RCP 8.5 exhibits a trend of first increasing and then decreasing, while the available precipitation under the RCP 6.0 scenario exhibits a trend of first decreasing and then increasing. Under the RCP 2.6 and 4.5 scenarios, the available precipitation increases, and the RCP 4.5 scenario has a higher rate of increase than that of RCP 2.6. In the context of climate change, changes in water resources and temperature cause widespread increases in potential agricultural productivity around Hu’s line, especially in southwestern China. However, the potential agricultural productivity decreases in a large area of southeastern China. Hu’s line has a partial breakthrough in the locking of agriculture, mainly in eastern Tibet, western Sichuan, northern Yunnan and northwestern Inner Mongolia. The results provide a reference for the management and deployment of future water resources and can aid in agricultural production in China.
... On the other hand, literature on maize in the NFR region indicates that local farmers has adopted new maize cultivars with longer growing cycle, which allows earlier sowing and later harvest compared with traditional local maize cultivar, and longer maize growing length has mitigated the maize yield loss (Meng et al., 2016;Zhao and Yang, 2018). Field experiments show that such adaptation measures can increase maize yield by 13-38% (Liu et al., 2012b;Chen et al., 2012;Yuan et al., 2012). From a regional perspective, another benefit from the warmer climate to the regional maize production is the northward extension of maize planting areas . ...
The Northeast Farming Region (NFR) is a major maize cropping region in China, which accounts for about 30% of national maize production. Although the regional maize production has an increasing trend in the last decades, it has greater inter-annual fluctuation. The fluctuation is caused by the increased variations of the local temperature and precipitation given the dominance of rainfed maize in the region. To secure high and stable level of maize production in the NFR under the warmer and drier future climate conditions, we employed a cross-scale model-coupling approach to identify the suitable maize cultivars and planting adaptation measures. Our simulation results show that, with proper adaptations of maize cultivars and adjustments of planting/harvest dates, both maize planting area and yield per unit of land will increase in most regions of NFR. This finding indicates that proactive adaptation can help local farmers to reap the benefits of increasing heat resource brought in by global warming, thus avoiding maize production losses as reported in other studies. This research can potentially contribute to the development of agricultural climate services to support climate-smart decisions for agricultural adaptations at the plot, farm and regional scales, in terms of planning the planting structure of multiple crops, breeding suitable maize varieties, and optimizing planting and field management schedules.
... Simulating the potential and rainfed yields are helpful for understanding the limitation of light, temperature, and rainfall resources on crop productivity (Yuan et al. 2012;He et al. 2017b). In this study, we analyzed the relationships between climate factors and simulated potential and rainfed yields and yield gaps with two types of models in the NCP over the current and warming climates. ...
Wheat productivity in the North China Plain (NCP) is highly sensitive to climate change and varies greatly in spatial-temporal scale. Contrasting responses of wheat productivity to climate change were reported with different assessment methods. In this study, the impacts of climate warming (+ 2 °C) on wheat yields and yield gaps in the NCP were compared under rainfed, irrigated, and potential conditions using climatic resource utilization model (CRUM) and APSIM. Average potential yield increased 289 kg ha⁻¹ per decade (P < 0.01) simulated by CRUM but decreased 219 kg ha⁻¹ per decade (P < 0.01) simulated by APSIM across the NCP during 1961–2010. Under the + 2 °C scenario compared with current climate (1961–2010), wheat yields under potential, two irrigations, one irrigation, and rainfed conditions increased 27%, 23%, 28%, and 13% simulated by CRUM but decreased 7%, 8%, 10%, and 17% simulated by APSIM. Simulated yield gaps between potential yield and yields under rainfed and one and two irrigations by CRUM increased 33%, 27%, and 32%, respectively. Simulated yield gap between potential and rainfed yields by APSIM increased 9% while the gaps between potential yield and yields under one and two irrigations by APSIM decreased 12% and 10%. Without cultivar change, simulated shortened growth period by APSIM due to increased temperature would decrease wheat yields. By contrast, increased temperature under a constant growth period assumed by CRUM would increase yields especially potential yield. This suggested that wheat yields could be maintained by effective utilization of crop growth duration, such as breeding new cultivars under warming climate in the NCP.
... by 2100 compared to 2000) (Riahi et al. 2011). We used a bilinear interpolation method to interpolate the RCP 8.5 gridded data to the historical stations, and the results were then validated by the observational data during -2014(Yuan et al. 2012. The annual daily air temperature, net radiation, average relative humidity, and wind speed of the historical and RCP 8.5 scenario are plotted in Fig. 2. A remarkable positive trend with a slope of 0.63°C decade −1 can be seen for T a , whereas WS and RH have significant negative trends with slopes of −0.06 m s −1 decade −1 and −0.45% decade −1 , respectively. ...
