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

Article

Ojeda, Jonathan Jesus, Islam, M. Rafiq Islam, Correa-Luna, Martin, Gargiulo, Juan Ignacio, Clark, Cameron Edward Fisher, Rotili, Diego Hernan and Garcia, Sergio Carlos. 2023. "Field and in-silico analysis of harvest index variability in maize silage." Frontiers in Plant Science. 14. https://doi.org/10.3389/fpls.2023.1206535
Article Title

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

ERA Journal ID200524
Article CategoryArticle
AuthorsOjeda, Jonathan Jesus, Islam, M. Rafiq Islam, Correa-Luna, Martin, Gargiulo, Juan Ignacio, Clark, Cameron Edward Fisher, Rotili, Diego Hernan and Garcia, Sergio Carlos
Journal TitleFrontiers in Plant Science
Journal Citation14
Article Number1206535
Number of Pages17
Year2023
PublisherFrontiers Media SA
Place of PublicationSwitzerland
ISSN1664-462X
Digital Object Identifier (DOI)https://doi.org/10.3389/fpls.2023.1206535
Web Address (URL)https://www.frontiersin.org/articles/10.3389/fpls.2023.1206535/full
AbstractMaize 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.
KeywordsAPSIM; silage quality; crop modelling; calibration; forage; Zea mays L.
ANZSRC Field of Research 20203099. Other agricultural, veterinary and food sciences
Byline AffiliationsCentre for Sustainable Agricultural Systems
University of Tasmania
University of Sydney
NSW Department of Primary Industries, New South Wales
University of Buenos Aires, Argentina
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