Summary:
Temperature, precipitation, CO2 levels, crop yield, soil health, extreme weather events, crop disease incidence, water availability, food security, and economic stability are just a few of the many ways that climate change is affecting agriculture. Traditional growing seasons are thrown off, and extreme weather events like droughts and floods are made worse by rising temperatures and changing weather patterns. Variations in precipitation patterns affect soil moisture and water availability, while variations in CO2 levels affect crop growth and productivity. The interplay of these factors jeopardizes soil health, food security, and financial stability, highlighting the pressing need for all-encompassing approaches to reduce the detrimental effects of climate change on agriculture.
Climate change extreme weather event
Background and Motivation (2020-2023):
The global agriculture sector has been greatly impacted by climate change between 2020 and 2023. Droughts, floods, hurricanes, heat waves, and other extreme weather events have become more frequent, resulting in crop failures and financial losses. Changes in temperature and precipitation affect crop growth and water availability as well, necessitating adaptation to shifting circumstances. Global food security is also at risk from climate change, which can disrupt supply chains and agricultural productivity. Long-term obstacles to sustainable food production include deteriorating soil health and environmental degradation. Understanding these effects helps to inform policy decisions and mitigation efforts aimed at increasing resilience, lowering greenhouse gas emissions, and promoting sustainable agricultural practices.
Top Climate Impacts on Agriculture:
1. Temperature:
Temperature affects photosynthetic rates, respiration, pest/disease pressure, plant growth stages, seed germination, and other aspects of agriculture. Severe temperature variations can stress crops, resulting in lower yields or crop loss. Furthermore, temperature changes have an impact on the amount of water needed, making times of high transpiration worse for water stress. Temperature variations brought on by climate change make agricultural practices much more complex, requiring farmers to develop adaptation techniques to retain resistance and output in the face of shifting climatic conditions.
Temperature impact
2. Precipitation:
Rainfall is vital to agriculture because it maintains soil moisture levels and supplies the water required for plant growth. A sufficient amount of precipitation encourages robust crop growth and increased yields, whereas a drought can cause water stress and crop failure. On the other hand, excessive precipitation can lead to soil erosion, flooding, and an increase in the pressure of pests and diseases. Where there is erratic rainfall, effective irrigation management is essential. Farmers have additional challenges as a result of changes in precipitation patterns brought about by climate change, which call for adaptation plans to preserve agricultural resilience and output.
Increased photosynthesis and water use efficiency are two ways that elevated carbon dioxide (CO2) levels impact agriculture and could result in higher crop yields. Adaptive management techniques are necessary since variations in CO2 levels can also affect crop quality, encourage the growth of weeds, and change the dynamics of pests. Additionally, CO2 has a major role in climate change, affecting patterns of temperature and precipitation. These changes have an impact on agricultural output and call for mitigation and adaptation measures to produce food sustainably.
CO2 Levels Impact
4. Soil Health:
Agriculture depends on healthy soil because it affects root development, water retention, microbial activity, and nutrient availability. In addition to promoting plant development and reducing soil erosion, healthy soils also trap carbon and support sustainable farming methods. They also reduce pests and illnesses and improve resistance to climate variability. Maintaining agricultural output and guaranteeing the long-term viability of food production systems require prioritizing soil health through conservation strategies.
Soil Health
5. Water Availability:
Water availability impacts soil moisture levels, plant growth, and potential output in agriculture. A sufficient supply of water guarantees the best possible crop development, but droughts can result in lower yields and financial losses. In areas with little rainfall, effective irrigation systems are crucial, while flood control measures lessen the dangers during heavy rains. Because agricultural runoff can affect ecosystems and human health, the quality of water is also very important. The supply of water is further complicated by climate change, requiring adaptive measures for sustainable farming operations.
Water Availability
6. Economic Impact:
Agriculture has a significant economic impact that includes contributions to the GDP, the creation of jobs, trade revenue, and rural development. Agriculture promotes innovation in production techniques, increases demand for input industries, and guarantees food security. It is also essential to the management of natural resources and the sustainability of the environment. In general, agriculture is a key component of economic development since it sustains livelihoods, promotes economic expansion, and guarantees the welfare of people everywhere.
