Alexandre Bryan Heinemann
@embrapa.br
Embrapa Arroz e Feijão
Scopus Publications
- Genotype × environment interaction in a national network of common bean trials across the three cropping seasons in Brazil
Demila Duarte da Mata Cruz, Alexandre Bryan Heinemann, Paula Pereira Torga, Eduardo Almeida Alves, Rafael Tassinari Resende
Euphytica, 2026
The cultivation of common bean in Brazil typically occurs in three annual cropping seasons across the country’s major geographic regions, exposing genotypes to contrasting environmental conditions across time (seasons and years) and space (locations). With this aim, this study investigated the influence of the G × E interaction on common bean yield across different sites, seasons, and years. It evaluated the contributions of fixed and random effects. Data from 424 multi-environment trials (METs) conducted by Embrapa between 2011 and 2018 were used, involving 87 genotypes across three cropping seasons (Wet, Dry, and Winter) distributed over 71 locations. Genetic and environmental effects were estimated through linear mixed models fitted with the REML/BLUP method. In addition, multivariate analyses, including the GGE Biplot, were used to decompose and visualize G × E effects, while missing data were imputed via Principal Component Analysis (PCA). Environmental stratification and the identification of mega-environments enabled grouping sites with similar characteristics based on the presence or absence of G × E interactions. The stability and adaptability analysis of the cultivars, based on a ranking that considered the particularities of the PRVG, MHPRVG, Lin and Binns, Wricke’s Wi, and Finlay–Wilkinson indices, revealed distinct patterns of behavior across the three seasons. - A Bayesian Spatiotemporal Functional Model for Data With Block Structure and Repeated Measures
David H. da Matta, Mariana R. Motta, Nancy L. Garcia, Alexandre B. Heinemann
Environmetrics, 2026
The analysis of spatiotemporal data is fundamental across multiple scientific disciplines, particularly in assessing the behavior of climate effects over space and time. A key challenge in this area is effectively capturing recurring climate phenomena, such as El Niño/La Niña (ENSO) phases, which induce prolonged periods of similar weather patterns across affected regions. To address this, our study introduces a novel spatiotemporal regression model that explicitly incorporates block structures representing these recurring climate effects. These blocks accommodate ENSO phases and manage the within‐block correlations and shared characteristics, enhancing the model's ability to capture the influence of such phenomena on precipitation variability. The model further integrates functional predictors of both fixed and random nature, along with spatial covariance modeled via the Matérn class, to accommodate complex spatial, temporal, and block‐related structures. Motivated by a monthly precipitation dataset from meteorological stations in Goiás State, Brazil, spanning 21 years (1980–2001), our approach assigns spatial effects to individual stations, temporal effects to months, blocks to ENSO phases, and repeated measures to years within those blocks. The results from simulation studies demonstrate the model's robustness and effectiveness, providing deeper insight into how recurring climate effects like ENSO impact rainfall patterns. This framework represents a significant methodological advancement in spatiotemporal modeling, highlighting the importance of explicitly modeling and estimating the effects of recurrent climate phenomena through block structures. - Optimizing Sowing Calendars for Climate-Resilient Common Bean Production in Central-Southern Brazil: A Functional Data Analysis Approach
Ludmilla Ferreira Justino, Alexandre Bryan Heinemann, David Henriques da Matta, Luís Fernando Stone, Felipe Waks Andrade, et al.
