一种称为Sawit的半机械油棕生长和产量模型。jl的开发是为了在马来西亚的气候条件下考虑广泛的种植密度和土壤质地。该模型包括与气象学相关的组件,光合作用,能量平衡,土壤含水量,和作物生长。该模型使用每日天气数据模拟瞬时气象属性,使用Shuttleworth-Wallace模型计算作物和土壤的同时蒸发量,通过达西定律确定土壤含水量,并采用生化C3模型进行光合作用。该模型还使用更新的tenera油棕的更新测量值进行参数化,包括温度依赖性Rubisco动力学,比叶面积,以及养分和干物质在不同树木部位之间的分配。Sawit.jl使用来自七个马来西亚油棕遗址的历史现场测量数据进行了验证,包括1-23年的棕榈年龄。这七个地点的土壤类型不同(Inceptisols和Ultisols),种植密度(82-299棕榈ha-1),土壤质地(27-59%粘土和7-67%砂),和降雨量(1800-2800毫米yr-1)。该模型在模拟不同条件下的油棕参数(躯干重量除外)方面显示出整体良好的准确性,对于模型绝对误差,模型协议度量范围从6%到27%,-22%到+17%的模型偏差,Kling-Gupta效率指数为0.38至0.98。该模型还预测了油棕产量对降雨突变的响应,比如厄尔尼诺和拉尼娜事件期间,在考虑土壤质地的同时,降雨,和其他气象因素影响水分亏缺和作物光合作用。然而,模型精度因地点而异,种植密度,和油棕参数。可以通过更准确地表示油棕微气候来提高模型精度,结合结果活动,并完善了树干的干物质分配机制。
A semi-mechanistic oil palm growth and yield model called Sawit.jl was developed to account for a wide range of planting densities and soil textures under Malaysia\'s climate conditions. The model comprises components related to meteorology, photosynthesis, energy balance, soil water content, and crop growth. The model simulates instantaneous meteorological properties using daily weather data, calculates simultaneous evaporation from crop and soil with the Shuttleworth-Wallace model, determines soil water content through Darcy\'s law, and adapts a biochemical C3 model for photosynthesis. The model is also parameterized using updated measurements from the newer tenera oil palm, including temperature-dependent Rubisco kinetics, specific leaf area, and the partitioning of nutrients and dry matter between various tree parts. Sawit.jl was validated using historical field measurement data from seven Malaysian oil palm sites, encompassing palm ages spanning 1-23 years. These seven sites differed in soil type (Inceptisols and Ultisols), planting density (82-299 palms ha-1), soil texture (27-59 % clay and 7-67 % sand), and rainfall (1800-2800 mm yr-1). The model showed overall good accuracy in simulating oil palm parameters (except for trunk weight) across diverse conditions, with model agreement metrics ranging from 6 to 27 % for model absolute errors, -22 to +17 % for model bias, and 0.38 to 0.98 for the Kling-Gupta Efficiency index. The model also predicted the response of oil palm yield to abrupt rainfall changes, such as those during El Niño and La Niña events, while accounting for how soil texture, rainfall, and other meteorological factors influence water deficits and crop photosynthesis. However, model accuracy varied by site, planting density, and oil palm parameter. Model accuracy can be increased by more accurately representing the oil palm microclimate, incorporating fruiting activity, and refining the dry matter partitioning mechanism for the trunk.