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Cassava (Manihot esculenta Crantz) foliage is a byproduct of cassava production characterized by high biomass and nutrient content. In this study, we investigated the effects of cassava foliage on antioxidant capacity, growth performance, and immunity status in goats, as well as rumen fermentation and microbial metabolism. Twenty-five Hainan black goats were randomly divided into five groups (n = 5 per group) and accepted five treatments: 0% (T1), 25% (T2), 50% (T3), 75% (T4), and 100% (T5) of the cassava foliage silage replaced king grass, respectively. The feeding experiment lasted for 70 d (including 10 d adaptation period and 60 d treatment period). Feeding a diet containing 50% cassava foliage resulted in beneficial effects for goat growth and health, as reflected by the higher average daily feed intake (ADFI), average daily gain (ADG) and better feed conversion rate (FCR), as well as by the reduced serum levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), creatinine (CRE), and triglycerides (TG). Meanwhile, cassava foliage improved antioxidant activity by increasing the level of glutathion peroxidase (GSH-Px), superoxide dismutase (SOD), and total antioxidant capacity (T-AOC) and lowering malondialdehyde (MDA). Moreover, feeding cassava foliage was also beneficial to immunity status by enhancing complement 3 (C3), complement 4 (C4), immunoglobulin A (IgA), immunoglobulin G (IgG), and immunoglobulin M (IgM). Furthermore, the addition of dietary cassava foliage also altered rumen fermentation, rumen bacterial community composition, and metabolism. The abundance of Butyrivibrio_2 and Prevotella_1 was elevated, as were the concentrations of beneficial metabolites such as butyric acid; there was a concomitant decline in metabolites that hindered nutrient metabolism and harmed host health. In summary, goats fed a diet containing 50% cassava foliage silage demonstrated a greater abundance of Butyrivibrio_2, which enhanced the production of butyric acid; these changes led to greater antioxidant capacity, growth performance, and immunity in the goats. [ABSTRACT FROM AUTHOR]

We evaluated the nutritive value of 10 varieties of stylo ( Stylosanthes guianensis) in Hainan province, China, based on chemical composition and in vitro dry matter digestibility. Chemical composition of the varieties averaged 10.5% crude protein, 2.6% ether extract, 47.4% neutral detergent fiber, 39.7% acid detergent fiber and 0.59% total tannins. In vitro dry matter digestibility averaged 61.4%. The Relative Feed Value quality standard of individual varieties was grade 2 or higher, with the highest values for 'TPRC90028' and 'Reyan No. 2.' The results show that the stylo varieties have suitable nutritive values for livestock in tropical regions of China. [ABSTRACT FROM AUTHOR]

To effectively utilize the tropical cassava (Manihot esculenta Crantz) foliage (CF) resources, the CF silages were prepared with microbial additives, including Chikuso-1 (CH1, Lactobacillus plantarum), Snow Lact L (SN, L. rhamnosus), Acremonium cellulase (CE), SN + CE and CH1 + CE. Silage fermentation, chemical composition and ruminal degradation were studied in Hainan, China. CF silages prepared with lactic acid bacteria (LAB) and CE were well preserved, with a higher (P < 0.05) lactic acid, a lower (P < 0.05) pH value, butyric acid content and NH3-N ⁄ total-N compared with the controls. The additive-treated silages showed increased crude protein (CP) content, but decreased (P < 0.05) NDF and ADF contents. Meanwhile, the additive treatment improved relative feed value and ruminal degradability of dry matter (DM), CP, neutral detergent fiber and acid detergent fiber. In addition, the combination of LAB and CE resulted in better fermentation quality and ruminal degradability compared with LAB or CE single treatment. The results demonstrated that the CF could be prepared as ruminant feed, and the combination of LAB and CE might exert beneficial synergistic effect on silage fermentation. [ABSTRACT FROM AUTHOR]

• A locally modelling approach was proposed to build spectral model for estimating leaf nutrients at regional scale. • This approach considered the varied relations between leaf nutrients and leaf spectra over space. • This approach was applied to a case study in Hainan Island to estimate leaf phosphorus concentration of rubber trees. • The proposed approach outperformed the commonly used globally modelling method. The globally modelling method was commonly used to build spectral models for estimating leaf nutrients of plants. However, predictive ability of such models was rather limited at regional scale because of the unreasonable assumption inherent in the globally modelling method which assumed that relationships between leaf nutrients and leaf spectra were stable over space. In fact, the leaf nutrients-spectra relationships generally varied across extensive area due to influences of various environmental factors. In order to accommodate the varied leaf nutrients-spectra relationships at regional scale, the locally modelling approach was proposed to develop spectral models for predicting leaf nutrients. The locally modelling approach assumed that the leaf nutrients-spectra relationships were stable at local spaces within which environmental conditions were relatively uniform. According to this basic idea, the proposed approach used environmental factor as grouping variable to classify leaf samples into several homogenous sub-datasets. Within these sub-datasets, the leaf nutrients-spectra relationships would be more stable. Then, linear or non-linear method were employed to fit model for each sub-dataset. In order to verify the effectiveness of this method, the locally modelling approach was applied to a case study to estimate leaf phosphorus concentration (LPC) of rubber trees (Hevea brasiliensis) in Hainan Island, China. In this case study, three scenarios were set up. In the first scenario, the local model was built using the partial least squares regression (PLSR) with full wavelengths as input variables. In the second scenario, the local model was also developed using the PLSR but with manually selected wavelengths as input variables. In the third scenario, the local model was constructed using the artificial neural networks with feature bands as input variables. Performances of these local models were compared to that of the corresponding global models. Results showed that for the three scenarios local models always yielded higher prediction accuracies than that of the global models in the test dataset. For the first scenario, values of coefficient of determination (r2) and root mean square error (RMSE) ranged from 0.652 to 0.735 and from 0.030% to 0.034%, respectively, for the local model, whereas those varied from 0.458 to 0.637 and from 0.037% to 0.041%, respectively, for the global model. For the second scenario, values of r2 and RMSE ranging from 0.565 to 0.734 and from 0.031% to 0.046%, respectively, for the local model, while those varied from 0.275 to 0.559 and from 0.038% to 0.053%, respectively, for the global model. For the third scenario, values of r2 and RMSE ranging from 0.678 to 0.863 and from 0.021% to 0.033%, respectively, for the local model, while those varied from 0.441 to 0.726 and from 0.030% to 0.043%, respectively, for the global model. This result demonstrates that the locally modelling approach was more effective in estimating LPC of rubber trees than the globally modelling method at the regional scale. In the future, more environmental factors should be considered and their combined effects on leaf sample classification need to be investigated. [ABSTRACT FROM AUTHOR]