Climate change is widely accepted to be one of the most critical problems faced by the Huang-Huai-Hai Plain (3H Plain), which is a region in which there is an over-exploitation of groundwater and where future warmer and drought conditions might intensify crop water demand. In this study, the spatiotemporal patterns of ET0 and primary driving meteorological variables were investigated based on a historical and RCP 8.5 scenario daily data set from 40 weather stations over the 3H Plain using linear regression, spline interpolation method, a partial derivative analysis, and multivariate regression. The results indicated a negative trend in all the analysed periods (except spring) of the past 54 years of which only summer and the entire year were statistically significant (p < 0.01) with slopes of −1.09 and −1.29 mm a−1, respectively. In contrast, a positive trend was observed in all four seasons and the entire year under the RCP 8.5 scenario, with the biggest increment equal to 1.36 mm a−1 in summer and an annual increment of 3.37 mm a−1. The spatial patterns of the seasonal and annual ET0 exhibited the lowest values in southeastern regions and the highest values in northeastern parts of Shandong Province, probably because of the combined effects of various meteorological variables over the past 54 years. Relative humidity (RH) together with solar radiation (RS) were detected to be the main climatic factors controlling the reduction of ET0 in summer, autumn, and the entire year on the 3H Plain. ET0 in spring was mainly sensitive to changes in RS and RH, whereas ET0 in winter was most sensitive to changes in wind speed (WS) and decreased due to declining RH. Under the future RCP 8.5 scenario, the annual ET0 distribution displays a rich spatial structure with a clear northeast–west gradient and an area with low values in the southern regions, which is similarly detected in spring and summer. The most sensitive and primary controlling variables with respect to the increment of future ET0 are in the first place RS and then mean temperature in spring, while they turn to be mean temperature and then RS in summer. In autumn, future ET0 is most sensitive to RH changes. WS and RH are the controlling variables for ET0 in winter. Annual future ET0 is most sensitive to RH changes, and accordingly, RS is responsible for the predicted increment of the annual ET0. Better understanding of current and future spatiotemporal patterns of ET0 and of the regional response of ET0 to climate change can contribute to the establishment of a policy to realize a more efficient use of water resources and a sustainable agricultural production in the 3H Plain.
... Determining the impact of climate change on the potential productivity of staple crops can help in our understanding of the yieldlimiting factors involved, as well as guide appropriate measures to adapt to climate change . Crop potential productivity can be classified into four levels: photosynthetic, light-temperature, climate, and climate-soil potential productivity limited by light, light-temperature, light-temperatureprecipitation, and light-temperature-precipitationsoil fertility under the control of insect pests and weeds, and optimal agricultural management (Huang, 1985;Li et al., 2010;Yuan et al., 2012;He et al., 2014). The gaps between the light-temperature and climatic potential productivity, the climate and climate-soil potential productivity reflect the yield loss due to water stress and soil fertility stress (He et al., 2014). ...
The aim of this study is to compare the impacts of climate change on the potential productivity and potential productivity gaps of sunflower (Helianthus annuus), potato (Solanum tuberosum), and spring wheat (Triticumaestivum Linn) in the agro-pastoral ecotone (APE) of North China. A crop growth dynamics statistical method was used to calculate the potential productivity affected by light, temperature, precipitation, and soil fertility. The growing season average temperature increased by 0.47, 0.48, and 0.52°C per decade (p < 0.05) for sunflower, potato, and spring wheat, respectively, from 1981 to 2010. Meanwhile, the growing season solar radiation showed a decreasing trend (p < 0.05) and the growing season precipitation changed non-significantly across APE. The light–temperature potential productivity increased by 4.48% per decade for sunflower but decreased by 1.58% and 0.59% per decade for potato and spring wheat. The climate–soil potential productivity reached only 31.20%, 27.79%, and 20.62% of the light–temperature potential productivity for sunflower, potato, and spring wheat, respectively. The gaps between the light–temperature and climate–soil potential productivity increased by 6.41%, 0.97%, and 1.29% per decade for sunflower, potato, and spring wheat, respectively. The increasing suitability of the climate for sunflower suggested that the sown area of sunflower should be increased compared with potato and spring wheat in APE under future climate warming.
... Therefore, the sowing conditions and harvest conditions which mainly are limited by low temperature were thought to improve in most sub-regions. Thus late maturing cultivars could be grown in central parts where only can grow medium cultivars, and medium maturing cultivars could be grown in northern NFR in the future where can only grow early cultivars (Yuan et al., 2012;Liu et al., 2013a). The development of cultivation techniques will also help prolong the growing season, for example, sprout cultivation can be completed in greenhouse in early March and transplanted via machinery in May, which can provide rice an extension in the growing season ( Drought risk was thought to increase across NFR under climate change, particularly in the drier and warmer areas, such as XA, NSL, CSL and SSL (Fig. 6). ...
