Field and in-silico analysis of harvest index variability in maize silage
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...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-06-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2023.1206535/full |
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| author | Jonathan Jesus Ojeda Jonathan Jesus Ojeda M. Rafiq Islam Martin Correa-Luna Juan Ignacio Gargiulo Cameron Edward Fisher Clark Diego Hernán Rotili Diego Hernán Rotili Sergio Carlos Garcia |
| author_facet | Jonathan Jesus Ojeda Jonathan Jesus Ojeda M. Rafiq Islam Martin Correa-Luna Juan Ignacio Gargiulo Cameron Edward Fisher Clark Diego Hernán Rotili Diego Hernán Rotili Sergio Carlos Garcia |
| author_sort | Jonathan Jesus Ojeda |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-13T04:32:13Z |
| format | Article |
| id | doaj.art-a69c2d53db4444c59b738b4de89619f3 |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-03-13T04:32:13Z |
| publishDate | 2023-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-a69c2d53db4444c59b738b4de89619f32023-06-19T11:40:03ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2023-06-011410.3389/fpls.2023.12065351206535Field and in-silico analysis of harvest index variability in maize silageJonathan Jesus Ojeda0Jonathan Jesus Ojeda1M. Rafiq Islam2Martin Correa-Luna3Juan Ignacio Gargiulo4Cameron Edward Fisher Clark5Diego Hernán Rotili6Diego Hernán Rotili7Sergio Carlos Garcia8Centre for Sustainable Agricultural Systems, University of Southern Queensland, Toowoomba, QLD, AustraliaTasmanian Institute of Agriculture, University of Tasmania, Hobart, TAS, AustraliaDairy Science Group, School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, Camden, NSW, AustraliaDairy Science Group, School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, Camden, NSW, AustraliaNSW Department of Primary Industries, Menangle, NSW, AustraliaLivestock Production and Welfare Group, School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, Camden, NSW, AustraliaCátedra de Cerealicultura, Departamento de Producción Vegetal, Facultad de Agronomía, Universidad de Buenos Aires, Buenos Aires, ArgentinaInstituto 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, ArgentinaDairy Science Group, School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, Camden, NSW, AustraliaMaize 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.https://www.frontiersin.org/articles/10.3389/fpls.2023.1206535/fullsilage qualityAPSIMcrop modellingcalibrationforageZea mays L. |
| spellingShingle | Jonathan Jesus Ojeda Jonathan Jesus Ojeda M. Rafiq Islam Martin Correa-Luna Juan Ignacio Gargiulo Cameron Edward Fisher Clark Diego Hernán Rotili Diego Hernán Rotili Sergio Carlos Garcia Field and in-silico analysis of harvest index variability in maize silage Frontiers in Plant Science silage quality APSIM crop modelling calibration forage Zea mays L. |
| title | Field and in-silico analysis of harvest index variability in maize silage |
| title_full | Field and in-silico analysis of harvest index variability in maize silage |
| title_fullStr | Field and in-silico analysis of harvest index variability in maize silage |
| title_full_unstemmed | Field and in-silico analysis of harvest index variability in maize silage |
| title_short | Field and in-silico analysis of harvest index variability in maize silage |
| title_sort | field and in silico analysis of harvest index variability in maize silage |
| topic | silage quality APSIM crop modelling calibration forage Zea mays L. |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2023.1206535/full |
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