scholarly journals Planning maize hybrids adaptation to future climate change by integrating crop modelling with machine learning

2021 ◽  
Author(s):  
Liangliang Zhang ◽  
Zhao Zhang ◽  
Fulu Tao ◽  
Yuchuan Luo ◽  
Juan Cao ◽  
...  
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Grain Filling ◽  
Growth Cycle ◽  
Future Climate Change ◽  
Crop Modelling ◽  
Maize Productivity ◽  
Accelerate Development ◽  
Negative Impacts ◽  
Hybrid Adaptation

Abstract Crop hybrid improvement is an efficient and environmental-friendly option to adapt to climate change and increase grain production. However, the adaptability of existing hybrids to a changing climate has not been systematically investigated. Therefore, little is known about the appropriate timing of hybrid adaptation. Here, using a novel hybrid model which coupled CERES-Maize with machine learning, we critically investigated the impacts of climate change on maize productivity with an ensemble of hybrid-specific estimations in China. We determined when and where current hybrids would become unviable and hybrid adaptation need be implemented, as well as which hybrid traits would be desirable. Climate change would have mostly negative impacts on maize productivity, and the magnitudes of yield reductions would highly depend on the growth cycle of the hybrids. Hybrid replacement could partially, but not completely, offset the yield loss caused by projected climate change. Without adaptation, approximately 53% of the cultivation areas would require hybrid renewal before 2050 under the RCP 4.5 and RCP 8.5 emission scenarios. The medium-maturing hybrids with a long grain-filling duration and a high light use efficiency would be promising, although the ideotypic traits could be different for a specific environment. The findings highlight the necessity and urgency of breeding climate resilient hybrids, providing policy-makers and crop breeders with the early signals of when, where and what hybrids will be required, which stimulate proactive investment to facilitate breeding. The proposed crop modelling approach is scalable, largely data-driven and can be used to tackle the longstanding problem of predicting hybrids’ future performance to accelerate development of new crop hybrids.

scholarly journals A demographic approach to study effects of climate change in desert plants

2012 ◽  
Vol 367 (1606) ◽  
pp. 3100-3114 ◽  
Author(s):  
Roberto Salguero-Gómez ◽  
Wolfgang Siewert ◽  
Brenda B. Casper ◽  
Katja Tielbörger
Keyword(s):  
Climate Change ◽  
Life History Strategies ◽  
Future Climate Change ◽  
Desert Plant ◽  
Desert Plants ◽  
Population Growth Rates ◽  
Growing Seasons ◽  
Mechanistic Approach ◽  
Negative Impacts ◽  
Variable Precipitation

Desert species respond strongly to infrequent, intense pulses of precipitation. Consequently, indigenous flora has developed a rich repertoire of life-history strategies to deal with fluctuations in resource availability. Examinations of how future climate change will affect the biota often forecast negative impacts, but these—usually correlative—approaches overlook precipitation variation because they are based on averages . Here, we provide an overview of how variable precipitation affects perennial and annual desert plants, and then implement an innovative, mechanistic approach to examine the effects of precipitation on populations of two desert plant species. This approach couples robust climatic projections, including variable precipitation, with stochastic, stage-structured models constructed from long-term demographic datasets of the short-lived Cryptantha flava in the Colorado Plateau Desert (USA) and the annual Carrichtera annua in the Negev Desert (Israel). Our results highlight these populations' potential to buffer future stochastic precipitation. Population growth rates in both species increased under future conditions: wetter, longer growing seasons for Cryptantha and drier years for Carrichtera . We determined that such changes are primarily due to survival and size changes for Cryptantha and the role of seed bank for Carrichtera . Our work suggests that desert plants, and thus the resources they provide, might be more resilient to climate change than previously thought.

scholarly journals Application of Machine Learning Techniques to Delineate Homogeneous Climate Zones in River Basins of Pakistan for Hydro-Climatic Change Impact Studies

2020 ◽  
Vol 10 (19) ◽  
pp. 6878
Author(s):  
Ammara Nusrat ◽  
Hamza Farooq Gabriel ◽  
Sajjad Haider ◽  
Shakil Ahmad ◽  
Muhammad Shahid ◽  
...  
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
River Basins ◽  
Precipitation Change ◽  
Future Climate ◽  
Machine Learning Techniques ◽  
Future Climate Change ◽  
Four Seasons ◽  
Learning Techniques ◽  
Change Impact