... Farmers that adopted drought resistant varieties had higher maize yield under dry conditions compared to that without using drought resistant varieties in 2009, and similar effects have been shown under other dry conditions by Campos et al. (2004). However, most studies about maize varieties in NFR have focused on how to get highest maize yield in non-dry conditions and adapting maize production to increasing temperature Yuan et al., 2012). Generally, the genetic improvement in maize yield is associated with increased stress tolerance, which is consistent with the improvement in genotype and management interaction (Tollenaar and Lee, 2002). ...
... Colour online. pollination and higher minimum temperature in the post-flowering phase will provide better conditions for grain filling, which helps to avoid the chilling stress caused by early frost Sun & Huang 2011;Yuan et al. 2012). However, there is some indication that an increase of minimum temperature in the flowering phase would have negative effects on wheat yield, which indicates that spring wheat is sensitive to warmer conditions in the flowering phase. ...
Crop production in the Northeast Farming Region of China (NFR) is affected considerably by variation in climatic conditions. Data on crop yield and weather conditions from a number of agro-meteorological stations in NFR were used in a mixed linear model to evaluate the impacts of climatic variables on the yield of maize (Zea maysL.), rice (Oryza sativaL.), soybean (Glycine maxL. Merr.) and spring wheat (Triticum aestivumL.) in different crop growth phases. The crop growing season was divided into three growth phases based on the average crop phenological dates from records covering 1981 and 2010 at each station, comprising pre-flowering (from sowing to just prior to flowering), flowering (20 days around flowering) and post-flowering (10 days after flowering to maturity). The climatic variables were mean minimum temperature, thermal time (which is used to indicate changes in the length of growth cycles), average daily solar radiation, accumulated precipitation, aridity index (which is used to assess drought stress) and heat degree-days index (HDD) (which is used to indicate heat stress) were calculated for each growth phase and year. Over the 1961–2010 period, the minimum temperature increased significantly in each crop growth phase, the thermal time increased significantly in the pre-flowering phase of each crop and in the post-flowering phases of maize, rice and soybean, and HDD increased significantly in the pre-flowering phase of soybean and wheat. Average solar radiation decreased significantly in the pre-flowering phase of all four crops and in the flowering phase of soybean and wheat. Precipitation increased during the pre-flowering phase leading to less aridity, whereas reduced precipitation in the flowering and post-flowering phases enhanced aridity. Statistical analyses indicated that higher minimum temperature was beneficial for maize, rice and soybean yields, whereas increased temperature reduced wheat yield. Higher solar radiation in the pre-flowering phase was beneficial for maize yield, in the post-flowering phase for wheat yield, whereas higher solar radiation in the flowering phase reduced rice yield. Increased aridity in the pre-flowering and flowering phases severely reduced maize yield, higher aridity in the flowering and post-flowering phases reduced rice yield, and aridity in all growth phases reduced soybean and wheat yields. Higher HDD in all growth phases reduced maize and soybean yield and HDD in the pre-flowering phase reduced rice yield. Such effects suggest that projected future climate change may have marked effects on crop yield through effects of several climatic variables, calling for adaptation measures such as breeding and changes in crop, soil and agricultural water management.
In the face of global warming and increasing impervious surfaces, quantifying the change of climate potential productivity (CPP) is of great significance for the food production planning. Targeting the Dongting Lake Basin, which is a key area for food production in China, this paper uses meteorological data, as well as Climate Change Initiative Land Cover, and Shuttle Radar Topography Mission digital elevation model to investigate the CPP and its changes from 2000 to 2020. The suitability of land for cultivation (SLC), and the land use/land cover change (LUCC) are also considered. The results showed that the CPP varied from 9,825 to 20,895 kg ha−1. Even though the newly added impervious surfaces indirectly resulted in the decrease of CPP by of 9.81×108 kg, overall, the CPP increased at an average rate of 83.7 kg ha−1 a−1. Global warming is the strongest driver behind CPP increase, and CPP has played an important role in the conversions between cultivated land and other land types. The structure of land types tends to be optimized against this challenge.
