https://repositorio.ufra.edu.br/jspui/handle/123456789/276 Doutorado em Agronomia 2026-09-28T22:16:55Z https://repositorio.ufra.edu.br/jspui/handle/riufra/2905 Título: AI-driven digital soil Mapping of the Eastern Amazon: integrating pedological knowledge and pedometrics Autor(es): SOBRINHO, Rômulo José Alencar Abstract: The lack of more detailed information on soil distribution in the Amazon is an impediment to effective soil governance in this region. Therefore, the overall objective of this study was to evaluate the performance and uncertainty of assembly algorithm approaches, legacy mapping, environmental covariates, and reference area. To this end, this thesis was divided into two chapters, titled: I) Integrating Tacit Knowledge and AI for Digital Soil Mapping in Eastern Amazonia: Ensemble Learning, Model Performance, and Uncertainty Incorporation; and II) Transferring soil–landscape relationships in data-limited regions of the Amazon: a knowledgeguided digital soil mapping approach based on reference areas. Output data from both chapters followed the common steps below. Fifteen environmental covariates based on the SCORPAN model were used. To generate the predictive models, we used the algorithms and four input datasets: Random Forest, Ranger, Xgboost, C 5.0, and Ensemble Learning. We also evaluated two levels of detail for the Mapping Units of the legacy map: taxonomic order level and large group level. Terrain covariates were derived from two distinct elevation models: digital surface model and digital terrain model. A total of 20 predictive models were analyzed. Key findings from Chapter 2: the algorithm assembly approach using a digital surface model and unit mapping at the Large Group taxonomic level achieved statistically superior performance in terms of accuracy and Kappa compared to individual models at the Large Group taxonomic level. Environmental covariates such as: 22,000-year-old Mean Annual Temperature, Channel Network Base Level, Altitude, and Land Surface Temperature showed greater importance in predictive modeling; And as highlights of Chapter 3: in the external validation in the extrapolation area, the ensemble learning predictive models using data derived from the digital surface model, in mapping units at the Oreder taxonomic level, ensemble learning using data derived from the digital terrain model, in mapping units at the Order taxonomic level and ensemble learning using data derived from the digital surface model, in mapping units at the Great group taxonomic level, and the prediction of the mapping units GLEISSOLOS and GLEISSOLOS SÁLICOS Sódicos stood out as the most accurate in the extrapolation area. The maps generated from the legacy map and for the extrapolation area, using reference area, successfully mapped mapping units related to the hydromorphic environment. The pioneering nature of this study in the Amazon revealed the potential of the ensemble learning approach for mapping soil classes under hydromorphic environments, environments that occupy a large area in the Amazon and are of significant importance.; The lack of more detailed information on soil distribution in the Amazon is an impediment to effective soil governance in this region. Therefore, the overall objective of this study was to evaluate the performance and uncertainty of assembly algorithm approaches, legacy mapping, environmental covariates, and reference area. To this end, this thesis was divided into two chapters, titled: I) Integrating Tacit Knowledge and AI for Digital Soil Mapping in Eastern Amazonia: Ensemble Learning, Model Performance, and Uncertainty Incorporation; and II) Transferring soil–landscape relationships in data-limited regions of the Amazon: a knowledgeguided digital soil mapping approach based on reference areas. Output data from both chapters followed the common steps below. Fifteen environmental covariates based on the SCORPAN model were used. To generate the predictive models, we used the algorithms and four input datasets: Random Forest, Ranger, Xgboost, C 5.0, and Ensemble Learning. We also evaluated two levels of detail for the Mapping Units of the legacy map: taxonomic order level and large group level. Terrain covariates were derived from two distinct elevation models: digital surface model and digital terrain model. A total of 20 predictive models were analyzed. Key findings from Chapter 2: the algorithm assembly approach using a digital surface model and unit mapping at the Large Group taxonomic level achieved statistically superior performance in terms of accuracy and Kappa compared to individual models at the Large Group taxonomic level. Environmental covariates such as: 22,000-year-old Mean Annual Temperature, Channel Network Base Level, Altitude, and Land Surface Temperature showed