Field and in-silico analysis of harvest index variability in maize silage

Jonathan Jesus Ojeda; M Rafiq Islam; Martin Correa-Luna; Juan Ignacio Gargiulo; Cameron Edward Fisher Clark; Diego Hernán Rotili; Sergio Carlos Garcia

doi:10.3389/fpls.2023.1206535

Field and in-silico analysis of harvest index variability in maize silage

Front Plant Sci. 2023 Jun 19:14:1206535. doi: 10.3389/fpls.2023.1206535. eCollection 2023.

Authors

Jonathan Jesus Ojeda^{1

2}, M Rafiq Islam³, Martin Correa-Luna³, Juan Ignacio Gargiulo⁴, Cameron Edward Fisher Clark⁵, Diego Hernán Rotili^{6

7}, Sergio Carlos Garcia³

Affiliations

¹ Centre for Sustainable Agricultural Systems, University of Southern Queensland, Toowoomba, QLD, Australia.
² Tasmanian Institute of Agriculture, University of Tasmania, Hobart, TAS, Australia.
³ Dairy Science Group, School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, Camden, NSW, Australia.
⁴ NSW Department of Primary Industries, Menangle, NSW, Australia.
⁵ Livestock Production and Welfare Group, School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, Camden, NSW, Australia.
⁶ Cátedra de Cerealicultura, Departamento de Producción Vegetal, Facultad de Agronomía, Universidad de Buenos Aires, Buenos Aires, Argentina.
⁷ Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA) Facultad de Agronomía, Universidad de Buenos Aires, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina.

Abstract

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.

Keywords: APSIM; Zea mays L.; calibration; crop modelling; forage; silage quality.

Grants and funding

This project is part of Dairy UP (www.dairyup.com.au), an industry driven program led by the University of Sydney’s Dairy Research Foundation (DRF, Camden, NSW, Australia); co-delivered together with Scibus (Camden, NSW, Australia), the New South Wales Department of Primary Industry (Orange, NSW, Australia), and Dairy Australia (Southbank, VIC, Australia); and supported by the NSW Government, Australian Fresh Milk Holding Ltd. (Gooloogong, NSW, Australia), Bega Cheese (Bega, NSW, Australia), Dairy Australia (Southbank, VIC, Australia), (formerly) Dairy Connect (Mascot, NSW, Australia), DairyNSW (Camden, NSW, Australia), DRF (Camden, NSW, Australia) Local Land Services (Hunter; Tocal, NSW, Australia), Leppington Pastoral Co. (Bringelly, NSW, Australia), Norco Dairy Co-Op (South Lismore, NSW, Australia), NSW Farmers (St Leonards, NSW, Australia), the NSW Department of Primary Industries (Menangle, NSW, Australia), Scibus, and South East Local Land Services (Goulburn, NSW, Australia). The authors declare that this study received funding from the Dairy UP program and that none of the individual funders of Dairy UP was involved in the study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication. The authors have not stated any conflicts of interest.