Simulation of productivity and soil moisture under Marandu palisade grass using the CSM-CROPGRO-Perennial Forage model
Miquéias G. Santos A D , Kenneth J. Boote B , Rogério T. Faria A and Gerrit Hoogenboom C
+ Author Affiliations
- Author Affiliations
A Department of Rural Engineering, College of Agrarian and Veterinarian Sciences, Sao Paulo State University, Jaboticabal, SP 14884-900, Brazil.
B Agronomy Department, University of Florida, Gainesville, FL 32611-0500, USA.
C Institute for Sustainable Food Systems & Agricultural and Biological Engineering Department, University of Florida, Gainesville, FL 32611-0570, USA.
D Corresponding author. Email: miqueiassjp@yahoo.com.br
Crop and Pasture Science 70(2) 159-168 https://doi.org/10.1071/CP18258
Submitted: 15 June 2018 Accepted: 8 January 2019 Published: 26 February 2019
Abstract
Crop models are important tools for assisting farmers and crop consultants to make decisions about fertilisation, irrigation and harvest management, because they allow users to understand productivity from the view of integrated sensitivities of basic plant physiological processes. The first objective of this study was to evaluate the performance of the CSM-CROPGRO-Perennial Forage model (PFM) to simulate regrowth of Urochloa brizantha (Hochst. ex A.Rich.) R.D.Webster cv. Marandu under varying irrigation and nitrogen levels. The second objective was to evaluate the water-balance module of the model under soil and climatic conditions in the Cerrado biome of central-eastern Brazil. The experimental data for model evaluation were obtained from a field experiment conducted during 2015, 2016 and 2017, and included herbage production, plant-part composition and plant nitrogen (N) concentration. The results suggest that the model can be used to simulate growth of Marandu palisade grass adequately under different managements of irrigation and N fertilisation. The findings indicate also that the agreement between simulations and field-observed soil moisture shows good performance of the water-balance module of CSM-CROPGRO-PFM. The most important parameterisation required by the model was the determination and calibration of inputs such as the stable soil carbon pool (SOM3) for N mineralisation, which affected the N response, and the soil water-holding characteristics, which affected the irrigation response. The default parameterisation (species, ecotype, cultivar) of cv. Marandu in CSM-CROPGRO-PFM was sufficient for adequate performance of the model for this new environment and new crop management. However, minor modifications of species parameters were helpful to account for winter-kill of foliage.
Additional keywords: DSSAT system, grassland.
References
Adiku SGK, Narh S, Jones JW, Laryea KB, Dowuona GN (2008) Short-term effects of crop rotation, residue management, and soil water on carbon mineralization in a tropical cropping system. Plant and Soil 311, 29–38.| Short-term effects of crop rotation, residue management, and soil water on carbon mineralization in a tropical cropping system.Crossref | GoogleScholarGoogle Scholar |
Allen RG, Pereira LS, Raes D, Smith M (1998) ‘Crop evapotranspiration: guidelines for computing crop water requirements.’ FAO Irrigation and Drainage Paper 56. pp. 1–15. (Food and Agriculture Organisation of the United Nations: Rome)
Alvares CA, Stape JL, Sentelhas PC, Gonçalves JLM, Sparovek G (2013) Köppen’s climate classification map for Brazil. Meteorologische Zeitschrift 22, 711–728.
| Köppen’s climate classification map for Brazil.Crossref | GoogleScholarGoogle Scholar |
Barioni LG, Zanett Albertini T, Tonato F, Raposo de Medeiros S, de Oliveira Silva R (2012) Using computer models to assist planning beef production: experiences in Brazil. Revista Argentina de Producción Animal 32, 77–86.
