DSpace Coleção: Artigo em periódico indexado (CNPTIA)

Cloud-based fusion of Sentinel-1 Radar, MODIS and soil moisture data for resolution-refined evapotranspiration mapping in mountain coffee systems.

2026-01-01T00:00:00Z

Título: Cloud-based fusion of Sentinel-1 Radar, MODIS and soil moisture data for resolution-refined evapotranspiration mapping in mountain coffee systems. Autoria: KLINKE NETO, G.; OLIVEIRA, A. H.; BOLFE, E. L.; BERGIER, I.; GOULART, A. J. H. Conteúdo: Accurate monitoring of hydrological dynamics in complex perennial landscapes is a cornerstone for tropical agricultural sustainability. Traditional energy balance models based on orbital optical data often face methodological bottlenecks due to cloud cover and the “greening myth,” where optical indices fail to capture immediate water stress due to the non-linear decoupling between stomatal closure and pigment loss. This study developed a cloud-integrated multisensor framework to estimate actual evapotranspiration (ETa) at a refined 100 m resolution in mountain coffee systems, utilizing active microwave proxies from Sentinel-1. We fused polarimetric metrics—Degree of Polarization (DoP) and Shannon Entropy (SE)—with land surface temperature and soil moisture data. Multiple Linear Regression (MLR) was compared against non-linear algorithms (Random Forest and SVR) to prioritize model parsimony and physical interpretability. The results show that MLR emerged as the most parsimonious and suitable model within this localized dataset scope (R2 = 0.872; RMSE = 2.916 mm/8-day), outperforming complex “black-box” architectures. Soil moisture emerged as the dominant environmental driver of ETa variability, while SAR-based metrics served as sensitive mechanical proxies for canopy geometric heterogeneity and macro-structural variations. Cross-correlation analysis revealed a 16-day lag, empirically indicating that biophysical water shifts temporally precede geometric canopy alterations. Operationally, this framework ensures temporal continuity under persistent cloud cover and provides high-fidelity spatial detailing for precision water management. This approach offers an auditable and scalable tool for watershed planning and climate resilience in tropical agriculture.

Induced chilling injury in banana: physiological and quality responses of cultivars to natural cold front.

2026-01-01T00:00:00Z

Título: Induced chilling injury in banana: physiological and quality responses of cultivars to natural cold front. Autoria: LIMA, J. D.; PEREIRA, M. R.; ROZANE, D. E.; SILVA, S. H. M. G. da; GOMES, E. N.; NOMURA, E. S.; GIACHETTO, P. F. Conteúdo: Banana fruits are susceptible to chilling injury (CI) under field conditions, which significantly impairs fruit quality. Cold tolerance varies among genotypes; however, only a limited number of cultivars have been identified as tolerant and are commercially cultivated. This study aimed to investigate the physiological responses and quality attributes of banana cultivars exposed to natural cold fronts during development, compared with fruits developed under summer conditions. Furthermore, it evaluated whether the B genome confers greater cold tolerance, driven by a more efficient antioxidant mechanism, thereby supporting its recommendation for cultivation in regions prone to low temperatures. Bunches were harvested in winter following five natural cold fronts, during which air temperatures fell below 12 °C (137 h). The experimental design followed a completely randomized design in a factorial arrangement. Consecutive cold fronts intensified CI symptoms up to the fourth exposure event. CI severity was highest in ‘Grande Naine’ (AAA), which exhibited lower L*, a*, and b* values at the ripe stage compared to ‘BRS Princesa’ (AAAB) and ‘Prata Catarina’ (AAB), along with greater deviations relative to summer-harvested fruits. Malondialdehyde (MDA), total phenolic content, and antioxidant enzyme activities (SOD, CAT, APX, and POD) in the peel of unripe fruits were significantly higher during winter, particularly in ‘BRS Princesa’ and ‘Prata Catarina’, compared to ‘Grande Naine’. Proline accumulation followed a similar pattern, with the highest levels observed in ‘BRS Princesa’, followed by ‘Prata Catarina’ and ‘Grande Naine’. The findings indicate that ‘BRS Princesa’ exhibits greater tolerance to cold stress and highlights of the contribution of the B genome. Phenolic content was identified as a consistent marker of seasonal variation across cultivars.

