Pig Compost Use on Zinc and Copper Concentrations in Soils and Corn Plants

The use of pig compost (PC) in agricultural land has increased in Chile in the last years. This organic amendment is a valuable nutritional source for crops, but its applying must be done in a controlled manner since it exhibited high copper (Cu) and zinc (Zn) concentrations. A short-term field experiment was conducted out to study the effects of increasing PC rates on the production and quality corn crop in two soils located at south central Chile. Five treatments were evaluated: control without fertilization (C), conventional fertilization (CF) (350 kg N ha-1), and three increasing PC rates (15.33, 30.65, and 61.31 Mg&middotha-1, corresponding to 350, 700, and 1400 kg N ha-1, respectively) in a split plot design with four replicates. The overall results indicated that dry matter production, grain yield, and plant Zn and Cu concentrations were similar among fertilization sources and rates. Extractable soil Zn concentration exhibited a rate-related increase of PC in both locations, while Cu concentration exhibited this behavior only at the soil located in Chillan. Nevertheless, the values obtained were below of those considered phytotoxic levels. Therefore, the contribution of Zn and Cu through PC applying at different rates to the soils studied showed a slight affect in soil extractable Zn and Cu values without negatively effects on quantity and quality corn crop. The organic amendment applied can be a good and cheaper substitute to conventional fertilization, although further monitoring of Zn and Cu soil levels should be carried out to avoid any environmental risk.

Effect of the Type of Pasture on the Meat Characteristics of Chilote Lambs

Chilote sheep are a native breed from Chiloé Archipelago in the southern Chile. They are descendants from sheep originally introduced by the Spaniards in the 1600s, and then evolved in a harsh environment in relative isolation from the continent. There is little information about the quality of the meat of the Chilote lambs (Ch). The objective of this study was to compare the quality of Ch lamb meat with two types of marginal pastures. The two treatments were: 1) Ch lambs, naturalized grassland (n = 13) and 2) Ch lambs, rangeland (n = 11). Rangeland is composed of both grasses and native shrubs. All lambs were kept with their mothers until slaughter at 90 days of age. Instrumental color, shear force, pH levels, and chemical and fatty acid content were analyzed. The pasture type did affect the results, given that Ch lambs fed on naturalized pasture had a lower shear force and higher intramuscular fat levels whereas Ch lambs fed on rangeland pasture showed higher percentages of n-3, n-6 fatty acids and Polyunsaturated fatty acids. However, the concentrations of fatty acids were similar in both groups. The results indicated some evidences that the type of pasture of Chiloe archipelago confers specific differences of quality that could form the basis to generate a premium product.

Deep learning for broadleaf weed seedlings classification incorporating data variability and model flexibility across two contrasting environments

The increasing deployment of deep learning models for distinguishing weeds and crops has witnessed notable strides in agricultural scenarios.However,a conspicuous gap endures in the literature concerning the training and testing of models across disparate environmental conditions.Predominant methodologies either delineate a single dataset distribution into training,validation,and testing subsets or merge datasets from diverse condi-tions or distributions before their division into the subsets.Our study aims to ameliorate this gap by extending to several broadleaf weed categories across varied distributions,evaluating the impact of training convolutional neural networks on datasets specific to particular conditions or distributions,and assessing their performance in entirely distinct settings through three experiments.By evaluating diverse network architectures and training approaches(finetuning versus feature extraction),testing various architectures,employing different training strategies,and amalgamating data,we devised straightforward guidelines to ensure the model's deployability in contrasting environments with sustained precision and accuracy.In Experiment 1,conducted in a uniform environment,accuracy ranged from 80%to 100%across all models and training strategies,with finetune mode achieving a superior performance of 94%to 99.9%compared to the feature extraction mode at 80%to 92.96%.Experiment 2 underscored a significant performance decline,with accuracy fig-ures between 25%and 60%,primarily at 40%,when the origin of the test data deviated from the train and valida-tion sets.Experiment 3,spotlighting dataset and distribution amalgamation,yielded promising accuracy metrics,notably a peak of 99.6%for ResNet in finetuning mode to a low of 69.9%for InceptionV3 in feature extraction mode.These pivotal findings emphasize that merging data from diverse distributions,coupled with finetuned training on advanced architectures like ResNet and MobileNet,markedly enhances performance,contrasting with the rel-atively lower performance exhibited by simpler networks like AlexNet.Our results suggest that embracing data diversity and flexible training methodologies are crucial for optimizing weed classification models when dispa-rate data distributions are available.This study gives a practical alternative for treating diverse datasets with real-world agricultural variances.

Catch per unit effort-environmental variables relations in the fishery of white shrimp(Litopenaeus schmitti)from the Gulf of Venezuela

During 15 months the white shrimp (Litopenaeus schmitti) fishing zone was characterized ecologically through the obtaining, compilation and analysis of environmental and physicochemical variables, and modeled the distribution of the relative abundance (CPUE) obtained in 21 fishing sites according to the environmental structure defined by the studied variables. A two-way factorial ANOVA with interaction was used to examine the spatial (fishing sites) and temporal (months) dynamics of CPUE, and a principal component analysis (PCA) was used to discern the environmental structure of the study area. To determine if the environmental structure modeled the distribution of white shrimp, the CPUE values were superimposed on maps of the study area showing strata of the most important physicochemical variables identified by PCA. ANOVA confirmed that the CPUE differed significantly among months (F = 15.6;GL = 11;P P = 0.1979);the interaction term was also not significant (F = 0.52;GL = 10;P = 0.8561). The superimposing of the CPUE on temperature and depth strata confirmed that white shrimp showed greater preference for intermediate temperatures and depths. Petróleos de Venezuela, S.A (PDVSA) contemplates the construction of a multiuse pipeline traversing the study area, by which the current environmental structure of the study area is prone to disturbance. Given the precedent effects represented by an aqueduct construction, it seems PDVSA should anticipate measures to minimize its impact on the fisheries of the zone, particularly on the white shrimp fishery.