Climatic data archives, including grid-based remote-sensing and general circulation model (GCM) data, are used to identify future climate change trends. The performances of climate models vary in regions with spatio-temporal climatic heterogeneities because of uncertainties in model equations, anthropogenic forcing or climate variability. Hence, GCMs should be selected from climatically homogeneous zones. This study presents a framework for selecting GCMs and detecting future climate change trends after regionalizing the Indus river sub-basins in three basic steps: (1) regionalization of large river basins, based on spatial climate homogeneities, for four seasons using different machine learning algorithms and daily gridded precipitation data for 1975–2004; (2) selection of GCMs in each homogeneous climate region based on performance to simulate past climate and its temporal distribution pattern; (3) detecting future precipitation change trends using projected data (2006–2099) from the selected model for two future scenarios. The comprehensive framework, subject to some limitations and assumptions, provides divisional boundaries for the climatic zones in the study area, suitable GCMs for climate change impact projections for adaptation studies and spatially mapped precipitation change trend projections for four seasons. Thus, the importance of machine learning techniques for different types of analyses and managing long-term data is highlighted.

scholarly journals Vulnerability of Water Resources to Climate Change: Adaptation and Resilience Strategies for Sustainable Development in Nigeria

2020 ◽  
Vol 3 (2) ◽  
Keyword(s):  
Climate Change ◽  
Sustainable Development ◽  
Economic Development ◽  
Water Resources ◽  
Economic Value ◽  
Future Climate Change ◽  
Industrial Sectors ◽  
Run Off ◽  
Increasing Demand ◽  
Negative Impacts

Nigeria faces inexorable climate change in recent times. This phenomenon will have a profound effect on the long-term sustainable socio-economic development and is also likely to jeopardize achievement of economic development of the country. All economic and social sectors will be adversely affected. The water resources sector is one that will be strongly impacted by climate change. Against a background of increasing demand for potable water, sea-level rise may lead to flooding of lowlands and seawater intrusion into coastal aquifers, while variability in climate may see more intense rainstorms resulting both in increased run-off leading to increased flooding and reduced recharge leading to aquifer depletion. Such impacts are already having negative ripple effects on other vital aspects of the economy such as the tourism, recreational, agricultural and industrial sectors. Unfortunately, adequate management of water resources in Nigeria is sorely lacking. Extensive studies to quantify the likely impacts of future climate change and climate variability on water resources in Nigeria are not available. In many cases, baseline data which may be used to track changes are sparse or non-existent. The impacts of climate change and economic value of water resources will form the basis for the development of adaptation strategies with regards to the sustainable management of regional and national water resources. This paper therefore explores the probable effect climate change will have on water resources in Nigeria, the fall-out from these effects and strategies for mitigating potential negative impacts for sustainable development.

scholarly journals Assessing Soil Organic Carbon Stock Dynamics under Future Climate Change Scenarios in the Middle Qilian Mountains

Forests ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1698
Author(s):  
Wei Liu ◽  
Meng Zhu ◽  
Yongge Li ◽  
Jutao Zhang ◽  
Linshan Yang ◽  
...  
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Organic Carbon ◽  
Qilian Mountains ◽  
Future Climate ◽  
Future Climate Change ◽  
Climate Change Scenarios ◽  
Machine Learning Model ◽  
Soc Stock ◽  
The Mean

