Hostname: page-component-8448b6f56d-sxzjt Total loading time: 0 Render date: 2024-04-24T02:25:01.909Z Has data issue: false hasContentIssue false
  • English
  • Français

Assessment of sowing dates and plant densities using CSM-CROPGRO-Soybean for soybean maturity groups in low latitude

Published online by Cambridge University Press:  05 April 2021

L. S. Sampaio ,
R. Battisti Open the ORCID record for R. Battisti [Opens in a new window] ,
M. A. Lana Open the ORCID record for M. A. Lana [Opens in a new window]  and
K. J. Boote
L. S. Sampaio
Affiliation:
Universidade Federal Rural da Amazônia, Instituto de Ciências Agrárias, Belém, PA, Brazil
R. Battisti*
Affiliation:
Universidade Federal de Goiás, Escola de Agronomia, Goiânia, GO, Brazil
M. A. Lana
Affiliation:
Department of Crop Production Ecology, Swedish University of Agricultural Sciences, Uppsala, Sweden
K. J. Boote
Affiliation:
Agricultural and Biological Engineering Department, University of Florida, Gainesville, FL, USA
*
Author for correspondence: R. Battisti, E-mail: battisti@ufg.br
Get access Rights & Permissions [Opens in a new window]

Abstract

Crop models can be used to explain yield variations associated with management practices, environment and genotype. This study aimed to assess the effect of plant densities using CSM-CROPGRO-Soybean for low latitudes. The crop model was calibrated and evaluated using data from field experiments, including plant densities (10, 20, 30 and 40 plants per m2), maturity groups (MG 7.7 and 8.8) and sowing dates (calibration: 06 Jan., 19 Jan., 16 Feb. 2018; and evaluation: 19 Jan. 2019). The model simulated phenology with a bias lower than 2 days for calibration and 7 days for evaluation. Relative root mean square error for the maximum leaf area index varied from 12.2 to 31.3%; while that for grain yield varied between 3 and 32%. The calibrated model was used to simulate different management scenarios across six sites located in the low latitude, considering 33 growing seasons. Simulations showed a higher yield for 40 pl per m2, as expected, but with greater yield gain increments occurring at low plant density going from 10 to 20 pl per m2. In Santarém, Brazil, MG 8.8 sown on 21 Feb. had a median yield of 2658, 3197, 3442 and 3583 kg/ha, respectively, for 10, 20, 30 and 40 pl per m2, resulting in a relative increase of 20, 8 and 4% for each additional 10 pl per m2. Overall, the crop model had adequate performance, indicating a minimum recommended plant density of 20 pl per m2, while sowing dates and maturity groups showed different yield level and pattern across sites in function of the local climate.

