Teh Christopher Boon Sung, Cheah See Siang, Kulaveerasingam Harikrishna
Faculty of Agriculture, Universiti Putra Malaysia, Malaysia.
Sime Darby Plantation Research Sdn. Bhd., Malaysia.
Heliyon. 2024 Jun 15;10(14):e32561. doi: 10.1016/j.heliyon.2024.e32561. eCollection 2024 Jul 30.
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), soil texture (27-59 % clay and 7-67 % sand), and rainfall (1800-2800 mm yr). 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.
开发了一个名为Sawit.jl的半机理油棕生长和产量模型，以考虑马来西亚气候条件下广泛的种植密度和土壤质地。该模型包括与气象学、光合作用、能量平衡、土壤含水量和作物生长相关的组件。该模型使用每日天气数据模拟瞬时气象特性，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。该模型还使用来自较新的tenera油棕的更新测量数据进行参数化，包括温度依赖性的Rubisco动力学、比叶面积以及各种树体部分之间的养分和干物质分配。Sawit.jl使用来自马来西亚七个油棕种植地的历史田间测量数据进行验证，这些种植地的棕榈树龄跨度为1至23年。这七个地点在土壤类型（始成土和老成土）、种植密度（82 - 299株/公顷）、土壤质地（27% - 59%的粘土和7% - 67%的沙子）和降雨量（1800 - 2800毫米/年）方面存在差异。该模型在模拟不同条件下的油棕参数（除树干重量外）时总体显示出良好的准确性，模型绝对误差的模型一致性指标范围为6%至27%，模型偏差为 - 22%至 + 17%，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然变化的响应，例如厄尔尼诺和拉尼娜事件期间的降雨变化，同时考虑了土壤质地、降雨和其他气象因素如何影响水分亏缺和作物光合作用。然而，模型准确性因地点、种植密度和油棕参数而异。通过更准确地表示油棕微气候、纳入结果活动以及完善树干的干物质分配机制，可以提高模型准确性。