The climate change on the impact of grain production potential has significant regional differences. Researchers have studied the grain production potential of various crop combinations or focused on single crop types in a typical area; however, the regional differences of the climate change on the impact of grain production potential were neglected. This paper used the Global Agro-Ecological Zone (GAEZ 3.0) model to focus on the analysis what is the climate change on the impact of grain production potential in different geographic units (Northern Shaanxi Plateau, Guanzhong Basin, Qinba Mountain) in Shaanxi Province of China. The case showed that the precipitation (Pre) what made changes of grain production potential was the most important factor in different geographic units. The increase of Pre had a positive impact on the grain production potential in Northern Shaanxi Plateau and Guanzhong Basin. However, in Qinba Mountain, due to excessive Pre in the Qinba Mountains, the decrease of Pre had a certain positive impact on the grain production potential. The precipitation was less in the Northern Shaanxi Plateau; therefore, its major factors leading to changes of crop production were precipitation and rainfall days. The increase of the mean maximum temperature (Tmx) and the mean minimum temperature (Tmn) had a positive impact of the grain production potential in the Northern Shaanxi Plateau and Guanzhong Basin. The higher temperature had a negative impact on the grain production potential. In Qinba Mountain, the increase of the temperature has a certain negative impact on the grain production potential. It has more influence of Tmx in the Guanzhong Basin and Qinba Mountain rather than that in the Northern Shaanxi Plateau. Generally speaking, the major climatic factors leading grain production potential were Pre and Tmx in Guanzhong Basin and Qinba Mountain.
Grain production potential (GrPP) is the maximum production in 1 year that can be achieved by land use under the limitations of climate conditions and in the absence of pests and diseases and other factors. Regional GrPP can change over time and there is an urgent need to identify the main factors affecting regional differences in such changes. Therefore, changes in GrPP were studied for six geographical units in Shaanxi Province, with summer maize and winter wheat as the main grain crops. Changes of GrPP during 2000–2015 were simulated by the global aro-ecological zone model. Analysis of modelled GrPP driven by observed changes in climate and land use suggest that over this period GrPP increased to the north but declined to the south of the Qinling Mountains. This is driven mainly by past changes in climate, with modelled GrPP more sensitive to changes in precipitation than temperature in all geographical units except one. Climate change was the main factor affecting GrPP in all geographical units except one; however, model prediction suggests that land use changes had a clear yield-reducing effect in three of the units. It is the conversion from cultivated land to construction land, grassland and woodland that led to the greatest declines in GrPP in these three geographical units. In order to ensure the stable development of regional agriculture and food security, Shaanxi Province should focus on tapping GrPP north of the Qinling Mountains and increasing the conversion rate of GrPP to actual production.
In this study, the spatial distribution and changing trends of agricultural heat and precipitation resources in Northeast China were analyzed to explore the impacts of future climate changes on agroclimatic resources in the region. This research is based on the output meteorological data from the regional climate model system for Northeast China from 2005 to 2099, under low and high radiative forcing scenarios RCP4.5 (low emission scenario) and RCP8.5 (high emission scenario) as proposed in IPCC AR5. Model outputs under the baseline scenario, and RCP4.5 and RCP8.5 scenarios were assimilated with observed data from 91 meteorological stations in Northeast China from 1961 to 2010 to perform the analyses. The results indicate that: (1) The spatial distribution of temperature decreases from south to north, and the temperature is projected to increase in all regions, especially under a high emission scenario. The average annual temperature under the baseline scenario is 7.70°C, and the average annual temperatures under RCP4.5 and RCP8.5 are 9.67°C and 10.66°C, respectively. Other agricultural heat resources change in accordance with temperature changes. Specifically, the first day with temperatures ≥10°C arrives 3 to 4 d earlier, the first frost date is delayed by 2 to 6 d, and the duration of the growing season is lengthened by 4 to 10 d, and the accumulated temperature increases by 400 to 700°C·d. Water resources exhibit slight but not significant increases. (2) While the historical temperature increase rate is 0.35°C/10a, the rate of future temperature increase is the highest under the RCP8.5 scenario at 0.48°C/10a, compared to 0.19°C/10a under the RCP4.5 scenario. In the later part of this century, the trend of temperature increase is significantly faster under the RCP8.5 scenario than under the RCP4.5 scenario, with faster increases in the northern region. Other agricultural heat resources exhibit similar trends as temperature, but with different specific spatial distributions. Precipitation in the growing season generally shows an increasing but insignificant trend in the future, with relatively large yearly fluctuations. Precipitation in the eastern region is projected to increase, while a decrease is expected in the western region. The future climate in Northeast China will change towards higher temperature and humidity. The heat resource will increase globally, however its disparity with the change in precipitation may negatively affect agricultural activities.