greater importance in predictive modeling; And as highlights of Chapter 3: in the external validation in the extrapolation area, the ensemble learning predictive models using data derived from the digital surface model, in mapping units at the Oreder taxonomic level, ensemble learning using data derived from the digital terrain model, in mapping units at the Order taxonomic level and ensemble learning using data derived from the digital surface model, in mapping units at the Great group taxonomic level, and the prediction of the mapping units GLEISSOLOS and GLEISSOLOS SÁLICOS Sódicos stood out as the most accurate in the extrapolation area. The maps generated from the legacy map and for the extrapolation area, using reference area, successfully mapped mapping units related to the hydromorphic environment. The pioneering nature of this study in the Amazon revealed the potential of the ensemble learning approach for mapping soil classes under hydromorphic environments, environments that occupy a large area in the Amazon and are of significant importance.; The lack of more detailed information on soil distribution in the Amazon is an impediment to effective soil governance in this region. Therefore, the overall objective of this study was to evaluate the performance and uncertainty of assembly algorithm approaches, legacy mapping, environmental covariates, and reference area. To this end, this thesis was divided into two chapters, titled: I) Integrating Tacit Knowledge and AI for Digital Soil Mapping in Eastern Amazonia: Ensemble Learning, Model Performance, and Uncertainty Incorporation; and II) Transferring soil–landscape relationships in data-limited regions of the Amazon: a knowledgeguided digital soil mapping approach based on reference areas. Output data from both chapters followed the common steps below. Fifteen environmental covariates based on the SCORPAN model were used. To generate the predictive models, we used the algorithms and four input datasets: Random Forest, Ranger, Xgboost, C 5.0, and Ensemble Learning. We also evaluated two levels of detail for the Mapping Units of the legacy map: taxonomic order level and large group level. Terrain covariates were derived from two distinct elevation models: digital surface model and digital terrain model. A total of 20 predictive models were analyzed. Key findings from Chapter 2: the algorithm assembly approach using a digital surface model and unit mapping at the Large Group taxonomic level achieved statistically superior performance in terms of accuracy and Kappa compared to individual models at the Large Group taxonomic level. Environmental covariates such as: 22,000-year-old Mean Annual Temperature, Channel Network Base Level, Altitude, and Land Surface Temperature showed greater importance in predictive modeling; And as highlights of Chapter 3: in the external validation in the extrapolation area, the ensemble learning predictive models using data derived from the digital surface model, in mapping units at the Oreder taxonomic level, ensemble learning using data derived from the digital terrain model, in mapping units at the Order taxonomic level and ensemble learning using data derived from the digital surface model, in mapping units at the Great group taxonomic level, and the prediction of the mapping units GLEISSOLOS and GLEISSOLOS SÁLICOS Sódicos stood out as the most accurate in the extrapolation area. The maps generated from the legacy map and for the extrapolation area, using reference area, successfully mapped mapping units related to the hydromorphic environment. The pioneering nature of this study in the Amazon revealed the potential of the ensemble learning approach for mapping soil classes under hydromorphic environments, environments that occupy a large area in the Amazon and are of significant importance. 2026-06-30T00:00:00Z https://repositorio.ufra.edu.br/jspui/handle/riufra/2901 Título: Sistema integrado de diagnose e recomendação (DRIS) na avaliação do estado nutricional da cultura do coqueiro hibrido Autor(es): SALDANHA, Eduardo Cézar Medeiros Abstract: Among the methods used for the interpretation of the results of foliar analysis, there is the integrated system of diagnosis and recommendation (DRIS). This method is based on the caleulation of an index for each nutrient. For the calceulation of DRIS norms, there is a need to organize a database of foliar orchards with known productivity, and establish standards for the relationships between nutrients. In the Amazonian conditions, specifically in the state of Pará, no DRIS norms developed for hybrid coconut culture. Hus the aim of this work was to develop DRIS norms for different selection criteria of the relationships between nutrients for growing the hybrid coconut in Pará. The study was