Examining limitations and future directions in climate change simulation models

¹ Niğde Ömer Halisdemir University, Institute of Science and Technology, Department of Plant Production and Technologies, Niğde, Türkiye
² Niğde Ömer Halisdemir University, Ayhan Şahenk Faculty of Agricultural Sciences and Technologies, Department of Biosystems Engineering, Türkiye

^* Corresponding author: bozkurt.f.1500@gmail.com

Abstract

Climate change refers to significant alterations in long-term climate conditions. If greenhouse gas emissions continue to rise, there is a high probability of exceeding the 1.5°C and 2° thresholds of global warming throughout the 21st century. This situation poses a serious threat to the agriculture sector and can lead to a decline in agricultural production and a reduction in product quality. Additionally, intensive farming practices can decrease the resilience of agriculture. This study aims to examine the effects of climate change on the agriculture sector, explain the concept of modeling and the parameters that can be measured, provide guidance on how modeling studies on alfalfa, and similar crops can be improved by identifying their shortcomings. The modeling method is used in many different fields by creating abstract representations of real-world objects or events via a mathematical equation, writing algorithm, or simulation. Parameters used in alfalfa modeling include yield, growth, carbon, water, nitrogen balance, climate effects, and other factors. However, these models have shortcomings such as the need for more comprehensive data collection and testing, the requirement for more parameter adjustments, the inability to address various crops and different growth cycles, the lack of simulation of crown and root roles in growth, sensitivity in measuring soil and input factors, limited testing and research, inaccuracies in automatic classification, the absence of growth and yield simulation models, and the lack of deep learning techniques. Addressing these shortcomings is crucial for achieving more reliable and effective results in the agricultural sector. Strengthening models and addressing these deficiencies have the potential to lead to more robust and sustainable solutions in agriculture.