The Three-North Shelter Forest Program (TNSFP), which is the largest ecological afforestation program worldwide, was launched in 1978 and will last until 2050 in the Three-North regions (accounting for 42.4% of China's territory). As a dominant component in the TNSFP, shelterbelts or windbreaks play an important role in preventing from wind damage and erosion and providing appropriate microclimate conditions for crop growth, thus improving crop yields. However, how shelterbelts influence crop yields at the regional scale has not yet been determined because there are certain difficulties in identifying the effects of shelterbelts on crop yields due to other factors such as climatic factors, crop seeds, fertilizer and management measures. In this study, a new approach is used to estimate the effects of shelterbelts on crop yields while overcoming these difficulties. The specific processes used in this study are detailed as follows. First, the climatic potential productivity, which is a combination of solar radiation, temperature and precipitation, was estimated using the multi-sensor remote data. All farmland in the region was divided into high, middle and low climatic potential productivity zones. Second, the crop (i.e., maize) yield across the Northeast China was estimated using the harvest index method and MODIS data. Third, according to the effectively protected distance, the levels of protection provided by the shelterbelts to the farmland at the regional scale were calculated by combining the stand age and the growth status of the shelterbelts using a time series of Landsat images. Finally, the levels of protection and the corresponding maize yields in pixels were extracted and averaged to identify the effects of shelterbelts on crop yields. The results of this study indicated that shelterbelts could enhance crop yields at the regional scale. The contribution rates of shelterbelts to increasing maize yields were found to be 4.68%, 4.28% and 9.45% in the high, middle and low climatic potential productivity zones, respectively. In Northeast China, the average level of protection of farmland was 18.28%, which was obviously lower than the optimal level of protection (i.e., approximately 80%); thus, many shelterbelts must be planted in the future. The findings of this study provide a sound theoretical foundation for increasing crop yields by planning shelterbelts in farmland regions similar to those in Northeast China.
Exploring the dynamics of the utilization of agricultural climatic resources (i.e., environmental factors that affect crop productivity such as light, temperature, and water) can provide a theoretical basis for modifying agricultural practices and distributions of agricultural production in the future. Northeast China is one of the major agricultural production areas in China and also an obvious region of climatic warming. We were motivated to analyze the utilization dynamics of agricultural climatic resource during spring maize cultivation from 1961 to 2010 in Northeast China. To understand these dynamics, we used the daily data from 101 meteorological stations in Northeast China between 1961 and 2010. The demands on agricultural climatic resources in Northeast China imposed by the cultivation of spring maize were combined and agricultural climatic suitability theory was applied. The growth period of spring maize was further detailedly divided into four stages: germination to emergence, emergence to jointing, jointing to tasseling, and tasseling to maturity. The average resource utilization index was established to evaluate the effects. Over the past five decades, Northeast China experienced increases in daily average temperature of 0.246. °C every decade during the growing season (May-September). At the same time, strong fluctuating decreases were observed in average total precipitation of 8.936. mm every decade and an average sunshine hour of 0.122. h every decade. Significant temporal and spatial changes occurred in K from 1961 to 2010. The K showed decreasing trends in Liaoning province and increasing trends in Jilin and especially in Heilongjiang province, which increased by 0.11. Spatial differences were visible in different periods, and the most obvious increase was found in the period 2001-2010. The areas with high values of K shifted northeastward over the past 50. years, indicating more efficient use of agricultural climatic resources in Northeast China.
Projections of climate change impacts on crop yields are subject to uncertainties, and quantification of such uncertainty is essential for the effective use of the projection results for adaptation and mitigation purposes. This work analyzes the uncertainties in maize yield predictions using two crop models together with three climate projections downscaled with one regional climate model nested with three global climate models under the A1B emission scenario in northeast China (NEC). Projections were evaluated for the Zhuanghe agrometeorological station in NEC for the 2021-50 period, taking 1971-2000 as the baseline period. The results indicated a yield reduction of 13% during 2021-50, with 95% probability intervals of (-41%, +12%) relative to 1971-2000. Variance decomposition of the yield projections showed that uncertainty in the projections caused by climate and crop models is likely to change with prediction period, and climate change uncertainty generally had a larger impact on projections than did crop model uncertainty during the 2021-50 period. In addition, downscaled climate projections had significant bias that can introduce significant uncertainties in yield projections. Therefore, they have to be bias corrected before use.
Understanding regional relationships between climate change and crop yield will help with making the strategic decisions for food security in China under climate change. In this study, the contributions of climate change to spring maize yield over the past three decades in Northeast China were decoupled based on the daily climate variables gathered from 68 meteorological stations and detailed observed data of spring maize from 55 agricultural meteorological experimental stations for the period 1978–2010 in Northeast China, analyzed with a linear statistical model. Then, the key climatic factors limiting the climate-induced yield of spring maize were identified. The agro-climatic similarity theory was applied. Finally, the relationships between the climatic variables and the climate-induced yield of spring maize were further explored by provinces. The results show that: from 1978 to 2010, the observed yields of spring maize in Northeast China increased markedly, with inter-annual fluctuations. Compared with the methods of moving average and harmonic average, Logistic regression optimally decoupled the climate-induced yield of spring maize. The key meteorological factors limiting the climate-induced yield were temperature, precipitation and sunshine, varying in the different regions. In Heilongjiang Province, the climate-induced yields of spring maize were mainly affected by maximum temperatures in August and precipitation in June. In Jilin Province, climate-induced yield was closely related to precipitation during daily the average temperature stably passing 10 °C (≥10 °C). In Liaoning Province, when the maximum temperature was high and the sunshine was abundant in June, the climate-induced yield of spring maize significantly increased. Finally, the regression models between climatic variables and climate-induced yield of spring maize in 11 representative zones in Northeast China also established geographical differences.
