This study aimed to identify the major climatic variables affecting dry matter yield (DMY) of alfalfa (Medicago sativa L.) and to predict DMY using a decision tree model. A total of 235 observations of alfalfa DMY collected from field experiments conducted in South Korea were combined with climate data obtained from the Korea Meteorological Administration. Twenty-six climate variables related to temperature, growing days, growing degree days, precipitation, duration of sunshine, and summer depression were considered as candidate variables. The minimum Redundancy Maximum Relevance (mRMR) algorithm was applied to select climate variables with high relevance to DMY and low redundancy among variables. Six variables, including growing degree days from 0 to 25°C (GDD 0–25), growing days from 5 to 25°C (GD 5–25) and from 0 to 25°C (GD 0–25), accumulated temperature of summer depression (ASD), duration of sunshine from 5 to 25°C (DS 5–25), and days with precipitation ≥5 mm (DP 5) were selected. The decision tree model showed an R² of 0.8103 and an RMSE of 2,804.0 kg ha-1. GD 0–25 was selected as the root-node variable, with 266 days as the primary split point. Eight terminal nodes were generated, with predicted DMY ranging from 3,620 to 24,647 kg ha-1. The highest predicted DMY was observed under conditions of GD 0–25 ≥266 days and DP 5 ≥60 days, whereas the most frequently observed prediction was 18,364 kg ha-1, accounting for 39% of the observations. These results indicate that alfalfa DMY is associated with conditional combinations of growing days, the number of days with precipitation, accumulated temperature, and sunshine duration. The mRMR-based decision tree approach may provide a useful framework for identifying major climate variables and interpreting threshold-dependent responses of alfalfa productivity.

ABSTRACT
Ⅰ. INTRODUCTION
Ⅱ. MATERIALS AND METHODS
    1. Data collection and processing
    2. Derivation of climatic variables
    3. Variable selection and model evaluation
    4. Statistical software
Ⅲ. RESULTS AND DISCUSSION
    1. Descriptive statistics
    2. Selection frequency of climatic variables using themRMR algorithm
    3. Climatic variables affecting alfalfa dry matter yieldand their effects as assessed using a decision treemodel
Ⅳ. CONCLUSION
Ⅴ. REFERENCES

• Jiyung Kim(Forages Production Systems Division, National Institute of Animal Science, Cheonan 31000, Republic of Korea)
• Kyung Il Sung(Department of Animal Industry Convergence, Kangwon National University, Chuncheon 24341, Republic of Korea)
• Jae Seong Choi(Department of Animal Science, Kangwon National University, Chuncheon 24341, Republic of Korea)
• Byong Wan Kim(Department of Animal Science, Kangwon National University, Chuncheon 24341, Republic of Korea) Corresponding author