Water availability is a crucial factor in the final productivity of cowpea. The objective of this work was to evaluate the production and productivity components of cowpea under different irrigation depths in Amazonian conditions. The experiment was carried out at the School Farm of the Federal Rural University of Amazonia, in the municipality of Castanhal-PA, using the cultivar BR3 Tracuateua, from September to November 2014, 2015, and 2016. The experimental design was conducted on six blocks and four treatments, where the four irrigation depths of 0, 25, 50, and 100% of crop evapotranspiration were tested. The productivity analysis was performed when 90% of the plants were in the phenological stage R9. The evaluated production components were pod length, number of pods per plant, number of grains per pod, mass of one hundred grains, and harvest index. There was a statistical difference among all treatments for the components of production and among productivities. An average reduction of 827 kg ha⁻¹ in cowpea productivity was observed during the three years of study, when the treatment without irrigation was compared with the treatment irrigated with 100% of the crop’s water demand. It was found in this research that the simple fulfillment of the nutritional and phytosanitary demands of the crop, associated with an adequate planning of when to plant in the region, would already help in the improvement of local production when choosing times where the water deficit in the reproductive phase is less than 33 mm.

The culture of açaí (Euterpe oleraceae M.), originating from floodplain areas, was planted on dry land in many properties in Pará, Brazil, making necessary the use of irrigation. To irrigate adequately with less waste, it is necessary that studies aim at increasing efficiency in the use of water in this sector, and one of the ways to do so is to estimate evapotranspiration (ET). The objective of this study was to estimate the actual daily evapotranspiration using the Surface Energy Balance Algorithm for Land (SEBAL) in eastern Amazonia. Six images from the Landsat 8 satellite were used, and the estimates of evapotranspiration with the SEBAL algorithm showed good agreement with the results obtained by the Bowen ratio method in the area of açaí planting, including the mean absolute error (MAE), mean relative error (MRE), root of mean square error (RMSE), and the concordance index (d index) equal to 0.45 mm day⁻¹, 4.23%, 0.52 mm day⁻¹, and 0.80, respectively. SEBAL showed the ability to distinguish the soil cover, demonstrating the sensitivity of the model, which provided the mapping of the components analyzed. The use of the algorithm helps in decision making regarding irrigation management and reducing costs and water losses.

ESTIMATIVA DO SALDO DE RADIAÇÃO DO AÇAÍ IRRIGADO POR MEIO DE SENSORIAMENTO REMOTO NO LESTE DA AMAZÔNIA EWELYN REGINA ROCHA SILVA ¹; DENILSON BARRETO DA LUZ²; DENIS DE PINHO SOUSA³; LUCAS BELÉM TAVARES⁴; BERNARDO BARBOSA DA SILVA⁵ E PAULO JORGE DE OLIVEIRA PONTE DE SOUZA^{6   1} Mestre em Agronomia pelo programa de Pós-Graduação em Agronomia – PgAgro, Universidade Federal Rural da Amazônia, Av. Presidente Tancredo Neves, 2501, Terra Firme, 66077-830, Belém, Pará, Brasil, ewelynrocha@gmail.com. ² Graduando em Agronomia, Laboratório de Agrometeorologia, Universidade Federal Rural da Amazônia, Av. Presidente Tancredo Neves, 2501, Terra Firme, 66077-830, Belém, Pará, Brasil, denilsong97@gmail.com. ³ Engenheiro Agrônomo, Doutor em Agronomia, Fiscal de Meio Ambiente na Secretaria de Estado de Meio Ambiente e Sustentabilidade do Pará, Rua do Utinga, n° 717, bairro Curió Utinga, CEP: 66610-010, Belém, PA, Brasil, denisdepinho@agronomo.eng.br. ⁴ Mestrando no programa de Pós-Graduação em Ciências Florestais, Universidade Federal Rural da Amazônia, Av. Presidente Tancredo Neves, 2501, Terra Firme, 66077-830, Belém, Pará, Brasil, lucas.belem.tavares@gmail.com. ⁵ Centro de Tecnologia e Recursos Naturais, Universidade Federal de Campina Grande, Rua Aprígio Veloso, 882, Campina Grande, 58429-900, Campina Grande, Paraíba, Brasil, bernardo.silva@ufcg.edu.br. ⁶ Instituto Socioambiental e dos Recursos Hídricos, Laboratório de Agrometeorologia, professor no programa de Pós-Graduação em Agronomia, Universidade Federal Rural da Amazônia, Av. Presidente Tancredo Neves, 2501, Terra Firme, 66077-830, Belém, Pará, Brasil, paulo.jorge@ufra.edu.br.     1 RESUMO   Muitas áreas na Amazônia foram modificadas, alterando as trocas energéticas neste ambiente. Essa mudança é uma das responsáveis pelas variações no saldo de radiação (R_n), pois afetam a troca de energia entre a superfície e a atmosfera. O objetivo foi estimar o R_n em uma área de açaí irrigado na Amazônia Oriental. Foram utilizadas imagens do satélite Landsat 8, do dia sequencial do ano (DSA) 151/2018 e DSA 241/2019, referente às órbitas/pontos 222/61 e 223/61, respectivamente. O R_n foi obtido por meio do algoritmo Surface Energy Balance Algorithm for Land – SEBAL, que se fundamenta na radiância dos canais reflexivos e termal do sensor. Os resultados (sensor x superfície) mostraram-se satisfatórios com valores de erro absoluto médio (EAM) iguais a 4,92 W/m² e 15,66 W/m², erro relativo médio (ERM) iguais a 0,98 % e 3,4 % para DSA 151 e o DSA 241, respectivamente. Observou-se a capacidade do SEBAL em diferenciar tipos de coberturas do solo, o que proporcionou elaborar a distribuição espacial do Rn na cena analisada e no plantio de açaí, demostrando assim, a sensibilidade do modelo e a importância da variabilidade espacial existente na área, essas informações podem auxiliar as tomadas de decisões quanto ao manejo de irrigação. Keywords: sebal, variabilidade espacial, landsat 8.     SILVA, E. R. R.; LUZ, D. B.; SOUSA; D. P.; TAVARES, L.B. SILVA, B. B.; SOUZA, P. J. O. P. ESTIMATION OF THE RADIATION BALANCE OF IRRIGATED AÇAÍ THROUGH REMOTE SENSING IN EAST AMAZONIA   2 ABSTRACT   Many areas in the Amazon have been modified, altering the energy exchanges in this environment. This change is a factor responsible for variations in the radiation balance (R_n), as they affect the energy exchange between the surface and the atmosphere. The objective was to estimate the R_n in an açaí irrigated area in Eastern Amazon. Landsat 8 satellite images of the sequential day of the year (DSA) 151/2018 and DSA 241/2019, referring to orbits/points: 222/61 and 223/61, respectively, were used. The R_n was obtained through the Surface Energy Balance Algorithm for Land - SEBAL algorithm, which is based on the radiance of the reflective and thermal bands of the sensor. The results (sensor x surface) were satisfactory with mean absolute error (EAM) values ​​equal to 4.92 W/m² and 15.66 W/m², mean relative error (ERM) equal to 0.98% and 3.4% for DSA 151 and DSA 241, respectively. SEBAL's ability to differentiate types of land cover was observed, which provided the elaboration of the spatial distribution of Rn in the analyzed scene and in the açaí planting, so demonstrating the sensitivity of the model and the importance of spatial variability in the area, this information can help in decision-making regarding irrigation management.   Keywords: sebal, spatial variability, landsat 8.