Agricultural climatic resources (such as light, temperature, and water) are environmental factors that affect crop productivity. Predicting the effects of climate change on agricultural climatic resource utilization can provide a theoretical basis for adapting agricultural practices and distributions of agricultural production. This study investigates these effects under the IPCC (Intergovernmental Panel on Climate Change) scenario A1B using daily data from the high-resolution RegCM3 (0.25°×0.25°) during 1951–2100. Model outputs are adjusted using corrections derived from daily observational data taken at 101 meteorological stations in Northeast China between 1971 and 2000. Agricultural climatic suitability theory is used to assess demand for agricultural climatic resources in Northeast China during the cultivation of spring maize. Three indices, i.e., an average resource suitability index (I
sr), an average efficacy suitability index (I
se), and an average resource utilization index (K), are defined to quantitatively evaluate the effects of climate change on climatic resource utilization between 1951 and 2100. These indices change significantly in both temporal and spatial dimensions in Northeast China under global warming. All three indices are projected to decrease in Liaoning Province from 1951 to 2100, with particularly sharp declines in I
sr, I
se, and K after 2030, 2021, and 2011, respectively. In Jilin and Heilongjiang provinces, I
sr is projected to increase slightly after 2011, while I
se increases slightly and K decreases slightly after 2030. The spatial maxima of all three indices are projected to shift northeastward. Overall, warming of the climate in Northeast China is expected to negatively impact spring maize production, especially in Liaoning Province. Spring maize cultivation will likely need to shift northward and expand eastward to make efficient use of future agricultural climatic resources.
Based on gridded meteorological data for the period 1981–2100 from the RegCM3 regional model, the changing trends of climatic resources in Northeast China are analyzed, and the distributions of maize varieties are accordingly adjusted. In order to explore the effects of different adaptation countermeasures on climatic productivity and meteorological suitability in the future, maize cultivars with resistance to high temperatures and/or drought are selected. The results show that, in the future, there is likely to be a significant increase in thermal resources, and potential atmospheric evaporation will increase correspondingly. Meanwhile, radiation is predicted to increase significantly during 2041–2070 in the growing season. However, changes in precipitation are unlikely to be sufficient enough to offset the intensification in atmospheric evaporation caused by the temperature increase. Water resources and high temperatures are found to be the two major factors constraining grain yield. The results also show that the warming climate will be favorable for maize production where thermal resources are already limited, such as in central and northern Heilongjiang Province and eastern Jilin Province; while in areas that are already relatively warm, such as Liaoning Province, climatic productivity will be reduced. The climatic productivity and the meteorological suitability of maize are found to improve when the planting of resistant varieties is modeled. The utilization of agricultural climatic resources through the adaptation countermeasures of maize varieties is to increase obviously with time. Specifically, maize with drought-resistant properties will have a marked influence on meteorological suitability during 2011–2070, with suitable areas expanding. During 2071–2100, those maize varieties with their upper limit of optimum temperature and maximum temperature increased by 2°, or water requirement reduced to 94%, or upper limit of optimum temperature and maximum temperature increased by 1°C and water requirement reduced to 98%, all exhibit significant differences in climatic potential productivity, compared to the present-day varieties. The meteorological suitability of maize is predicted to increase in some parts of Heilongjiang Provine, with the eastern boundary of the “unavailable” area shifting westward.
To learn the effects of climate change on cultivation patterns of spring maize and its suitability will benefit the strategic decisions for future agricultural adaptation. In this paper, based on the daily data from 68 meteorological stations and 82 agro-meteorological observation stations in Northeast China between 1961 and 2010, the cultivation pattern of spring maize and its climatic suitability in Northeast China were investigated. The agricultural climatic suitability theory was applied. The specific growth phases of spring maize that were most sensitive to environmental limitations were further divided into four stages: from germination to emergence, from emergence to jointing, from jointing to tasseling, and from tasseling to maturity. The average resource suitability index (Isr) was established to evaluate the effects. Higher values of Isr indicate a higher degree of climatic resource suitability. Over the past five decades, the northern planting boundaries of different maturities (late, medium-late, medium, medium-early and early) of spring maize varieties in Northeast China all markedly extended northward and eastward. Of all the varieties, the medium-late maturity variety had the most expanded planting area. This further illustrated the importance of promoting medium-late range heat-tolerant cultivars of spring maize in reducing the unfavorable effect of climate change in the near future in Northeast China. In addition, the most significant extension was found in the early 21st century. Moreover, the southern planting boundaries of unsuitable planting spring maize areas continually compressed northward from the Tonghe County of Heilongjiang Province (128°49′, 46°21′) to the Huma County of Heilongjiang Province (124°11′, 51°26′). Climate change affected not only the planting patterns of spring maize, but also the climatic suitability of spring maize. Significant temporal and spatial changes of Isr from 1961 to 2010 were found. The Isr showed increasing trends, which increased by 0.19 in Heilongjiang Province, 0.16 in Jilin Province and 0.12 in Liaoning Province. Spatial differences of Isr were obvious, with high values shifting northeastward over the past 50 years, indicating more efficient suitability of agricultural climatic resources in Northeast China.
