Jarne Adrián, Usón Asunción, Reiné Ramón
Departamento de Ciencias Agrarias y del Medio Natural, Escuela Politécnica Superior, Universidad de Zaragoza, Ctra Cuarte s/n, 22071 Huesca, Spain.
Plants (Basel). 2025 Jul 11;14(14):2150. doi: 10.3390/plants14142150.
Seasonal climate variability and agronomic management profoundly influence both the productivity and nutritive value of temperate hay meadows. We analyzed five years of data (2019, 2020, 2022-2024) from 15 meadows in the central Spanish Pyrenees to quantify how environmental variables (January-June minimum temperatures, rainfall), management variables (fertilization rates (N, P, K), livestock load, cutting date), and vegetation (plant biodiversity (Shannon index)) drive total biomass yield (kg ha), protein content (%), and Relative Feed Value (RFV). Using Random Forest regression with rigorous cross-validation, our yield model achieved an R of 0.802 (RMSE = 983.8 kg ha), the protein model an R of 0.786 (RMSE = 1.71%), and the RFV model an R of 0.718 (RMSE = 13.86). Variable importance analyses revealed that March rainfall was the dominant predictor of yield (importance = 0.430), reflecting the critical role of early-spring moisture in tiller establishment and canopy development. In contrast, cutting date exerted the greatest influence on protein (importance = 0.366) and RFV (importance = 0.344), underscoring the sensitivity of forage quality to harvest timing. Lower minimum temperatures-particularly in March and May-and moderate livestock densities (up to 1 LU) were also positively associated with enhanced protein and RFV, whereas higher biodiversity (Shannon ≥ 3) produced modest gains in feed quality without substantial yield penalties. These findings suggest that adaptive management-prioritizing soil moisture conservation in early spring, timely harvesting, balanced grazing intensity, and maintenance of plant diversity-can optimize both the quantity and quality of hay meadow biomass under variable climatic conditions.
季节性气候变率和农艺管理深刻影响着温带干草草地的生产力和营养价值。我们分析了西班牙比利牛斯山脉中部15块草地连续五年（2019年、2020年、2022年至2024年）的数据，以量化环境变量（1月至6月最低气温、降雨量）、管理变量（施肥量（氮、磷、钾）、载畜量、刈割日期）和植被（植物生物多样性（香农指数））如何驱动总生物量产量（千克/公顷）、蛋白质含量（%）和相对饲用价值（RFV）。通过带有严格交叉验证的随机森林回归分析，我们的产量模型R值为0.802（均方根误差=983.8千克/公顷），蛋白质模型R值为0.786（均方根误差=1.71%），RFV模型R值为0.718（均方根误差=13.86）。变量重要性分析表明，3月降雨量是产量的主要预测因子（重要性=0.430），这反映了早春水分在分蘖形成和冠层发育中的关键作用。相比之下，刈割日期对蛋白质（重要性=0.366）和RFV（重要性=0.344）的影响最大，这突出了饲草质量对收获时间的敏感性。较低的最低气温——尤其是3月和5月——以及适度的载畜密度（最高1 LU）也与蛋白质和RFV的提高呈正相关，而较高的生物多样性（香农指数≥3）在对产量没有实质性影响的情况下，使饲料质量略有提高。这些发现表明，适应性管理——优先在早春保护土壤水分、适时收获、平衡放牧强度以及维持植物多样性——可以在多变的气候条件下优化干草草地生物量的数量和质量。