Bonesmo H, Bélanger G (2002) Timothy yield and nutritive value by the CATIMO model: I. Growth and nitrogen. Agronomy Journal 94, 337–345.
| Timothy yield and nutritive value by the CATIMO model: I. Growth and nitrogen.Crossref | GoogleScholarGoogle Scholar |
Boote KJ, Jones JW, Hoogenboom G, Pickering NB (1998) The CROPGRO model for grain legumes. In ‘Understanding options for agricultural production’. (Eds GY Tsuji, G Hoogenboom, PK Thornton) pp. 99–128. (Kluwer: Dordrecht, The Netherlands)
Boval M, Dixon RM (2012) The importance of grasslands for animal production and other functions: a review on management and methodological progress in the tropics. Animal 6, 748–762.
| The importance of grasslands for animal production and other functions: a review on management and methodological progress in the tropics.Crossref | GoogleScholarGoogle Scholar | 22558923PubMed |
Cacho OJ, Finlayson JD, Bywater AC (1995) A simulation model of grazing sheep: II. Whole farm model. Agricultural Systems 48, 27–50.
| A simulation model of grazing sheep: II. Whole farm model.Crossref | GoogleScholarGoogle Scholar |
Cruz PG (2010) Produção de forragem em Brahiaria brizantha: adaptação, geração e avaliação de modelos empíricos e mecanicistas para estimativa do acúmulo de forragem. PhD Thesis, University of São Paulo - College of Agriculture ‘Luiz de Queiroz’, Piracicaba, SP, Brazil.
Dallacort R, Freitas PSL, Faria RT, Jácome ACA, Gonçalves GA, Rezende R (2010) Soil water balance simulated by CROPGRO-Dry bean model for edaphoclimatic conditions in Maringá. Revista Brasileira de Engenharia Agrícola e Ambiental 14, 351–357.
| Soil water balance simulated by CROPGRO-Dry bean model for edaphoclimatic conditions in Maringá.Crossref | GoogleScholarGoogle Scholar |
Etheridge RD, Pesti GM, Foster EH (1998) A comparison of nitrogen values obtained utilizing the Kjeldahl nitrogen and Dumas combustion methodologies (Leco CNS 2000) on samples typical of an animal nutrition analytical laboratory. Animal Feed Science and Technology 73, 21–28.
| A comparison of nitrogen values obtained utilizing the Kjeldahl nitrogen and Dumas combustion methodologies (Leco CNS 2000) on samples typical of an animal nutrition analytical laboratory.Crossref | GoogleScholarGoogle Scholar |
FAO (2016) FAOSTAT. Food and Agriculture Organization of the United Nations, Rome. Available at: http://www.fao.org/faostat/en/ (accessed 22 May 2018).
Gijsman AJ, Hoogenboom G, Parton WJ, Kerridge PC (2002) Modifying DSSAT crop models for low-input agricultural systems using a soil organic matter-residue module from CENTURY. Agronomy Journal 94, 462–474.
| Modifying DSSAT crop models for low-input agricultural systems using a soil organic matter-residue module from CENTURY.Crossref | GoogleScholarGoogle Scholar |
Godwin DC, Singh U (1998) Nitrogen balance and crop response to nitrogen in upland and lowland cropping systems. In ‘Understanding options for agricultural production’. (Eds GY Tsuji, G Hoogenboom, PK Thornton) pp. 55–77. (Kluwer Academic Publishers: Dordrecht, The Netherlands)
Hanks RJ, Sisson DV, Hurst RL, Hubbard KG (1980) Statistical analysis of results from irrigation experiments using the line-source sprinkler system. Soil Science Society of America Journal 44, 886–888.
| Statistical analysis of results from irrigation experiments using the line-source sprinkler system.Crossref | GoogleScholarGoogle Scholar |
Hoogenboom G, Porter CH, Shelia V, Boote KJ, Singh U, White JW, Hunt LA, Ogoshi R, Lizaso JI, Koo J, Asseng S, Singels A, Moreno LP, Jones JW (2017) Decision Support System for Agrotechnology Transfer (DSSAT) Version 4.7. DSSAT Foundation, Gainesville, FL, USA. Available at: www.DSSAT.net
Jones JW, Hoogenboom G, Porter CH, Boote KJ, Batchelor WD, Hunt LA, Wilkens PW, Singh U, Gijsman AJ, Ritchie JT (2003) The DSSAT cropping system model. European Journal of Agronomy 18, 235–265.
| The DSSAT cropping system model.Crossref | GoogleScholarGoogle Scholar |
Kiniry JR, Blanchet R, Williams JR, Texier V, Jones CA, Cabelguenne M (1992) Sunflower simulation using the EPIC and ALMANAC models. Field Crops Research 30, 403–423.
