Spatial-temporal variability of leaf chlorophyll and its relationship with cocoa yield

ABSTRACT The objective of this study was to evaluate the spatial-temporal variability of leaf chlorophyll index and its relationship with cocoa yield. The experiment was carried out in an experimental area of cocoa production located in Ilhéus, Bahia State, Brazil. Leaf chlorophyll content was measured in September, October, January, February, March and April in the 2014/2015 season, at each sampling point of a regular grid by using a portable chlorophyll meter. Under the same conditions, yield was evaluated and the data were submitted to descriptive statistics and a linear correlation study. Geostatistical analysis was used to determine and quantify the spatial and temporal variability of leaf chlorophyll index and yield. Leaf chlorophyll index varied over the period evaluated, but the months of February, March and April showed no spatial dependence in the study area, indicating absence of temporal stability. Cocoa monthly yield, except in January, presented high spatial variability. Under the conditions of this study, it was not possible to establish a relationship between leaf chlorophyll index and cocoa yield

Spatial-temporal variability of leaf chlorophyll and its relationship with cocoa yield

<div><p>ABSTRACT The objective of this study was to evaluate the spatial-temporal variability of leaf chlorophyll index and its relationship with cocoa yield. The experiment was carried out in an experimental area of cocoa production located in Ilhéus, Bahia State, Brazil. Leaf chlorophyll content was measured in September, October, January, February, March and April in the 2014/2015 season, at each sampling point of a regular grid by using a portable chlorophyll meter. Under the same conditions, yield was evaluated and the data were submitted to descriptive statistics and a linear correlation study. Geostatistical analysis was used to determine and quantify the spatial and temporal variability of leaf chlorophyll index and yield. Leaf chlorophyll index varied over the period evaluated, but the months of February, March and April showed no spatial dependence in the study area, indicating absence of temporal stability. Cocoa monthly yield, except in January, presented high spatial variability. Under the conditions of this study, it was not possible to establish a relationship between leaf chlorophyll index and cocoa yield.</p></div

Nickel and copper accumulate at low concentrations in cacao beans cotyledons and do not affect the health of chocolate consumers

Aim of study: Nickel (Ni) and Copper (Cu) are essential metals for the growth and development of plants. In view of the above, the aim of this work was to quantify and correlate Ni and Cu concentrations in the leaf and the parts of the fruit [pod husk, pulp, tegument (seed coating) and cotyledons] of clonal cacao genotype PH 16.Area of study: Cacao genotypes were collected from adult plants grown on farms located in three different climatic regions of southern Bahia, Brazil.Material and methods: Plant material was collected in four plots of twenty farms, located under different edaphic and topographic conditions. They were subjected to chemical analysis and later to statistical analyses.Main results: There was high variability of Ni and Cu concentrations in all evaluated plant materials. Leaf, pulp, and tegument were the plant materials that accumulated more Ni. On the other hand, the greatest accumulation of Cu occurred in the tegument and in the pod husk, while in the cotyledons there was little accumulation of these metals. The concentrations of Ni were influenced by the three climatic regions, a fact not observed for Cu, except at the leaf level. There was interdependence between the accumulation of Ni in the leaves and in the different parts of the fruit, a fact not observed for Cu.Research highlights: Since Ni and Cu accumulated in low concentrations in the cacao beans cotyledons, raw material for the manufacture of chocolate and other food products, these metallic elements do not affect the consumers' health

Artificial neural networks in the prediction of soil chemical attributes using apparent electrical conductivity

Aim of study: To use artificial neural networks (ANN) to predict the values and spatial distribution of soil chemical attributes from apparent soil electrical conductivity (ECa) and soil clay contents.Area of study: The study was carried out in an area of 1.2-ha cultivated with cocoa, located in the state of Bahia, Brazil.Material and methods: Data collections were performed on a sampling grid containing 120 points. Soil samples were collected to determine the attributes: clay, silt, sand, P, K+, Ca2+, Mg2+, S, pH, H+Al, SB, CTC, V, OM and P-rem. ECa was measured using the electrical resistivity method in three different periods related to soil sampling: 60 days before (60ECa), 30 days before (30ECa) and when collecting soil samples (0ECa). For the prediction of chemical and physical-chemical attributes of the soil, models based on ANN were used. As input variables, the ECa and the clay contents were used. The quality of ANN predictions was determined using different statistical indicators. Thematic maps were constructed for the attributes determined in the laboratory and those predicted by the ANNs and the values were grouped using the fuzzy k-means algorithm. The agreement between classes was performed using the kappa coefficient.Main results: Only P and K+ attributes correlated with all ANN input variables. ECa and clay contents in the soil proved to be good variables for predicting soil attributes.Research highlights: The best results in the prediction process of the P and K+ attributes were obtained with the combination of ECa and the clay content