ProCarbon‐Soil: a dynamic model for improved model‐data compatibility in carbon farming.

2026-01-01T00:00:00Z

Título: ProCarbon‐Soil: a dynamic model for improved model‐data compatibility in carbon farming. Autoria: BARIONI, L. G.; VALLADÃO, B. A.; MOURÃO, V. H. M.; EWING, R. P.; KARATAY, Y. N.; DAMIAN, J. M.; MELÍCIO, V. C.; REJAILI, R. P. A.; SILVA, R. O. Conteúdo: Carbon farming is a nature-based solution to capture atmospheric CO2 and store it as soil organic carbon (SOC). Carbon farming trading schemes (CFTS) incentivize farmers to adopt these practices. Integral to CFTS is forecasting SOC changes, typically achieved using traditional multicompartmental soil carbon models (mSCM), and monitoring total SOC stocks. However, traditional mSCM simulate unmeasurable compartments, leading to overparameterization and indeterminable partitioning among carbon compartments, suggesting a need for structural improvements. The ProCarbon-Soil (PROCS) model addresses this need by abstracting fundamental principles of mSCM, reducing SOC state variables to two (total carbon and decomposability), and employing only one stabilization parameter, compared to the four to eight state variables and 7–20 parameters typically required by mSCM. We mathematically derive methods that can use successive carbon measurements to estimate decomposability and initialize the model. PROCS can handle environmental modifiers and events such as crop rotations, tillage, and manuring events, and respond to soil characteristics and weather conditions. Tests show that PROCS can accurately reproduce synthetic SOC trajectories generated by an mSCM with perturbed parameters using short-term data (12 years) with acceptable accuracy (median root mean square error <1.03 Mg ha−1 and absolute median of mean bias <0.55 Mg ha−1). In a cross-validation test, the mean normalized root mean square error (NRMSE) closely aligns with the coefficient of variation of white noise introduced in the synthetic data (4.15% vs. 4.00%, respectively) for augmented carbon inflow scenarios, whereas the model exhibits higher errors for the no-carbon-inflow scenario (NRMSE = 5.48%, 7.25%, and 8.99% for 12, 24, and 50 years, respectively).

Thermal sensitivity and yield of Arabica coffee under climatic variability.

2026-01-01T00:00:00Z

Título: Thermal sensitivity and yield of Arabica coffee under climatic variability. Autoria: NORONHA, M. V. O.; SILVA, J. P.; PLACHI, M. A.; SANTANA, D. M. de; UMBURANAS, R. C.; FAVARIN, J. L.; ROMANI, L. A. S.; MASSRUHÁ, S. M. F. S.; DOURADO NETO, D. Conteúdo: Coffee yield is influenced by climatic factors, with temperature and water availability being key determinants. This study aimed to evaluate the effects of climate variability on the yield of Arabica coffee (Coffea arabica L.) cultivars across phenological phases and to validate an agrometeorological model at local and regional scales using yield–climate time-series data from the Volcanic Region of Poços de Caldas. The Arabica coffee cultivarsMundoNovo 376/4, Catuaí IAC 144, and BourbonVermelho were evaluated across four phenological phases. Pearson’s correlation coefficients were applied for each phenological phase and production year between yield and climatic variables, along with the Mann–Kendall test to detect monotonic trends in climatic data. The agrometeorological model was evaluated at different analytical scales and under distinct cultivars and yield scenarios, using historical data from 2011 to 2024. The grain-filling phase was the most sensitive to increasing temperature, while flowering showed a risingwarming trend, increasing its vulnerability. Cultivars differed in their thermal and water tolerance, with Bourbon Vermelho showing high vegetative resilience but marked reproductive susceptibility. The model performed well at the regional scale (R2 = 0.93; RMSE = 453 kg ha−1), particularly within the Volcanic Region, but exhibited limited accuracy at the plot scale due to local variability. Estimated yield losses were mainly associated with water deficit, followed by frost events, while thermal penalties were minimal. These results highlight the need to improve climate-resilience strategies and mitigate the impacts of climate change on coffee cultivation.