Economic Impact
AI-Powered Decision Support Systems for climate change Effect on Agriculture:
Artificial intelligence-driven decision support systems are useful instruments for evaluating and alleviating the impacts of climate change on agriculture. These systems evaluate climate data, forecast future trends, and give farmers and policymaker’s useful insights by utilizing sophisticated algorithms and data analytics. Artificial intelligence (AI) can assist in identifying climate-related hazards and opportunities, optimizing resource allocation, and enhancing resilience in agricultural systems by integrating satellite imagery, weather forecasts, soil data, and historical crop performance. By giving stakeholders the information they need to make wise choices about crop selection, irrigation schedules, pest control, and adaptation tactics, these systems eventually increase productivity and sustainability in the face of shifting environmental conditions.
List of the AI-Powered Decision Support Systems that are being used in agriculture:
1. Robotic system for precision farming:
The latest technical advancements in robotics are used in precision farming to automate and efficiently optimize a variety of agricultural operations. Typically, these systems use autonomous or remotely controlled robots that are outfitted with sensors, cameras, and actuators to carry out various tasks like planting, weeding, harvesting, and sowing. Robotic systems can detect weeds, evaluate field data in real-time, diagnose crop health issues, and administer inputs precisely where needed by utilizing technologies like artificial intelligence, machine learning, and GPS. This helps farmers improve yields, save labor expenses, boost production, and decrease resource waste all while having a minimal negative impact on the environment. Furthermore, autonomous operation of robotic equipment enables 24/7 operations and greater farm management flexibility.
Robot Systems for agriculture
2. Agriculture drones:
Drones used for agricultural purposes that are outfitted with many sensors and cameras are referred to as agriculture drones, often known as unmanned aerial vehicles, or UASs (unmanned aerial systems). These drones are used to gather vital information for farmers by monitoring crops, soil, and field health from above. Agriculture drones that are outfitted with multispectral, hyperspectral, or thermal cameras are able to take high-resolution pictures of farms, which facilitates accurate evaluation of crop health, pest infestations, irrigation requirements, and general field conditions. Drones help farmers manage their crops, allocate resources, and implement effective pest control techniques by gathering and evaluating data on plant health indices, moisture content, and temperature fluctuations. Agriculture drones also provide the benefits of quick data collection, affordability, and scalability.
Agriculture Drones
3. Soil Mapping:
In agriculture, soil mapping is a method used to evaluate and categorize the features and attributes of soil in a specific region. In order to ascertain variables like nutrient levels, pH balance, organic matter content, and soil texture, it entails gathering soil samples from various fields and evaluating them. Subsequently, comprehensive maps that show the geographic variability of soil qualities throughout the field are made using this information. Farmers can use soil mapping to guide their decisions about crop choice, fertilizer use, irrigation control, and soil conservation techniques. Farmers can optimize their agricultural operations to increase crop yields, lower input costs, and lessen their impact on the environment by knowing the variability of the soil in their farms.
Soil Mapping
4. Irrigation system:
The controlled and effective application of water to crops is accomplished in agriculture through the use of irrigation systems. To guarantee ideal development and yield, water is sent straight to the roots of the plants. The sophistication of irrigation systems can vary greatly; from basic strategies like surface flooding and furrow irrigation to more sophisticated ones like drip irrigation and sprinkler systems. Precise water management is made possible by these automated systems that are fitted with sensors to track crop water requirements, meteorological conditions, and soil moisture levels. Irrigation systems assist farmers maximize yields, save water resources, and lessen the effects of drought and water shortages by giving crops the proper amount of water at the right time.