Keywords

Crop managementcrop modellingGlycine max Llate maturity grouppotential yield
Type
Crops and Soils Research Paper
Information
The Journal of Agricultural Science , Volume 158 , Issue 10: iCROPM 2020 , December 2020 , pp. 819 - 832
Check for updates
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Alliprandini, LF, Abatti, C, Bertagnolli, PF, Cavassim, JE, Gabe, HL, Kurek, A, Marsumoto, MN, Oliveira, MAR, Pitol, C, Prado, LC and Steckling, C (2009) Understanding soybean maturity groups in Brazil: environment, cultivar classification, and stability. Crop Science 49, 801–808.CrossRefGoogle Scholar
Alvares, CA, Stape, JL, Sentelhas, PC, Gonçalves, JLM and Sparovek, G (2013) Köppen's climate classification map for Brazil. Meteorologische Zeirschrift 22, 711–728.CrossRefGoogle Scholar
Bajgain, R, Kawasaki, Y, Akamatsu, Y, Tanaka, Y, Kawamura, H, Katsura, K and Shiraiwa, T (2015) Biomass production and yield of soybean grown under converted paddy fields with excess water during the early growth stage. Field Crops Research 180, 221–227.CrossRefGoogle Scholar
Balbinot Junior, AA, de Oliveira, MCN, Zucareli, C, Ferreira, AS, Werner, F and Silva, MAA (2018) Analysis of phenotypic plasticity in indeterminate soybean cultivars under different row spacing. Australian Journal of Crop Science 12, 648–654.CrossRefGoogle Scholar
Ball, RA, Purcell, LC and Vories, ED (2000) Optimizing soybean plant population for a short-season production system in the southern USA. Crop Science 40, 757–764.CrossRefGoogle Scholar
Banterng, P, Hoogenboom, G, Patanothai, A, Singh, P, Wani, SP, Pathak, P, Tongpoon-pol, S, Atichart, S, Srihaban, P, Buranaviriyakul, S, Jintrawet, A and Nguyen, TC (2009) Application of the cropping system model (CSM)-CROPGRO-soybean for determining optimum management strategies for soybean in tropical environments. Journal of Agronomy and Crop Science 196, 231–242.CrossRefGoogle Scholar
Basso, B, Ritchie, JT, Pierce, JF, Braga, RP and Jones, JW (2001) Spatial validation of crop models for precision agriculture. Agricultural Systems 68, 97–112.CrossRefGoogle Scholar
Battisti, R and Sentelhas, PC (2019) Characterizing Brazilian soybean-growing regions by water deficit patterns. Field Crop Research 240, 95–105.CrossRefGoogle Scholar
Battisti, R, Sentelhas, PC and Boote, KJ (2017) Inter-comparison of performance of soybean crop simulation models and their ensemble in southern Brazil. Field Crops Research 200, 28–37.CrossRefGoogle Scholar
Battisti, R, Sentelhas, PC, Parker, PS, Nendel, C, Câmara, GMS, Farias, JRB and Basso, CJ (2018) Assessment of crop-management strategies to improve soybean resilience to climate change in Southern Brazil. Crop and Pasture Science 69, 154–162.CrossRefGoogle Scholar
Battisti, R, Ferreira, MDP, Tavares, ÉB, Knapp, FM, Bender, FD, Casaroli, D and Alves Júnior, J (2020a) Rules for grown soybean-maize cropping system in midwestern Brazil: food production and economic profits. Agricultural Systems 182, 102850.CrossRefGoogle Scholar
Battisti, R, Casaroli, D, Paixão, JS, Alves Júnior, J, Evangelista, AWP and Mesquita, M (2020b) Assessment of soybeans crop management strategies using crop growth models for central Brazil. In Mirschel, W, Terleev, V and Wenkel, KO (eds), Landscape Modelling and Decision Support. Cham, Switzerland: Springer, pp. 525–543. doi:10.1007/978-3-030-37421-1_27.CrossRefGoogle Scholar
Board, JE (2000) Light interception efficiency and light quality affect yield compensation of soybean at low plant populations. Crop Science 40, 1285–1294.CrossRefGoogle Scholar