| Sunflower simulation using the EPIC and ALMANAC models.Crossref | GoogleScholarGoogle Scholar |
Lara MAS, Pedreira CGS, Boote KJ, Pedreira BC, Moreno LSB, Alderman PD (2012) Predicting growth of Panicum maximum: an adaptation of the CROPGRO-Perennial Forage model. Agronomy Journal 104, 600–611.
| Predicting growth of Panicum maximum: an adaptation of the CROPGRO-Perennial Forage model.Crossref | GoogleScholarGoogle Scholar |
Liu HL, Yang JY, Tan CS, Drury CF, Reynolds WD, Zhang TQ, Babi YL, Gin J, He P, Hoogenboom G (2011) Simulating water content, crop yield and nitrate-N loss under free and controlled tile drainage with subsurface irrigation using the DSSAT model. Agricultural Water Management 98, 1105–1111.
| Simulating water content, crop yield and nitrate-N loss under free and controlled tile drainage with subsurface irrigation using the DSSAT model.Crossref | GoogleScholarGoogle Scholar |
MacNeil MD, Skiles JW, Hanson JD (1985) Sensitivity analysis of a general rangeland model. Ecological Modelling 29, 57–76.
| Sensitivity analysis of a general rangeland model.Crossref | GoogleScholarGoogle Scholar |
McCall DG, Bishop-Hurley GJ (2003) A pasture growth model for use in a whole-farm dairy production model. Agricultural Systems 76, 1183–1205.
| A pasture growth model for use in a whole-farm dairy production model.Crossref | GoogleScholarGoogle Scholar |
Mohtar RH, Zhai T, Chen X (2000) A world wide web-based grazing simulation model (GRASIM). Computers and Electronics in Agriculture 29, 243–250.
| A world wide web-based grazing simulation model (GRASIM).Crossref | GoogleScholarGoogle Scholar |
Moore AD, Donnelly JR, Freer M (1997) GRAZPLAN: decision support systems for Australian grazing enterprises. III. Pasture growth and soil moisture submodels, and the GrassGro DSS. Agricultural Systems 55, 535–582.
| GRAZPLAN: decision support systems for Australian grazing enterprises. III. Pasture growth and soil moisture submodels, and the GrassGro DSS.Crossref | GoogleScholarGoogle Scholar |
Moreno LSB (2017) Modeling regrowth dynamics of two contrasting forage grasses in response to shade and nitrogen fertilization. PhD Thesis, University of Florida, Gainesville, FL, USA.
Noy-Meir I (1975) Stability of grazing systems: an application of predator-prey graphs. Journal of Ecology 63, 459–481.
| Stability of grazing systems: an application of predator-prey graphs.Crossref | GoogleScholarGoogle Scholar |
Parton WJ, Stewart JWB, Cole CV (1988) Dynamics of C, N, P and S in grassland soils: a model. Biogeochemistry 5, 109–131.
| Dynamics of C, N, P and S in grassland soils: a model.Crossref | GoogleScholarGoogle Scholar |
Pedreira BC, Pedreira CGS, Boote KJ, Lara MAS, Alderman PD (2011) Adapting the CROPGRO perennial forage model to predict growth of Brachiaria brizantha. Field Crops Research 120, 370–379.
| Adapting the CROPGRO perennial forage model to predict growth of Brachiaria brizantha.Crossref | GoogleScholarGoogle Scholar |
Pequeno DNL, Pedreira CGS, Boote KJ (2014) Simulating forage production of Marandu palisade grass (Brachiaria brizantha) with the CROPGRO-Perennial Forage model. Crop & Pasture Science 65, 1335–1348.
| Simulating forage production of Marandu palisade grass (Brachiaria brizantha) with the CROPGRO-Perennial Forage model.Crossref | GoogleScholarGoogle Scholar |
Pequeno DNL, Pedreira CGS, Sollenberger LE, Faria AFG, Silva LS (2015) Forage accumulation and nutritive value of Brachiariagrasses and Tifton 85 Bermudagrass as affected by harvest frequency and irrigation. Agronomy Journal 107, 1741–1749.