conducted in the municipality of Farm SOCOCO Moju PA. Was used to form the database results of leaf analysis and productivity of 134 observations for the period 2001-2011. Obtained the mean, standard deviation, coefficient of variation and variance of the relationships of the nutrients, the leaf samples of vintages from 2001 to 2011, the coefficient of correlation between the ratio of each pair of nutrients and fruit yield. The highest values of standard deviation, variance and coefficient of variation were presented for foliar concentrations of the micronutrients iron, manganese and boron. The nutrients showed higher percentages of samples with levels below the appropriate levels used were Mg and Ca, while nutrients showed that samples with foliar concentrations above or equal to the appropriate levels were Fe, Mn and Ca. DRIS norms for growing hybrid greens were established, based on the relationships between nutrients in the population of high productivity. Of the 110 relationships between nutrients studied, 55 relationships were selected to compose the DRIS norms for the cultivation of hybrid coconut, using two selection criteria of the relationships between nutrients.; The assessment of the nutritional status of plants by leaf analysis as the Integrated System Diagnosis and Recommendation (DRIS) has been highlighting the traditional methods of interpreting the results of the analysis of plant tissue. From the DRIS indices expressing the balance of nutrients in a plant, comparing relationships in the sample being diagnosed with rules or default values are calculated. For the coconut, nutritional counseling through foliar analysis is touted as an efficient method for fertilizer recommendation, and the results have been traditionally interpreted using the criteria and the critical level of sufficiency ranges. The objective of this study was to evaluate the nutritional status and establish nutritional standards for the cultivation of hybrid coconut in the municipality of Moju, Pará, using the DRIS. Was used to form the database results of leaf analysis and productivity of 134 observations for the period 2001-2011. To calculate DRIS indices of 134 leaf samples were used DRIS norms established in the first chapter of this work. It was found for the most common deficiencies element K, and Mg possibly in excess element. The order of nutrient limitation, was K> P>Ca> Fe> N> O> B> Zn> Cu> Mn> Mg. The Ca, Fe and K nutrients are more likely to respond positively to fertilization, since the Mg, Cu and Mn nutrients, were diagnosed as having the greatest like lihood of negative response to fertilization. It was also found that N and P are the ones that are in the best position to nutritional balance. Regression equations for the relationship between the nutrient content in leaves of hybrid coconut and its DRIS indices, which allowed establishing nutrient reference values, based on DRIS were adjusted.; The DRIS method has been used infrequently as a tool for evaluating theresults of fertilization experiments in different crops. Watts work has shown that the method has been able to identify nutritional limitations with different culture, thus confirming its robustness and efficiency in nutritional diagnostics. The aim of this study carry the nutritional diagnosis of the culture of hybrid coconut, from the use of the critical level method and the integrated system of diagnosis and recommendation (DRIS) on the results of experiment evaluating different sources of phosphorus. Nutritional diagnostics for macro and micronutrients were drawn from critical levels (CL) and integrated system of diagnosis and recommendation (DRIS), with results obtained from the experiment of phosphorus fertilization on crops in 2004, 2005, 2006 and 2007 data and productivity levels leaf were used from this experiment in the area of commercial coconut production company Socôco - Agribusiness Amazon. There was no significant response to the productivity variables assessed in any of the four years evaluated. Despite the absence of significant effects, it was found that the treatments resulted in different values of productivity. It was found that the nutritional diagnostic prepared from the DRIS method of predicting were consistent fills or nutritional deficiencies for P element, depending on the treatments to different sources and combinations, since with reference to the traditional method of interpreting through the level critical leaf, there was agreement in the nutritional information for P. 2026-05-26T00:00:00Z https://repositorio.ufra.edu.br/jspui/handle/riufra/2815 Título: Mitigação de déficit hirdrico e alelopatia por rizobactérias em plantas de arroz de terras altas. Autor(es): RÊGO, Marcela Cristiane Ferreira Abstract: Due to the high global consumption