To identify the main edaphic variables most correlated to banana productivity in Venezuela and explore the development of an empirical correlation model to predict this productivity based on soil characteristics. Six agricultural fields located in two of the main banana production areas of Venezuela were selected. The experimental sites were in large farms (≥ 50 ha) with four productivity levels in "Gran Nain" bananas, with an area of 4 ha for each of four productive levels: High-High, High-Low, Low-High, and Low-Low. Sixty sampling points were used to characterize the soils under study. Additionally, a Productivity Index (PI) based on three different biometric data on plant productivity was proposed. Through hierarchical statistical analysis, the first 16 soil variables that best explained the PI were selected. Thus, five multiple linear regression models were estimated, using the stepwise regression method. Subsequently, a performance analysis was used to compare the prediction quality range and the error associated with the number of soil variables selected for the proposed models. The selected model included the following soil variables: Mg, penetration resistance, total microbial respiration, bulk density, and omnivorous free-living nematodes. These variables explain the PI with an R 2 of 0.55, the mean absolute error (MAE) of 0.8, and the root of the mean squared error (RMSE) of 1.0. The five selected variables are proposed to characterize the soil Productivity Index in banana and could be used in a site-specific soil management program for the banana areas of Venezuela.
Knowledge and monitoring of the grapevine phenology during the season are important 13 requirements for characterization of productive regions, climate change studies and planning 14 of various production activities at the vine field scale. This work aims at studying the spatial 15 variability of grapevine phenology at the within field scale. It was conducted on two fields, 16 one of cv Cabernet Sauvignon of 1.56 ha and the other of cv Chardonnay of 1.66 ha, both 17 located in Maule Valley, Chile. Within each vine field, a regular sampling grid was designed, 18 to carry out weekly measurements of phenology and maturation. The main results show that 19 there is a significant spatial variability in the phenological development and maturation at 20 the within field scale for both fields. This variability is spatially organised and temporaly 21 stable from the beginning of the season (post-budburst) to harvest and over the years. A 22 cluster analysis allowed us to define two clearly contrasted zones in terms of phenology and 23 maturation in both fields, explained by the microclimate. The magnitude of difference 24 between zones varied from 4 to 9 days depending on phenological stages and from 5 to 43 25 days for maturation. These differences are similar and comparable to that observed at larger 26 scales or under scenarios of climate change. These results highlight the necessity to better 27 take into account this variability to improve sampling and to base decisions of production 28 activities (spraying, harvest, pruning, etc.) application on more relevant information. Further 29 investigations should determine the environmental factors that determine the observed spatial 30 variability.
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