| Forage accumulation and nutritive value of Brachiariagrasses and Tifton 85 Bermudagrass as affected by harvest frequency and irrigation.Crossref | GoogleScholarGoogle Scholar |
Pereira LET, Paiva AJ, Guarda VA, Pereira PM, Caminha FO, Silva SC (2015) Herbage utilisation efficiency of continuously stocked Marandu palisade grass subjected to nitrogen fertilization. Scientia Agrícola 72, 114–123.
| Herbage utilisation efficiency of continuously stocked Marandu palisade grass subjected to nitrogen fertilization.Crossref | GoogleScholarGoogle Scholar |
Pontes LDS, Baldissera TC, Giostri AF, Stafin G, Santos BRC, Carvalho PCF (2017) Effects of nitrogen fertilization and cutting intensity on the agronomic performance of warm-season grasses. Grass and Forage Science 72, 663–675.
| Effects of nitrogen fertilization and cutting intensity on the agronomic performance of warm-season grasses.Crossref | GoogleScholarGoogle Scholar |
Porter CH, Jones JW, Adiku S, Gijsman AJ, Gargiulo O, Naab JB (2010) Modeling organic carbon and carbon-mediated soil processes in DSSAT V4.5. Operations Research 10, 274–278.
Rotz CA, Buckmaster DR, Mertens DR, Black JR (1989) DAFOSYM: A dairy forage system model for evaluating alternatives in forage conservation. Journal of Dairy Science 72, 3050–3063.
| DAFOSYM: A dairy forage system model for evaluating alternatives in forage conservation.Crossref | GoogleScholarGoogle Scholar |
Rymph SJ, Boote KJ, Irmak A, Mislevy P, Evers GW (2004) Adapting the CROPGRO model to predict growth and composition of tropical grasses: Developing physiological parameters. Proceedings - Soil and Crop Science Society of Florida 63, 37–51.
Santos GO, Faria RTD, Rodriguês GA, Dantas GDF, Dalri AB, Palaretti LF (2017) Forage yield and quality of marandugrass fertigated with treated sewage wastewater and mineral fertilizer. Acta Scientiarum. Agronomy 39, 515–523.
| Forage yield and quality of marandugrass fertigated with treated sewage wastewater and mineral fertilizer.Crossref | GoogleScholarGoogle Scholar |
Schapendonk AHCM, Stol W, Van Kraalingen DWG, Bouman BAM (1998) LINGRA, a sink/source model to simulate grassland productivity in Europe. European Journal of Agronomy 9, 87–100.
| LINGRA, a sink/source model to simulate grassland productivity in Europe.Crossref | GoogleScholarGoogle Scholar |
Smith EM, Loewer OJ (1983) Mathematical-logic to simulate the growth of two perennial grasses. Transactions of the American Society of Agricultural Engineers 26, 0878–0883.
| Mathematical-logic to simulate the growth of two perennial grasses.Crossref | GoogleScholarGoogle Scholar |
Steinhorst RK, Hunt HW, Innis GS, Haydock KP (1978) Sensitivity analyses of the ELM model. In ‘Grassland simulation model’. (Ed. GS Innis) pp. 231–255. (Springer: New York)
Stöckle CO, Donatelli M, Nelson R (2003) CropSyst a cropping systems simulation model. European Journal of Agronomy 18, 289–307.
| CropSyst a cropping systems simulation model.Crossref | GoogleScholarGoogle Scholar |
Vilela L, Soares WV, Sousa DMG, Macedo MCM (1998) ‘Calagem e adubação para pastagens na região do cerrado.’ Special Publication No. 37. (EMBRAPA: Brazil)
Wendi DA, Russelle MP (2007) Nutrient cycling in forage production systems. In ‘Forages: the science of grassland agriculture’. (Eds RF Barnes, CJ Nelson, KJ Moore, M Collins) pp. 137–148. (Blackwell: Ames, IA, USA)
Willmott CJ, Ackleson SG, Davis RE, Feddema JJ, Klink KM, Legates DR, Odonnell J, Rowe CM (1985) Statistics for the evaluation and comparison of models. Journal of Geophysical Research. Oceans 90, 8995–9005.
| Statistics for the evaluation and comparison of models.Crossref | GoogleScholarGoogle Scholar |