Irrigation System
Climate change on agriculture using Machine Learning Models:
1. DecisionTreeRegressor:
The DecisionTreeRegressor is a machine learning model tailored for regression tasks that recursively divides the input space to approximate the mean target value of training samples within each segment. The model uses the 'mean squared error' (mse) as the default criterion to assess split quality, and the 'best' splitter to choose the most effective split based on this criterion. If 'max_depth' is set to 'None', the tree grows until all leaves are pure or have fewer samples than the minimum required by 'min_samples_split', which is 2 by default. The minimum number of samples required at a leaf node is determined by 'min_samples_leaf', set at 1, and the 'min_weight_fraction_leaf' sets the minimum weighted fraction of total samples necessary at a leaf node to 0.0. The 'max_features' parameter, which is set to 'auto' by default, considers all features when looking for the best split, while the 'random_state' parameter uses the numpy.random instance to generate random numbers if set to 'None'. Adjusting these parameters allows the DecisionTreeRegressor to be finely tuned to specific datasets and regression challenges, enhancing both performance and accuracy.
Actual Vs Predicted Values:
DecisionTreeRegressor(Actual Vs Predicted values)
2. RandomForestRegressor:
The RandomForestRegressor is a popular ensemble learning technique used for regression challenges that operates by building a multitude of decision trees during training and producing the average prediction from each tree. By default, the model constructs 100 trees, as determined by the n_estimators parameter. The depth to which each tree is allowed to grow is controlled by the max_depth parameter, with the default setting of None allowing nodes to expand until all leaves are pure or contain fewer samples than specified by the min_samples_split, which is set to 2. The min_samples_leaf parameter dictates the minimum number of samples that must be present at a leaf node, set at 1 by default. The max_features parameter, which defaults to "auto" (equivalent to "sqrt" in regression tasks), governs the number of features considered for determining the best split. Additionally, the random_state parameter can be set to ensure reproducibility of model results. These settings collectively help manage the model’s complexity and enhance its ability to generalize across different data sets.
Actual Vs Predicted Values:
RandomForestRegressor (Actual vs. predicted values)
3. XGBRegressor:
The XGBRegressor is highly regarded for its accuracy, speed, and efficiency in addressing regression problems. It utilizes several key parameters by default to guide its training behavior. The learning_rate parameter, set at a default of 0.1, controls the step size shrinkage to prevent overfitting. The n_estimators parameter specifies the number of boosting rounds or trees to be constructed, with a default of 100. The min_child_weight is set at 1 by default, indicating the minimum sum of instance weight needed in a child node. The maximum depth of the trees is controlled by max_depth, with a default setting of 3, while gamma determines the minimum loss reduction required to make further splits on a leaf node, set at 0 by default.
Additionally, colsample_bytree influences the fraction of features used per boosting round and is set to 1 by default. Regularization terms on weights, which help control the model's complexity, are determined by reg_alpha and reg_lambda, with default values of 0 and 1, respectively. The subsample parameter, which controls the fraction of samples used to fit the individual base learners, enhances model training diversity. Finally, the random_state parameter, set to None by default, can be adjusted to ensure reproducibility and manage the randomness of the algorithm’s operations. These parameters collectively fine-tune the XGBRegressor's performance, making it a robust choice for regression tasks.
Actual Vs Predicted Values:
XGBRegressor (Actual vs. predicted values)
4. ExtraTreesRegressor:
K-Nearest Neighbors (KNN) is a straightforward yet effective supervised machine learning method used for both classification and regression problems. This technique operates by identifying the K closest training dataset points to any new input sample in order to make predictions. In a regression context, KNN predicts the outcome by calculating the average of the target values from these K nearest neighbors. For classification, it employs majority voting among the K nearest neighbors to determine the class label.
As a non-parametric and lazy-learning algorithm, KNN distinguishes itself by not learning any explicit model parameters during training nor making assumptions about the distribution of underlying data. Instead, it focuses its computations at the time of prediction and retains the entire training dataset in memory. This approach allows KNN to adapt flexibly to the incoming data but requires careful consideration of memory usage and computation time, especially with large datasets.
ExtraTreesRegressor Actual Vs Predicted Values:
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