LINKAGE OF CLIMATE DIAGNOSTICS IN PREDICTIONS FOR CROP PRODUCTION is a well-researched Life Science Thesis/Dissertation topic, it is to be used as a guide or framework for your Academic Research.
This research presents three case studies of low-temperature anomalies that occurred during the winter-spring seasons influenced extreme events and crop production. We investigate the causes and effects of each climate event and developed prediction methods for crops based on climate diagnostic information.
The first study diagnosed driven environmental-factors associated with the 2011 great flood in Thailand which resulted in total crop loss. The flood was caused by abnormally high monsoon rainfall over the Chao Phraya River basin (CPRB) and low drainage capacity because of anomalous high soil-moisture, increased Gulf of Thailand sea level, and other management factors. Increased premonsoon rainfall in March, strengthened by prominent northeasterly cold winds coming from East Asia, was a key element to increase soil moisture.
Increases in the premonsoon rainfall are projected in the future because of influences from rising anthropogenic greenhouse gases over the CPRB. The second study investigated climate circulation and indices related to wet-and-cold (WC) events that lead to significant crop damage in Taiwan. We developed empirical-dynamical models by using observed indices of western North Pacific (WNP), Niño 3.4, and Arctic Oscillation and predicted indices of WNP and Pacific meridional mode (PMM) from Climate Forecast System Version 2 (CFSv2) outputs.
The prediction was suitable for 6 months leading up to the occurrence of the WC events. Our final study extends from the second study and aims to predict chronic crop damage from climate change by using a crop simulation model, ORYZA(v3), and RCP 8.5 scenario from Coordinated Regional Climate Downscaling Experiment (CORDEX).
The long-term prediction of rice growth and yield for different regions of Taiwan illustrated an earlier maturation of 6–11 days by 2045. The yield was predicted to be reduced by 3.3–10% or increased by 8.5–18% without or with rising CO2 effects respectively.
The three studies, while different in location and circumstances, were influenced by similar climate phenomena. These findings are useful to support plans to adapt cropping in these specific study sites. The same methodologies can be applied across Thailand, Taiwan, and other areas with similar agro-climatology.
Specific weather events or “extreme events” occur with the trend toward global warming. While high-temperature anomalies are understandable, low-temperature extremes not as intuitive. Climate scientists have explained changes in the atmospheric circulation related to elevated greenhouse gasses (GHGs) emissions, such as carbon dioxide (CO2), that likely increase the number of high-temperature events.
The likelihood of unusually cold events is possible due to unusual patterns in the earth’s atmosphere that are exacerbated by the prevalence of global warming. These extreme events represent “climate variability” but not of all associated climate change. Most climate variation is unique and specific for space and time, from a short span (i.e., weekly, seasonal, interannual) to a longer period (i.e., decadal, interdecadal).
Climate diagnostics, which apply a theoretical framework of climate systems and dynamics, using several analytical tools, is an appropriate method to identify the nature and causes of climate variability for an individual case. Understanding extreme events can provide information to support the progress in climate forecast and can help to prevent risks and manage their impacts.
Crop production is a particular and important case that is highly vulnerable to climate extremes and climate change. National adaptation plans for crops, e.g. Thailand and Taiwan, are primarily established based on general climate impacts but are limited in predicting a particular event.
Research studies predicting crop yields have commonly used observed/predicted meteorological parameters (e.g., temperature, precipitation) and well-known climate phenomenon (El Niño, La Niña, Pacific Decadal Oscillation, etc.) without understanding whether those climate phenomena are involved.
Therefore, numerous studies reported influences of high temperatures on crop production that is explained in terms of average temperature; however, don’t acknowledge those influences on cold events in the low latitudes. Currently, cold events during winter and spring occur in the tropics, such as Thailand and other Indo-China countries, which influence people’s livelihoods, animals, and crops. Unusually cold events and snow have occurred in sub-tropical countries like Taiwan, leading to unprecedented crop loss in 2016.
Therefore, it is essential to understand climate variability and enable growers to prepare. Manipulation of climate diagnostic results to establish a prediction in crop risks and productivity is a challenge and is the motivation for this dissertation. The main objective of the study is to understand extreme events associated with cold anomalies in the tropics and sub-tropical and subsequently develop predictions for crop production.
The study goal is accomplished by case analysis. Two prominent extreme events were selected, the 2011 great flood in Thailand and the unusual cold in 2016 in Taiwan. The third case focuses on future changes in climate on mean crop production and variability during the cold season in Taiwan.
The great flood in Thailand represents an impact of climate extremes that resulted in total crop loss, and no ability to predict for adaptation by the country’s rice growers. Predictions can help make for a resilient cropping system under climate extremes and those predictions rely on climate projections and implications from climate change.
The flood occurred during the monsoon season with high intensity and among the longest of the country’s flood events. Our research findings differ from other work by suggesting causes of substantially increased premonsoon rainfall in winter-spring because of cold spells and high sea levels in the Gulf of Thailand.
We observed signals of climate change-related the premonsoon rainfall that suggests potential flooding similar to 2011 occurring in the future. Details of the analysis results and its implication from climate change are documented in Chapter 2.
The second study is of cold events in Taiwan that present significant and abrupt damage in many crop species. The crop loss was mainly by physical injury from freeze and chilling stresses which are difficult to predict by any ecophysiological model. Our alternative method is the prediction of climate events that result in the damage.
Diagnostic results reveal that it is not only cold but cold combined with wet conditions that significantly caused the loss in 2016. Limitations of existing climate models are in their resolution and poor evaluation for some climate phenomena over land.
Thus, the combined empirical-dynamical models to predict wet and cold (WC) events were developed. Climate pattern attributed WC events, components, and performance of the predicting models were presented in Chapter 3.
Extending from Chapter 3, the last study, or Chapter 4, determined winter crop response to climate change and variability in a future period. The probability of yield variability is investigated. This case study presents a latent effect (gradual change) of climate on crop growth and yield where predictions were directly made to crop productivity. Rice, a staple food, was selected for the study which is normally exposed to cold stress in the early growing season.
The prediction was constructed based on an existing crop simulation model and RCP 8.5 climate scenarios. The predicted results with and without CO2 effects, the performance of the model, and limitation in predictions are described in Chapter 4. Conclusions of all three case studies are given in Chapter 5.