Advancing databases and modeling techniques for yield-gap analysis in Brazilian agricultural systems

• Grant number: 21/00720-0
• Support Opportunities: Regular Research Grants
• Start date: June 01, 2021
• End date: November 30, 2023
• Field of knowledge: Agronomical Sciences - Agronomy - Agricultural Meteorology
• Principal Investigator: Fabio Ricardo Marin
• Grantee: Fabio Ricardo Marin
• Host Institution: Escola Superior de Agricultura Luiz de Queiroz (ESALQ). Universidade de São Paulo (USP). Piracicaba , SP, Brazil
• Associated researchers: Bruno Carneiro e Pedreira ; Carlos Guilherme Silveira Pedreira ; Fernando Campos Mendonca ; Ivo Zution Gonçalves ; Quirijn de Jong van Lier

Abstract

Global population will increase by 30% up to 2050. At the same time, consumption of livestock and dairy products will increase as a result of higher income per capita, and this will lead to a 50-70% increase in food demand. Brazil has a comparative advantage to help meet global food demand due to plentiful land and water resources. However, the rate of increase in average farmer yields has been slow and average yields have remained well below highest yields reported by progressive farmers and those measured in research stations, notably for animal producing systems. Yield gap (defined as the yield of an adapted crop cultivar when grown without water and nutrient limitations and kept free of diseases, weeds and insect pests) analysis provides a robust quantitative framework to quantify the gap and identify their causes and suggest the required interventions to close them. The overall objective of the proposal is to use a new approach to quantify the yield gap of sugarcane and soybean crops based on on-farm data e new modeling techniques to estimate potential and water-limited yield. Still, the proposal intends to advance in the collection of basic data and modeling for tropical pastures, thus analyzing the three main sectors in term of land use in Brazil. Such project will require collaboration among scientists from different institutions to achieve full geographic coverage and have the range of expertise needed to achieve the proposed goals. (AU)

Scientific publications

(7)

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

GONCALVES, IVO Z.; NEALE, CHRISTOPHER M. U.; SUYKER, ANDY; MARIN, FABIO R.. Evapotranspiration adjustment for irrigated maize-soybean rotation systems in Nebraska, USA. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY, v. N/A, p. 11-pg., 2023-09-06. (21/00720-0, 20/08365-1)

GASPAROTTO, LETICIA G.; ROSA, JULIANO M.; GRASSINI, PATRICIO; MARIN, FABIO R.. Developing an operational framework to diagnose yield gaps in commercial sugarcane mills. FIELD CROPS RESEARCH, v. 278, p. 9-pg., 2022-03-15. (17/50445-0, 21/00720-0, 18/06396-7, 17/20925-0)

IVO Z. GONÇALVES; LEANDRO G. DA COSTA; FÁBIO R. MARIN. Simulando a resposta da produtividade da cana-de-açúcar a reposições da ETc e quantidade da palhada no Brasil. Revista Brasileira de Engenharia Agrícola e Ambiental, v. 26, n. 8, p. 586-593, 2022-07-25. (21/00720-0, 13/16511-4, 17/20925-0, 14/12406-4, 20/08365-1, 14/19410-7)

FATTORI JUNIOR, IZAEL MARTINS; VIANNA, MURILO DOS SANTOS; MARIN, FABIO RICARDO. Assimilating leaf area index data into a sugarcane process-based crop model for estimation. EUROPEAN JOURNAL OF AGRONOMY, v. 136, p. 13-pg., 2022-05-01. (14/05887-6, 21/00720-0)

MARIN, FABIO R.; ZANON, ALENCAR J.; MONZON, JUAN P.; ANDRADE, JOSE F.; SILVA, EVANDRO H. F. M.; RICHTER, GEAN L.; ANTOLIN, LUIS A. S.; RIBEIRO, BRUNA S. M. R.; RIBAS, GIOVANA G.; BATTISTI, RAFAEL; et al. Protecting the Amazon forest and reducing global warming via agricultural intensification. NATURE SUSTAINABILITY, v. 5, n. 12, p. 14-pg., 2022-10-10. (17/50445-0, 21/00720-0, 18/06396-7, 17/20925-0)

GONCALVES, I. Z.; RUHOFF, A.; LAIPELT, L.; BISPO, R. C.; HERNANDEZ, F. B. T.; NEALE, C. M. U.; TEIXEIRA, A. H. C.; MARIN, F. R.. Remote sensing-based evapotranspiration modeling using geeSEBAL for sugarcane irrigation management in Brazil. Agricultural Water Management, v. 274, p. 12-pg., 2022-10-21. (20/08365-1, 21/00720-0)

FATTORI JR, IZAEL M.; MARIN, FABIO R.. Assessing the influence of crop model structure on the performance of data assimilation for sugarcane. COMPUTERS AND ELECTRONICS IN AGRICULTURE, v. 209, p. 11-pg., 2023-04-21. (14/05887-6, 21/00720-0)