Ojeda Jonathan Jesus, Islam M Rafiq, Correa-Luna Martin, Gargiulo Juan Ignacio, Clark Cameron Edward Fisher, Rotili Diego Hernán, Garcia Sergio Carlos
Centre for Sustainable Agricultural Systems, University of Southern Queensland, Toowoomba, QLD, Australia.
Tasmanian Institute of Agriculture, University of Tasmania, Hobart, TAS, Australia.
Front Plant Sci. 2023 Jun 19;14:1206535. doi: 10.3389/fpls.2023.1206535. eCollection 2023.
Maize silage is a key component of feed rations in dairy systems due to its high forage and grain yield, water use efficiency, and energy content. However, maize silage nutritive value can be compromised by in-season changes during crop development due to changes in plant partitioning between grain and other biomass fractions. The partitioning to grain (harvest index, HI) is affected by the interactions between genotype (G) × environment (E) × management (M). Thus, modelling tools could assist in accurately predicting changes during the in-season crop partitioning and composition and, from these, the HI of maize silage. Our objectives were to (i) identify the main drivers of grain yield and HI variability, (ii) calibrate the Agricultural Production Systems Simulator (APSIM) to estimate crop growth, development, and plant partitioning using detailed experimental field data, and (iii) explore the main sources of HI variance in a wide range of G × E × M combinations. Nitrogen (N) rates, sowing date, harvest date, plant density, irrigation rates, and genotype data were used from four field experiments to assess the main drivers of HI variability and to calibrate the maize crop module in APSIM. Then, the model was run for a complete range of G × E × M combinations across 50 years. Experimental data demonstrated that the main drivers of observed HI variability were genotype and water status. The model accurately simulated phenology [leaf number and canopy green cover; Concordance Correlation Coefficient (CCC)=0.79-0.97, and Root Mean Square Percentage Error (RMSPE)=13%] and crop growth (total aboveground biomass, grain + cob, leaf, and stover weight; CCC=0.86-0.94 and RMSPE=23-39%). In addition, for HI, CCC was high (0.78) with an RMSPE of 12%. The long-term scenario analysis exercise showed that genotype and N rate contributed to 44% and 36% of the HI variance. Our study demonstrated that APSIM is a suitable tool to estimate maize HI as one potential proxy of silage quality. The calibrated APSIM model can now be used to compare the inter-annual variability of HI for maize forage crops based on G × E × M interactions. Therefore, the model provides new knowledge to (potentially) improve maize silage nutritive value and aid genotype selection and harvest timing decision-making.
玉米青贮饲料是奶牛养殖系统日粮的关键组成部分，因为它具有较高的草料和谷物产量、水分利用效率以及能量含量。然而，由于作物生长发育期间籽粒与其他生物量部分之间的植物分配变化，玉米青贮饲料的营养价值可能会受到季节内变化的影响。籽粒分配（收获指数，HI）受基因型（G）×环境（E）×管理（M）之间相互作用的影响。因此，建模工具可以帮助准确预测季节内作物分配和组成的变化，并据此预测玉米青贮饲料的收获指数。我们的目标是：（i）确定籽粒产量和HI变异的主要驱动因素；（ii）使用详细的田间试验数据校准农业生产系统模拟器（APSIM），以估计作物生长、发育和植物分配；（iii）在广泛的G×E×M组合中探索HI变异的主要来源。利用来自四个田间试验的氮（N）施用量、播种日期、收获日期、种植密度、灌溉量和基因型数据，评估HI变异的主要驱动因素，并校准APSIM中的玉米作物模块。然后，在50年的时间里，对完整的G×E×M组合范围运行该模型。实验数据表明，观察到的HI变异的主要驱动因素是基因型和水分状况。该模型准确模拟了物候（叶片数和冠层绿色覆盖；一致性相关系数（CCC）=0.79 - 0.97，均方根百分比误差（RMSPE）=13%）和作物生长（地上部总生物量、籽粒+穗轴、叶片和茎秆重量；CCC = 0.86 - 0.94，RMSPE = 23 - 39%）。此外，对于HI，CCC较高（0.78），RMSPE为12%。长期情景分析表明，基因型和N施用量分别导致HI变异的44%和36%。我们的研究表明，APSIM是估计玉米HI的合适工具，HI可作为青贮饲料质量的一个潜在指标。校准后的APSIM模型现在可用于基于G×E×M相互作用比较玉米饲料作物HI的年际变异性。因此，该模型为（潜在地）提高玉米青贮饲料营养价值以及辅助基因型选择和收获时机决策提供了新知识。