Soil organic carbon (SOC) simply cannot be managed if its amounts, changes and locations are not well known. Thus, evaluations of the spatio-temporal dynamics of SOC stock under future climate change are crucial for the adaptive management of regional carbon sequestration. Here, we evaluated the dynamics of SOC stock to a 60 cm depth in the middle Qilian Mountains (1755–5051 m a.s.l.) by combining systematic measurements from 138 sampling sites with a machine learning model. Our results reveal that the combination of systematic measurements with the machine learning model allowed spatially explicit estimates of SOC change to be made. The average SOC stock in the middle Qilian Mountains was expected to decrease under future climate change, while the size and direction of SOC stock changes seemed to be elevation-dependent. Specifically, in comparison with the 2000s, the mean annual precipitation was projected to increase by 18.37, 19.80 and 30.80 mm, and the mean annual temperature was projected to increase by 1.9, 2.4 and 2.9 °C under the Representative Concentration Pathway (RCP) 2.6 (low-emissions pathway), RCP4.5 (low-to-moderate-emissions pathway), and RCP8.5 (high-emissions pathway) scenarios by the 2050s, respectively. Accordingly, the area-weighted SOC stock and total storage for the whole study area were estimated to decrease by 0.43, 0.63 and 1.01 kg m–2 and 4.55, 6.66 and 10.62 Tg under the RCP2.6, RCP4.5 and RCP8.5 scenarios, respectively. In addition, the mid-elevation zones (3100–3900 m), especially the subalpine shrub-meadow Mollic Leptosols, were projected to experience the most intense carbon loss. However, the higher elevation zones (>3900 m), especially the alpine desert zone, were characterized by significant carbon accumulation. As for the low-elevation zones (<2900 m), SOC was projected to be less varied under future climate change scenarios. Thus, the mid-elevation zones, especially the subalpine shrub-meadows and Mollic Leptosols, should be given priority in terms of reducing CO2 emissions in the Qilian Mountains.

scholarly journals Separating the impacts of heat stress events from rising mean temperatures on winter wheat yield of China

2021 ◽  
Author(s):  
Bing Liu ◽  
Dongzheng Zhang ◽  
Huxing Zhang ◽  
Senthold Asseng ◽  
Tingwei Yin ◽  
...  
Keyword(s):  
Climate Change ◽  
Heat Stress ◽  
Climate Warming ◽  
Management Practices ◽  
Crop Yields ◽  
Wheat Yield ◽  
Grain Filling ◽  
Growing Season ◽  
Historical Climate ◽  
Negative Impacts

Abstract Warming due to climate change has profound impacts on regional crop yields, and this includes impacts from rising mean growing season temperature and heat stress events. Adapting to these two impacts could be substantially different, and the overall contribution of these two factors on the effects of climate warming and crop yield is not known. This study used the improved WheatGrow model, which can reproduce the effects of temperature change and heat stress, along with detailed information from 19 location-specific cultivars and local agronomic management practices at 129 research stations across the main wheat-producing region of China, to quantify the regional impacts of temperature increase and heat stress separately on wheat in China. Historical climate, plus two future low-warming scenarios (1.5/2.0oC warming above pre-industrial) and one future high-warming scenario (RCP8.5), were applied using the crop model, without considering elevated CO2 effects. The results showed that heat stress and its yield impact were more severe in the cooler northern sub-regions than the warmer southern sub-regions with historical and future warming scenarios. Heat stress was estimated to reduce wheat yield in most of northern sub-regions by 2.0% - 4.0% (up to 29% in extreme years) under the historical climate. Climate warming is projected to increase heat stress events in frequency and extent, especially in northern sub-regions. Surprisingly, higher warming did not result in more yield-impacting heat stress compared to low-warming, due to advanced phenology with mean warming and finally avoiding heat stress events during grain filling in summer. Most negative impacts of climate warming are attributed to increasing mean growing-season temperature, while changes in heat stress are projected to reduce wheat yields by an additional 1.0% to 1.5% in northern sub-regions. Adapting to climate change in China must consider the different regional and temperature impacts to be effective.

scholarly journals Impact of global warming (1.5ºC) on the productivity of selected C3 and C4 crops across Tamil Nadu

2021 ◽  
Vol 22 (1) ◽  
pp. 7-17
Author(s):  
R. GOWTHAM ◽  
K. BHUVANESHWARI ◽  
A. SENTHIL ◽  
M. DHASARATHAN ◽  
AROMAR REVI ◽  
...  
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Tamil Nadu ◽  
Rice Yield ◽  
Climate Change Impacts ◽  
Crop Phenology ◽  
Maize Productivity ◽  
C4 Pathway ◽  
Rcp 8.5 ◽  
Negative Impacts