Model simulation is an important way to study the effects of climate change on agriculture. Such assessment is subject to
a range of uncertainties because of either incomplete knowledge or model technical uncertainties, impeding effective decision-making
to climate change. On the basis of uncertainties in the impact assessment at different levels, this article systematically
summarizes the sources and propagation of uncertainty in the assessment of the effect of climate change on agriculture in
terms of the climate projection, the assessment process, and the crop models linking to climate models. Meanwhile, techniques
and methods focusing on different levels and sources of uncertainty and uncertainty propagation are introduced, and shortcomings
and insufficiencies in uncertainty processing are pointed out. Finally, in terms of how to accurately assess the effect of
climate change on agriculture, improvements to further decrease potential uncertainty are suggested.
Keywordsclimate change–agriculture–impact assessment–uncertainty–model simulation
Based on continuous three-year measurements (from 2004 to 2007) of eddy covariance and related environmental factors, environmental
controls on variation in soil respiration (R
s) during non-growing season were explored in a maize agroecosystem in Northeast China. Our results indicated that during non-growing
seasons, daily R
s was 1.08–4.08 g CO2 m−2 d−1, and the lowest occurred in late November. The average R
s of non-growing season was 456.06 ± 20.01 g CO2 m−2, accounting for 11% of the gross primary production (GPP) of the growing season. Additionally, at monthly scale, the lowest
value of R
s appeared in January or February. From the beginning to the end of non-growing season, daily R
s tended to decrease first, and then increase to the highest. There was a significant quadratic curve relationship between
R
s and soil temperature at 10 cm depth when soil temperature was more than 0°C (P<0.001), with the explaining ratio of 38%–70%. When soil water content was more than 0.1 m3 m−3, soil moisture at 10 cm depth was significantly parabolically correlated with R
s (P<0.001), explaining the rate of 18%–60%. Based on all the data of soil temperature of more than 0°C, a better model for R
s was established by coupling soil temperature and moisture, which could explain the rate of up to 53%–79%. Meanwhile, the
standard error of regression estimation between the values of prediction and observation for R
s could reach 2.7%–11.8%. R
s in non-growing season can account for 22.4% of R
s in growing season, indicating that it plays a critical role in assessing the carbon budget in maize agroecosystem, Northeast
China.
Keywordssoil respiration-non-growing season-soil temperature-soil water content-maize
It is widely acknowledged that northern arid region is agricultural production base considered as the most promising potential area. Land potential productivity (LPP) as land essential attribute is drawing increasing attention. Based on the climate data from 1967 to 1999, soil physiochemical data and maps, with the support of geographic information system (GIS), LPP was computed according to the factors such as radiation, temperature, precipitation, and soil using mechanism method. The results show that water deficiency is the main limiting factor of climatic potential productivity, because climatic potential productivity becomes one third of light-temperature potential productivity according to water correction coefficient; the distribution characteristic of LPP is influenced by many factors, which is same as distribution characteristic of landform. The value of land potential productivity in the basin valley is the highest, and the value in mountain is the lowest.
