Application of artificial neural networks as an alternative to volumetric water balance in drip irrigation management in watermelon crop

Precision irrigation seeks to establish strategies which achieve an efficient ratio between the volume of water used (reduction in input) and the productivity obtained (increase in production). There are several studies in the literature on strategies for achieving this efficiency, such as those dealing with the method of volumetric water balance (VWB). However, it is also of great practical and economic interest to set up versatile implementations of irrigation strategies that: (i) maintain the performance obtained with other implementations, (ii) rely on few computational resources, (iii) adapt well to field conditions, and (iv) allow easy modification of the irrigation strategy. In this study, such characteristics are achieved when using an Artificial Neural Network (ANN) to determine the period of irrigation for a watermelon crop in the Irrigation Perimeter of the Lower Acaraú, in the state of Ceará, Brazil. The Volumetric Water Balance was taken as the standard for comparing the management carried out with the proposed implementation of ANN. The statistical analysis demonstrates the effectiveness of the proposed management, which is able to replace VWB as a strategy in automation.

Temporal variations in the primary productivity of Eleocharis acutangula (Cyperaceae) in a tropical wetland environment

Wetland vegetation typically includes aquatic macrophytes with high primary production capacities. The present study investigated how hydrological variations affect biomass allocation and primary productivity in the emergent macrophyte Eleocharis acutangula (Roxb.) Schult. Eleocharis acutangula ramets were collected from the Campelo Lagoon flood plain (21°39'S, 41°12'W and 21°37S, 41°11'W) between March/2005 and February/2006. This region experienced an unusually short rainy period between November/2005 and February/2006 that generated atypically high primary production levels (128gDWm-2month-1) and total biomass gains (447gDWm-2) in May and June/2005 respectively. Our data indicated that primary production and biomass allocation were strongly influenced by variations in wetland water levels and that macrophytes quickly invested in biomass accumulation when surface water levels rised.