Boote, KJ and Pickering, NB (1994) Modeling photosynthesis of row crop canopies. HortScience 29, 1423–1434.CrossRefGoogle Scholar
Boote, KJ, Jones, JW, Hoogenboom, G and Pickering, NB (1998) Simulation of crop growth: CROPGRO model. In Peart, RM and Curry, RB (eds), Agricultural Systems Modeling and Simulation. New York: Marcel Dekker, pp. 651–692.Google Scholar
Boote, KJ, Jones, JW, Batchelor, WD, Nafziger, ED and Myers, O (2003) Genetic coefficients in the CROPGRO–soybean model. Agronomy Journal 95, 32–51.Google Scholar
Boote, KJ, Jones, JW, White, JW, Asseng, S and Lizaso, JI (2013) Putting mechanisms into crop production models. Plant, Cell and Environment 36, 1658–1672.CrossRefGoogle ScholarPubMed
Carciochi, WD, Schwalbert, R, Andrade, FH, Corassa, GM, Carter, P, Gaspar, AP, Schmidt, J and Ciampitti, IA (2019) Soybean seed yield response to plant density by yield environment in North America. Agronomy Journal 111, 1923–1932.CrossRefGoogle Scholar
Carpenter, AC and Board, JE (1997) Branch yield components controlling soybean yield stability across plant populations. Crop Science 37, 885–891.CrossRefGoogle Scholar
Carpentieri-Pípolo, V, de Almeida, LA and de Kiihl, RAS (2002) Inheritance of a long juvenile period under short-day conditions in soybean. Genetics and Molecular Biology 25, 463–469.CrossRefGoogle Scholar
Cattelan, AJ and Dall'Agnol, A (2018) The rapid soybean growth in Brazil. Oilseeds and Fats Crops and Lipids 25, 1–12.Google Scholar
CONAB (2020) Crops historical data. Available at: https://www.conab.gov.br/info-agro/safras/. Accessed 02 March 2020 (in Portuguese).Google Scholar
Corassa, GM, Amado, TJC, Strieder, ML, Schwalbert, R, Pires, JLF, Carter, PR and Ciampitti, IA (2018) Optimum soybean seeding rates by yield environment in southern Brazil. Agronomy Journal 110, 2430–2438.CrossRefGoogle Scholar
Del Ponte, EM, Godoy, CV, Li, X and Yang, XB (2006) Predicting severity of Asian soybean rust epidemics with empirical rainfall models. Phytopathology 96, 797–803.CrossRefGoogle ScholarPubMed
Destro, D, Afonso, R, Kiihl, DS, Alves, L and Almeida, D (2001) Photoperiodism and genetic control of the long juvenile period in soybean. Crop Breeding and Applied Biotechnology 1, 72–92.CrossRefGoogle Scholar
Ewert, F, Rötter, RP, Bindi, M, Webber, H, Trnka, M, Kersebaum, KC, Olesen, JE, van Ittersum, MK, Janssen, S, Rivington, M, Semenov, MA, Wallach, D, Porter, JR, Stewart, D, Verhagen, J, Gaiser, T, Palosuo, T, Tao, F, Nendel, C, Roggero, PP, Bartosová, L and Asseng, S (2015) Crop modelling for integrated assessment of risk to food production from climate change. Environmental Modelling & Software 72, 287–303.CrossRefGoogle Scholar
FAOSTAT (2020) Food and agriculture data. Available at: http://www.fao.org/faostat/en/#home. Accessed 15 February 2020.Google Scholar
Ferreira, AS, Zucareli, C, Werner, F, Fonseca, ICB and Balbinot Junior, AA (2020) Minimum optimal seeding rate for indeterminate soybean cultivars grown in tropics. Agronomy Journal 112, 2092–2102.CrossRefGoogle Scholar
Grimm, SS, Jones, JW, Boote, KJ and Hesketh, JD (1993) Parameter estimation for predicting flowering date of soybean cultivars. Crop Science 33, 137–144.CrossRefGoogle Scholar
Halsnaes, K and Taerup, S (2009) Development and climate change: a mainstreaming approach for assessing economic, social, and environmental impacts of adaptation measures. Environmental Management 43, 765–778.CrossRefGoogle ScholarPubMed