of rice grains and productivity losses caused by abiotic stresses (water deficit and allelopathy), especially in the early stages of crop development, studies seeking to mitigate damage are necessary. In this sense, the objective was to study the possible beneficial effects on the morphology -anatomy and physiology in plants subjected to stress through the use of growth-promoting rhizobacteria (PGPR) (Pseudomonas fluorescens BRM-32111 and Burkholderia pyrrocinia BRM-32113), which were previously selected and identified in earlier studies, was to identify the potential action in upland rice plants. In studies related to water deficit, it was possible to verify that BRM-32113 and BRM-32111 were found to be tolerant to abiotic stresses of salinity, temperature, and drought, and both isolated and combined promoted drought stress tolerance in rice plants in water layers in the soil of up to 30% of field capacity in biomass, relative chlorophyll content, number of leaves, biomass, and root length. Seeds treated with PEG and inoculated with BRM-32111 showed higher germination, and plants had higher Y'am, while plants with BRM 32113 had higher IVG. Plants inoculated with BRM 32111 and BRM-32113 had greater root diameter, number of protoxylem pores, cortex thickness, reduction in stomatal pores, and increase in stomatal density, and increased A, Ci/Ca, WUE, and A/Ci, greater accumulation of chlorophyll a and chlorophyll b, proline, and reduced MDA. When BRM 32111 and BRM-32113 were tested for the induction of tolerance to allelochemicals, there was an increase in biomass, leaf area, root length and biomass, chlorophyll a and chlorophyll a + b, A, A/Ci, and WUE. 220 221 These effects that the isolates BRM 32111 and BRM 32113 induced in plants under stress conditions 222 show efficiency in stimulating the tolerance of upland rice plants 223 to stress with allelochemicals and water deficit. 2026-02-09T00:00:00Z https://repositorio.ufra.edu.br/jspui/handle/riufra/2814 Título: Demanda hídrica do feijão Caupi no nordeste paraense. Autor(es): FARIAS, Vivian Dielly da Silva Abstract: The knowledge about water needs of crops of great importance for the study of irrigation water management and evapotranspiration are the top main variables of the hydrological cycle. The evapotranspiration of a crop can be measured by lysimeters, or estimated through empirical equations. The use of models related to evapotranspiration allows the establishment of a relationship between the knowledge about the physiological processes that determine yield and irrigation. The main objectives envolve the estimation of the reference evapotranspiration (ETo) for municipalities that produce cowpea, the determination and estimatation of the crop evapotranspiration (ETC) and the crop coefficients (Kc) for the different stages of cowpea. Thereby, a series of data was collected from an automatic meteorological station that belons to the National Institute of Meteorology (INMET). Then, methods of estimation of reference evapotranspiration (ETo) were compared with the Penman-Monteith method FAO 56 for cowpea producers municipalities. The ETC of the cowpea was determined through drainage lysimeters and by the Bowen ratio method. After, it was estimated by the Penman-Monteith indirect method. For the municipalities of Tracuateua, Bragança, Capitão Poço e Castanhal, the Turc method, Blaney-Criddle-FAO24 and the Multiple Regression function presented the best evaluations for all the statistical criteria, requiring no further adjustments; the Priestley-Taylor, Makking and FAO 24 methods presented excellent results after adjustments. Thus, adjusted equations can be used in the study region. The Camargo and Hargreaves-Samani method obtained the worst evaluation for all municipalities and for all criteria comparison with the other methods. The total water consumption of cowpea was, on average, 267.73 mm day¹. On average during the cowpea cycle, the was 0.66, indicating that the cowpea crop does not decouple completely from the atmosphere under the climatic conditions of Castanhal Pará. The Kc of the cowpea presented an average value of 0.8 in the vegetative phase, 1.4 during the reproductive phase, reaching the final stage with an average value of 0.5. The Penman-Monteith model, with a canopy resistance proposed by Ortega-Farias (1993) (rcO), can be used to estimate the evapotranspiration of cowpea cultivated under the edaphoclimatic conditions of Castanhal Pará. On a daily scale, the model presented greater precision and accuracy for LAI conditions ≥ 3. However, the Penman Monteith model achevied a better estimation for the evapotranspiration of cowpea with the canopy resistance (rc1) estimated from the conductance (gf) proposed by Lima et al. (2016, p.547). 2026-02-12T00:00:00Z