Resources, 2026
Addressing the intertwined challenges of food security and climate vulnerability requires robust and regionally tailored strategies for staple crops such as common beans. Although adjusting sowing dates is a key adaptive practice, spatio-temporal climate variability complicates the identification of optimal planting windows. This study integrates crop modeling with Functional Data Analysis (FDA) to quantify sowing-date-dependent yield losses for rainfed common beans across Central-Southern Brazil. The CSM-CROPGRO-Dry Bean model, driven by long-term climate data (1980–2016), soil properties, and management practices, was used to simulate yields for the BRS Estilo cultivar. FDA was subsequently applied to cluster yield-loss curves across municipalities and growing seasons, generating representative regional risk profiles. The results reveal clear spatial patterns. During the wet season, earlier sowing minimizes losses in Goiás, Minas Gerais, and western Paraná, whereas later sowing is beneficial in São Paulo, Santa Catarina, and eastern Paraná. In the dry season, earlier sowing consistently reduces losses across most regions. These patterns are primarily driven by water deficits and suboptimal temperatures during critical phenological phases. The resulting spatio-temporal sowing calendar provides an evidence-based decision-support tool to help farmers mitigate climatic risks. Moreover, it offers a scientific foundation for policymakers to refine sustainable management practices, improve crop insurance design, and enhance agricultural resilience and productivity under increasing climate uncertainty. - Soybean yield potencial in petric plinthosols: climate and economic interactions
Bragantia, 2026 - COSMIC-SWAMP: IoT Processing of Cosmic-Ray Soil Moisture Sensors
Patrick Stowell, Carlos Kamienski, Alexandre Heideker, Dener Silva, João Kleinschmidt, et al.
IEEE Access, 2026 - Vegetation Indices as Rapid, Non-Destructive Tool to Assess Nitrogen Status in Irrigated Rice
Marcos Paulo dos Santos, Nívea Patrícia Ribeiro Reges, Alberto Baêta dos Santos, Luís Fernando Stone, Alexandre Bryan Heinemann
Modern Agriculture, 2025
The use of optical radiation sensors is a promising strategy for nitrogen management as it reduces the costs of chemical analyses and allows quick decision‐making in the supplementary application of N to irrigated rice. By combining three spectral reflectance bands (red, far‐red, and near‐infrared), 22 vegetation indices (VIs) were computed and assessed for their effectiveness in estimating the nitrogen status of rice crops. The results indicated that the selected VIs considerably underestimated dry leaf biomass (DLB) and did not efficiently estimate N status parameters, such as leaf N concentration (LNC) and leaf N uptake, at the vegetative stage. The large variations in these N status parameters can be explained by the VI in subsequent stages. The VI selected in the parametrisation process was promising for explaining variation in DLB and leaf area index at the reproductive and grain‐filling stages. However, the VI showed low performance in estimating LNC at the reproductive stage. The modified red‐edge soil‐adjusted VI and normalised difference red‐edge index showed high performance in estimating the N nutrition index in the growth stage and across the whole crop cycle. These results show the importance of using active sensors for effective crop N status estimation. - Irrigation strategies for upland rice cultivars in Brazil: Physiological responses and agronomic performance
Carlos Alberto Quiloango-Chimarro, Rubens Duarte Coelho, Alice da Silva Gundim, Jéfferson de Oliveira Costa, Tainá Ferreira da Rocha, et al.
Agricultural Water Management, 2025 - Rice-based cropping systems in Brazil: Irrigated and rainfed
Giovana Ghisleni Ribas, Alexandre Bryan Heinemann, Luís Fernando Stone, Adriano Pereira de Castro, Nereu Augusto Streck, et al.
Crop and Environment, 2025 - Envirotyping-informed mixed models to study the climatic drivers and yield seasonal variation for common beans in Brazil
Alexandre Bryan Heinemann, David Henriques da Matta, Luís Fernando Stone, Germano Costa-Neto, Rafael T. Resende, et al.
European Journal of Agronomy, 2025 - Spatio-temporal dynamics of water stress for common bean production in Goiás, Brazil
Ludmilla Ferreira Justino, Alexandre Bryan Heinemann, David Henriques da Matta, Luís Fernando Stone, Paulo Augusto de Oliveira Gonçalves, et al.
Theoretical and Applied Climatology, 2025 - Characterization of common bean production regions in Brazil using machine learning techniques
Ludmilla Ferreira Justino, Alexandre Bryan Heinemann, David Henriques da Matta, Luís Fernando Stone, Paulo Augusto de Oliveira Gonçalves, et al.