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 and Jones, JW (2017) Decision Support System for Agrotechnology Transfer (DSSAT) Version 4.7.0.0. Gainesville, Florida, USA: DSSAT Foundation. https://DSSAT.netGoogle Scholar
Hoogenboom, G, Porter, CH, Boote, KJ, Shelia, V, Wilkens, PW, Singh, U, White, JW, Asseng, S, Lizaso, JI, Moreno, LP, Pavan, W, Ogoshi, R, Hunt, LA, Tsuji, GY and Jones, JW (2019) The DSSAT crop modeling ecosystem. In Boote, KJ (ed.), Advances in Crop Modeling for a Sustainable Agriculture. Cambridge, United Kingdom: Burleigh Dodds Science Publishing, pp. 173–216.CrossRefGoogle Scholar
Hu, M and Wiatrak, P (2012) Effect of planting date on soybean growth, yield, and grain quality: review. Agronomy Journal 104, 785–790.CrossRefGoogle Scholar
Hunt, LA and Boote, KJ (1998) Data for model operation, calibration, and evaluation. In Tsuji, GY, Hoogenboom, G and Thornton, PK (eds), Understanding Options for Agricultural Production. Dordrecht: Springer, pp. 9–39.CrossRefGoogle Scholar
IBGE (National Institute of Geography and Statistics) (2020) Agricultural Production. In Portuguese. Available at http://www.sidra.ibge.gov.br/bda/pesquisas/pam. Accessed 12 April 2020 (in Portuguese).Google Scholar
Jones, JW, Jianqiang, H, Boote, KJ, Wilkens, P, Porter, CH and Hu, Z (2011) Estimating DSSAT cropping system cultivar-specific parameters using Bayesian techniques. In Ahuja, LR and Ma, L (eds). Methods of Introducing System Models Into Agricultural Research. Madison: ASA; CSSA; SSSA, pp. 365–394, 2011. (Advances in Agricultural Systems Modeling, 2).Google Scholar
Justino, LF, Alves Júnior, J, Battisti, R, Heinemann, AB, Leite, CV, Evangelista, AWP and Casaroli, D (2019) Assessment of economic returns by using a central pivot system to irrigate common beans during the rainfed season in Central Brazil. Agricultural Water Management 224, 104749.CrossRefGoogle Scholar
Lee, CD, Egli, DB and TeKrony, DM (2008) Soybean response to plant population at early and late planting dates in the Mid-South. Agronomy Journal 100, 971–976.CrossRefGoogle Scholar
Lima, MJA, Oliveira, EC, Sampaio, LS, William, FC and Souza, PJOP (2019) Agrometeorological analysis of the soybean potentiality in an Amazonian environment. Pesquisa Agropecuária Tropical 49, 1–9.CrossRefGoogle Scholar
Liu, X, Wu, JA, Ren, H, Qi, Y, Li, C, Cao, J, Zhang, X, Zhang, Z, Cai, Z and Gai, J (2017) Genetic variation of world soybean maturity date and geographic distribution of maturity groups. Breeding Science 67, 221–232.CrossRefGoogle ScholarPubMed
Makowski, D, Wallach, D and Tremblay, M (2002) Using Bayesian approach to parameter estimation: comparison of the GLUE and MCMC methods. Agronomie 22, 191–203.CrossRefGoogle Scholar
MAPA (Minister of Agriculture, Livestock and Food Supply) (2020) Agroclimatic Risk Zoning. Available from: http://www.agricultura.gov.br/politica-agricola/zoneamento-agricola. Accessed: 01 December 2020.Google Scholar
Nakano, S, Purcell, LC, Homma, K and Shiraiwa, T (2019) Modeling leaf area development in soybean (Glycine max L.) based on the branch growth and leaf elongation. Plant Production Science 22, 1–13.Google Scholar
Nendel, C, Berg, M, Kersebaum, KC, Mirschel, W, Specka, X, Wegehenkel, M, Wendel, KO and Wieland, R (2011) The MONICA model: testing predictability for crop growth, soil moisture and nitrogen dynamics. Ecological Modelling 222, 1614–1624.CrossRefGoogle Scholar