Over the last century, mean annual temperatures increased by ~1°C. UNFCCC has proposed to limit warming below 1.5°C relative to pre-industrial levels. A study was conducted on rice (C3 pathway) and maize (C4 pathway) over Tamil Nadu using DSSAT to understand the climate change impacts with projected temperature increase of 1.5°C.The future climate under RCP 4.5 and RCP 8.5 indicated 1.5°Cincrease in temperature to happen by 2053 and 2035, respectively over Tamil Nadu.Annual rainfall deviations in RCP4.5 showed drier than current condition and RCP8.5 projected wetter SWM and drier NEM (90 % of current rainfall).Impact of 1.5°C warming on crop phenology indicated 8 days reduction in duration for rice and maize. The W UE of rice would decrease by 17 per cent at current CO2 whereas, enrichment (430 ppm) would reduce by12 per cent and rice yield is reduced by 21 per cent with 360 ppm CO2 and 430 ppm reducedby 17 per cent. There is no considerable varaition (- 5 to 1 %) in maize productivity with 1.5 ºC warming. The above results indicated that 1.5 ºC warming has more negative impacts on plants with C3 compared to C4 pathway

scholarly journals Prediction of Groundwater Level Variations in a Changing Climate: A Danish Case Study

2021 ◽  
Vol 10 (11) ◽  
pp. 792
Author(s):  
Rebeca Quintero Gonzalez ◽  
Jamal Jokar Arsanjani
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Water Level ◽  
Water Table ◽  
Water Levels ◽  
Series Data ◽  
Future Climate Change ◽  
Support Vector ◽  
Climate Change Scenarios ◽  
The Future

Shallow groundwater is a key resource for human activities and ecosystems, and is susceptible to alterations caused by climate change, causing negative socio-economic and environmental impacts, and increasing the need to predict the evolution of the water table. The main objective of this study is to gain insights about future water level changes based on different climate change scenarios using machine learning algorithms, while addressing the following research questions: (a) how will the water table be affected by climate change in the future based on different socio-economic pathways (SSPs)?: (b) do machine learning models perform well enough in predicting changes of the groundwater in Denmark? If so, which ML model outperforms for forecasting these changes? Three ML algorithms were used in R: artificial neural networks (ANN), support vector machine (SVM) and random forest (RF). The ML models were trained with time-series data of groundwater levels taken at wells in the Hovedstaden region, for the period 1990–2018. Several independent variables were used to train the models, including different soil parameters, topographical features and climatic variables for the time period and region selected. Results show that the RF model outperformed the other two, resulting in a higher R-squared and lower mean absolute error (MAE). The future prediction maps for the different scenarios show little variation in the water table. Nevertheless, predictions show that it will rise slightly, mostly in the order of 0–0.25 m, especially during winter. The proposed approach in this study can be used to visualize areas where the water levels are expected to change, as well as to gain insights about how big the changes will be. The approaches and models developed with this paper could be replicated and applied to other study areas, allowing for the possibility to extend this model to a national level, improving the prevention and adaptation plans in Denmark and providing a more global overview of future water level predictions to more efficiently handle future climate change scenarios.

scholarly journals Aboveground biomass in Inner Mongolian temperate grasslands decreases under climate warming

2020 ◽  
Author(s):  
Guocheng Wang ◽  
Zhongkui Luo ◽  
Yao Huang ◽  
Wenjuan Sun ◽  
Yurong Wei ◽  
...  
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Climate Warming ◽  
Aboveground Biomass ◽  
Future Climate ◽  
Future Climate Change ◽  
Desert Steppe ◽  
Meadow Steppe ◽  
Temperate Grasslands ◽  
Spatiotemporal Changes