The potential climate productivity refers to the proper highest biological yield or agricultural yield in per unit area when climatic resources such as the light, heat, and water are fully used while the other conditions such as soil, nutrient, carbon dioxide, and so on are under the most suitable status. Gansu Province is the main dry land farming agricultural region in northwestern China. Because of the low natural productivity, its agricultural production still partly relies on climatic conditions, mainly depending on the environmental factors such as the light, heat and water resources and their changes. It has very important theoretical and practical significance in reasonably using of climatic resources, fully displaying the potential climatic productivity, improving productivity application level, and providing valuable advices for agricultural production to study the potential climatic dynamics and its main influence factors. Based on the temperature and precipitation data in 69 meteorological stations during 1971 to 2007 in Gansu province, the temporal and spatial distribution of the potential temperature productivity, precipitation productivity, and climatic productivity and their dynamics were analyzed using Miami model and Thornthwaite Memorial model. At the same time, the spatial and temporal dynamics characteristics of potential climatic productivity in the recent 40 years were analyzed using the EOF function and the Mann-Kendall statistical method. Also, the driving forces to the dynamics of potential climatic productivity in Gansu province were analyzed. The results showed that the potential temperature productivity was significantly increased while the potential precipitation productivity was slightly decreased in the recent 40 years with their conversion year in 1997and 1994. The mean potential climatic productivity in Hexi Corridor, Gannan Grassland, Middle Gansu Plateau, Eastern Gansu Plateau, Southern Gansu province were 313. 36, 741.72, 763.85, 867.52, 982. 86 kg•hm-2••a-1, respectively. Besides, the potential climatic productivity was obviously increased from 1979 to 1996 with its conversion year in 1997, and the conversion year was 1997 while significantly and continually decreased from 1997 to 2007. The correlation coefficients between the potential climatic productivity and the mean annual precipitation, and between the potential productivity and the mean annual temperature were 0. 94 and 0.04, respectively. Therefore, the precipitation was the key factor to determine the potential climate productivity in Gansu province. The spatial distribution of potential climatic productivity in Gansu province was decreased from southeast to northwest, the minimum and maximum values marked in Dunhuang with 74. 52 kg•hm-2••a-1 and Huixian with 1094.39 kg•hm-2••a-1, respectively, while the mean potential climate productivity of the whole province was 733. 86 kg•hm-2••a-1 According to the average distribution of potential climatic productivity, the maximum value was obtained in Southern Gansu province, followed by Eastern Gansu Plateau, Middle Gansu Plateau, Gannan Grassland and Hexi Corridor in order. furthermore, both temperature and humidity increasing were beneficial to the agricultural production in the whole province with the temperature increasing effect of 5. 51 -25. 34 kg•hm-2••a-1 and the humidity increasing effect of 27. 89 -34.49 kg•hm-2••a-1, and the humidity increasing effect was much more significant, especially having significant promotion to agricultural development in Hexi Corridor. Therefore, the precipitation was the main driving force to influence the potential climate productivity in Gansu province. In addition, the warmer and drier climatic changing trend can aggravate to the reduction of the potential climate productivity.
Northeast China is one of the highest latitude areas in China, so the low temperature disaster during the maize growing season was the main agro-meteorological disasters which affected the maize production in Northeast China. Crop models can numerically simulate the relations between the important physiological and ecological processes of the crops and meteorology and soil, and reproduce crop growth processes. Therefore, based on the suitability test of WOFOST model in simulating the growth of maize in Northeast China, the paper analyzed the influence of the fluctuation of temperature on maize yields in recent 46 years in Northeast China, results showed that there was the same trends in the fluctuation of maize yields in Heilongjiang, Jilin and Liaoning, the fluctuation decreases with the increase in years, the largest fluctuation occurred in Heilongjiang province, with the value ranging between-20%- 12% ,and smallest fluctuation occurred in Liaoning province, with the value ranging about-15%-8%.
The novel technology for synthesis of 1,2-propylene glycol by dehydration-hydrogenation of glycerol at ambient hydrogen pressure was reported. Dehydration of glycerol to acetol was catalyzed by Cu/Al(2)O(3) catalyst. A 86% selectivity for acetol was achieved at 220 degrees C and ambient hydrogen pressure. The effects of copper loading, reaction temperature, and reaction atmosphere on the catalyst performance were studied. Raney Ni catalyst showed excellent catalytic performance in the hydrogenation of acetol to 1,2-propylene glycol. The selectivity for 1,2-propylene glycol was more than 99% at 120 degrees C and ambient hydrogen pressure, and no appreciable change in the activity was observed over the Raney Ni catalyst after 5 h of time-on-stream. The influence of reaction conditions on the conversion and selectivity was also investigated in the hydrogenation of acetol.
Based on the 2011-2050 A2 climate scenario derived from the regional climate model PRECIS and the daily data of 1961-1990 baseline climate condition, this paper analyzed the possible changes of the agricultural thermal resources in China from 2011 to 2050. Comparing with the baseline climate condition in 1961-1990, the average frost-free periods in most parts of China in 2011-2050 under A2 climate scenario would have an obvious extension, mainly manifested in the advance of last frost date and the postpone of first frost date. The days with the daily average temperature stably passing 0 degrees C would also prolong significantly, and extend from 1 day to 14 days in most parts of the country. Especially from 2041 to 2050, the days with the daily average temperature stably passing 0 degrees C in most regions of Qinghai-Tibet Plateau, middle and lower reaches of Yangtze River, and western and southwestern regions of Gansu and Xinjiang could be extended by 49 days. The > or = 0 degrees C accumulated temperatures in most parts of the country would have increasing trends. In order to meet the future change trend of our agricultural thermal resources and to realize the sustainable development of agriculture in China, some countermeasures should be formulated, e.g., further adjusting agricultural cropping system, optimizing agricultural production distribution, developing biotechnology, and so on.
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