Nóia Junior, RS and Sentelhas, PC (2019) Soybean-maize succession in Brazil: impacts of sowing dates on climate variability, yields and economic profitability. European Journal of Agronomy 103, 140–151.CrossRefGoogle Scholar
Pasley, HR, Huber, I, Castellano, MJ and Archontoulis, SV (2020) Modeling flood-induced stress in soybeans. Frontiers in Plant Science 11, 1–13.CrossRefGoogle ScholarPubMed
Purcell, LC, Ball, RA, Reaper, JD and Voires, ED (2002) Radiation use efficiency and biomass production in soybean at different plant population densities. Crop Science 42, 172–177.CrossRefGoogle ScholarPubMed
RADAMBRASIL (1974) Survey of natural resources. Vol. 4. (In Portuguese.) Brazilian Gov., Ministry of Mines and Energy, Rio de Janeiro.Google Scholar
Salmerόn, M, Purcell, LC, Vories, ED and Shannon, G (2017) Simulation of genotype-by-environment interactions on irrigated soybean yields in the U.S. Midsouth. Agricultural Systems 150, 120–129.CrossRefGoogle Scholar
Salmerón, M, Gbur, EE, Bourland, FM, Earnest, L, Golden, BR and Purcell, LC (2015) Soybean maturity group choices for maximizing radiation interception across planting dates in the Midsouth United States. Agronomy Journal 107, 2132–2142.CrossRefGoogle Scholar
Setiyono, TD, Cassman, KG, Specht, JE, Dobermann, A, Weiss, A, Yang, H, Conley, SP, Robinson, AP, Pedersen, P and De Bruin, JL (2010) Simulation of soybean growth and yield in near-optimal growth conditions. Field Crop Research 119, 161–174.CrossRefGoogle Scholar
Silva, EHFM, Pereira, RAA, Gonçalves, AO, Bordignon, ÁJZ and Marin, FR (2017) Simulation of soybean yield in Piracicaba-SP based on climate change. Agrometeoros 25, 9–17.Google Scholar
Sinclair, TR, Neumaier, N, Farias, JRB and Nepomuceno, AL (2005) Comparison of vegetative development in soybean cultivars for low-latitude environments. Field Crops Research 92, 53–59.CrossRefGoogle Scholar
Spehar, CR, Francisco, ER and Pereira, EA (2015) Yield stability of soybean cultivars in response to sowing date in the lower latitude Brazilian Savannah Highlands. Journal of Agricultural Science 153, 1059–1068.CrossRefGoogle Scholar
Tagliapietra, EL, Streck, NA, da Rocha, TSM, Richter, GL, da Silva, MR, Cera, JC and Junior Zanon, A (2018) Optimum leaf area index to reach soybean yield potential in subtropical environment. Agronomy Journal 110, 932–938.CrossRefGoogle Scholar
Teixeira, WWR, Battisti, R, Sentelhas, PC, de Moraes, MF and de Oliveira Junior, A (2019) Uncertainty assessment of soya bean yield gaps using DSSAT-CSM-CROPGRO-soybean calibrated by cultivar maturity groups. Journal of Agronomy and Crop Science 205, 1–12.CrossRefGoogle Scholar
Van Roekel, RJ, Purcell, LC and Salmerón, M (2015) Physiological and management factors contributing to soybean potential yield. Field Crops Research 182, 86–97.CrossRefGoogle Scholar
Wallach, D, Makowski, D and Jones, JW (2006) Working with Dynamic Crop Models: Evaluation, Analysis, Parameterization and Applications. Amsterdam: Elsevier, pp. 1–462.Google Scholar
Xavier, AC, King, CW and Scanlon, BR (2015) Daily gridded meteorological variables in Brazil (1980–2013). International Journal of Climatology 36, 2644–2659.CrossRefGoogle Scholar
Zdziarski, AD, Todeschini, MH, Milioli, AS, Woyann, LG, Madureira, A, Stoco, MG and Benin, G (2018) Key soybean maturity groups to increase grain yield in Brazil. Crop Science 58, 1155–1165.CrossRefGoogle Scholar
Supplementary material: PDF

Sampaio et al. supplementary material

Tables S1-S2 and Figures S1-S7

Download Sampaio et al. supplementary material(PDF)
PDF 548.5 KB