Abstract. Grassland aboveground biomass (AGB) is a critical component of the global carbon cycle and reflects ecosystem productivity. Although it is widely acknowledged that dynamics of grassland biomass are significantly regulated by climate change, in situ evidence at large spatiotemporal scales is limited. Here, we combine biomass measurements from six long-term (> 30 years) experiments and data in existing literatures to explore the spatiotemporal changes in AGB in Inner Mongolian temperate grasslands. We show that, on average, annual AGB over the past four decades is 2,561 ka ha−1, 1,496 kg ha−1 and 835 kg ha−1, respectively, in meadow steppe, typical steppe and desert steppe in Inner Mongolia. The spatiotemporal changes of AGB are regulated by interactions of climatic attributes, edaphic properties, grazing intensity and grassland type. Using a machine learning-based approach, we map annual AGB (from 1981 to 2100) across the Inner Mongolian grassland at the spatial resolution of 1 km. We find that on the regional scale, meadow steppe has the highest annual AGB, followed by typical and desert steppe. During 1981–2019, the average annual AGB generally exhibited a declining trend across all the three types of grassland. Under future climate warming, AGB in the study region could continue to decrease. On average, compared with the historical AGB (i.e., average of 1981–2019), the AGB at the end of this century (i.e., average of 2080–2100) would decrease by 14 % under RCP4.5 and 28 % under RCP8.5, respectively. The decreases in AGB under warming show large disparities across different grassland types and future climate change scenarios. Our results demonstrate the accuracy of predictions in AGB using a machine learning-based approach driven by several readily obtainable environmental variables; and provide new data at large scale and fine resolution extrapolated from field measurements.

Reassessing Climate Change Impacts on Agriculture in China

2015 ◽  
Vol 03 (02) ◽  
pp. 1550011
Author(s):  
Jie LIU ◽  
Changyi LIU ◽  
Yan WEN
Keyword(s):  
Climate Change ◽  
Crop Yields ◽  
Empirical Studies ◽  
Climate Change Impacts ◽  
Adaptation Effect ◽  
Future Climate ◽  
Future Climate Change ◽  
Bias Estimation ◽  
Rcp Scenarios ◽  
Negative Impacts

Nonlinearity and adaptation effect are rarely taken into consideration in the existing literature of empirical studies on climate change impacts, which may lead to bias estimation of the impacts on agricultural production. This paper aims to reassess the impacts on crop yields (rice, wheat, and maize) by incorporating the terms of nonlinearity and adaptation into a provincial panel data model and further study the impacts of future climate change under the represented concentration pathways (RCP) scenarios. Results reveal that the historical warming temperature benefits rice but harm wheat and maize productions, and decreasing precipitation benefits rice and maize but harm wheat production. Adaptation can significantly mitigate the negative impacts. Under RCP4.5 and RCP8.0, after adaptation, the yield changes attributed to future climate change vary from 0.66% to 0.98% for rice, -0.65% to -0.84% for wheat, and -0.24 to 0.08% for maize. The shifts of means of climatic variables impose no challenge on national food security of China.

scholarly journals Identifying Who Engages in Sustainable Adaptation in Large-Scale Commodity Agriculture

2021 ◽  
Author(s):  
Robyn S. Wilson ◽  
Hugh Walpole
Keyword(s):  
Climate Change ◽  
Water Quality ◽  
Large Scale ◽  
Global Climate ◽  
Soil Health ◽  
The United States ◽  
Future Climate Change ◽  
Quality Practices ◽  
Common Strategy ◽  
Negative Impacts

Abstract Global climate change is projected to negatively impact agriculture through increasingly severe weather. In the eastern Corn Belt of the United States, it is projected to get warmer and wetter overall, with more variability in the seasonal timing of rainfall. This will make it more difficult to get into the fields in the spring and fall due to wet conditions, while higher overall temperatures and decreased rainfall in the summer may limit crop growth. While there are multiple adaptations to reduce the vulnerability of agricultural production to a changing climate, these adaptations have varying implications for soil health, carbon sequestration and water quality. We explore the drivers of adaptations that vary in their provisioning of a variety of ecosystem services. We find that adaptation is driven in large part by self-reported past negative experiences with climate change that drive up concern about future climate change. Adaptation is also more likely among farmers that are younger, more educated, and more conservation minded, and who operate farms that are larger, more extensively insured, and will be passed on to a family member. However, increasing tile drainage will be the most common strategy in response to increased and more variable rainfall, indicating potential negative impacts for water quality. Practices that promote soil health and sequestration will be less common, and more driven by the identity of farmers as conservationists than by the weather. There will be a need to offset the potential negative impacts of increasing drainage through the promotion of edge-of-field filtration practices.

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