Agricultural Systems, 2025 - Defining the target population of environments (TPE) for enviromics studies using R-based GIS tools
Crop Breeding and Applied Biotechnology, 2025 - Harnessing crop models and machine learning for a spatial-temporal characterization of irrigated rice breeding environments in Brazil
Alexandre Bryan Heinemann, Germano Costa-Neto, David Henriques da Matta, Igor Kuivjogi Fernandes, Luís Fernando Stone
Field Crops Research, 2024 - Environmental clusters defining breeding zones for tropical irrigated rice in Brazil
Germano Costa‐Neto, David Henriques da Matta, Igor Kuivjogi Fernandes, Luís Fernando Stone, Alexandre Bryan Heinemann
Agronomy Journal, 2024 - GIS-FA: an approach to integrating thematic maps, factor-analytic, and envirotyping for cultivar targeting
Maurício S. Araújo, Saulo F. S. Chaves, Luiz A. S. Dias, Filipe M. Ferreira, Guilherme R. Pereira, et al.
Theoretical and Applied Genetics, 2024 - Phenology, gas exchange, biomass accumulation, and irrigated rice yield under alternative irrigation managements
Marcos Paulo dos Santos, Alexandre Bryan Heinemann, Luís Fernando Stone, Mellissa Ananias Soler da Silva, Anna Cristina Lanna, et al.
Agronomy Journal, 2024 - Strategies for fungicide application based on the yield response of common bean genotypes under El Niño-Southern Oscillation (ENSO)
Alexandre Bryan Heinemann, Patrícia Valle Pinheiro, David Henriques da Matta, Luís Fernando Stone, Pedro Araújo Pietrafesa, et al.
European Journal of Agronomy, 2024 - Climate drivers affecting upland rice yield in the central region of Brazil
Alexandre Bryan Heinemann, Luís Fernando Stone, Guilherme Custódio Cândido Silva, David Henriques da Matta, Ludmilla Ferreira Justino, et al.
Pesquisa Agropecuaria Tropical, 2024 - Flowering prediction for flood-irrigated rice in the Midwest and North regions of Brazil
Revista Ceres, 2024 - Phenological Restriction of the Oryza (v3) Model
Gutemberg Resende Honorio Filho, Alexandre Bryan Heinemann, David Henriques da Matta, Luís Fernando Stone, Santiago Vianna Cuadra, et al.
Revista Brasileira De Meteorologia, 2024 - Calibration and evaluation of new irrigated rice cultivars in the SimulArroz model
Anderson H. Poersch, Nereu A. Streck, Alexandre B. Heinemann, Silvio Steinmetz, Alencar J. Zanon, et al.
Revista Brasileira De Engenharia Agricola E Ambiental, 2024 - WATER STRESS IN MODERN UPLAND RICE CULTIVARS: A MULTIVARIATE STUDY BETWEEN PHYSIOLOGICAL TRAITS AND YIELD
Carlos Quiloango-Chimarro, Rubens Duarte Coelho, Jéfferson Costa, Alice da Silva Gundim, Alexandre Bryan Heinemann
Irriga, 2023 - Analysis of Goiás State rainfall and temperature similarity patterns during the El Niño-Southern Oscillation phenomenon phases across the years
David Henriques da Matta, Caio Augusto dos Santos Coelho, Leydson Lara dos Santos, Luís Fernando Stone, Alexandre Bryan Heinemann
Theoretical and Applied Climatology, 2023 - Protecting the Amazon forest and reducing global warming via agricultural intensification
Fabio R. Marin, Alencar J. Zanon, Juan P. Monzon, José F. Andrade, Evandro H. F. M. Silva, et al.
Nature Sustainability, 2022 - Enviromic prediction is useful to define the limits of climate adaptation: A case study of common bean in Brazil
Alexandre Bryan Heinemann, Germano Costa-Neto, Roberto Fritsche-Neto, David Henriques da Matta, Igor Kuivjogi Fernandes
Field Crops Research, 2022
