Effect of Nitrogenous Fertilizer Treatment on Mineral Metabolism in Grazing Yaks

To assess the impact of N fertilization on contents of mineral elements in herbage and the effect of increased forage S on the copper metabolism of grazing yaks, study was conducted during the summer grazing season （2005, 2006, and 2007）. Pasture replicates （20 ha; n=3 per treatment） received the same fertilizer treatment in each growing season, consisting of i） 90 kg N ha^-1 from quickly available nitrogen, ii） 90 kg N ha^-1 from ammonium nitrate, iii） 90 kg N ha^-1 from ammonium sulfate, and iv） control （no fertilizer）. Forage sampling was collected at 60 days intervals following fertilization （10 samples per pasture） for Cu, Mo, Mn, Se, Fe, Zn, Ca, and P. To determine the effect of fertilizer treatment on mineral metabolism in grazing yaks, liver and blood samples were collected at the start and end of the study period in 2005, 2006, and 2007. Ammonium sulfate fertilization increased （P 〈 0.01） forage S concentration. Plant tissue N concentrations were increased by N fertilization, regardless of source in 2005, 2006, and 2007. Yaks grazing S fertilization pastures had lower （P〈0.05） liver and blood Cu concentrations at the end of the study period in 2005, 2006, and 2007, compared with urea, ammonium nitrate, and control. Nominal increases in forage in vitro organic matter digestibility were realized by fertilization, regardless of N source in each year.

An investigation on the best-fit models for sugarcane biomass estimation by linear mixedeffect modelling on unmanned aerial vehicle-based multispectral images:A case study of Australia

Due to the worldwide population growth and the increasing needs for sugar-based products,accurate estimation of sugarcane biomass is critical to the precise monitoring of sugarcane growth.This research aims to find the imperative predictors correspond to the random and fixed effects to improve the accuracy of wet and dry sugarcane biomass estimations by integrating ground data and multi-temporal images from Unmanned Aerial Vehicles(UAVs).The multispectral images and biomass measurements were obtained at different sugarcane growth stages from 12 plots with three nitrogen fertilizer treatments.Individual spectral bands and different combinations of the plots,growth stages,and nitrogen fertilizer treatments were investigated to address the issue of selecting the correct fixed and random effects for the modelling.A model selection strategy was applied to obtain the optimum fixed effects and their proportional contribution.The results showed that utilizing Green,Blue,and Near Infrared spectral bands on models rather than all bands improved model performance for wet and dry biomass estimates.Additionally,the combination of plots and growth stages outperformed all the candidates of random effects.The proposed model outperformed the Multiple Linear Regression(MLR),Generalized Linear Model(GLM),and Generalized Additive Model(GAM)for wet and dry sugarcane biomass,with coefficients of determination(R2)of 0.93 and 0.97,and Root Mean Square Error(RMSE)of 12.78 and 2.57 t/ha,respectively.This study indicates that the proposed model can accurately estimate sugarcane biomasses without relying on nitrogen fertilizers or the saturation/senescence problem of Vegetation Indices(VIs)in mature growth stages.