Cassava common mosaic virus (CsCMV) is a potexvirus that causes systemic infections in cassava plants, leading to chlorotic mosaic and producing significant yield losses. To date, the physiological alterations and the mechanism underlying biotic stress during the cassava-CsCMV compatible interaction remains unknown. In this study, we found that CsCMV infection adversely modified chloroplast structure and had functional effects on chloroplasts in source leaves during the course of viral infection. Extrusion of the chloroplast membrane with amoeboid-shaped appearance and disorganized grana stacks were observed in infected mesophyll cells. These alterations were associated with up to 35% reduction of relative chlorophyll content, and a decline of CO 2 fixation (13.5% and 24.2% at 90 and 210 days after planting, respectively). The effects of CsCMV infection on the performance index on absorption basis dropped up to 37%. The analysis of chlorophyll a fluorescence showed a progressive loss of both oxygen evolving complex activity and "connectivity" within the tripartite system (core antenna-LHCII-reaction centre). Here, we report the latter phenomenon for the first time in a viral infection. The oxidative stress process was observed in CsCMV-infected plants (20.8% reduction of antioxidant capacity with respect to noninfected plants). Other effects of the pathogen included reduction of starch and maltose content in source leaves, and a significant increase (24.7%) of the sucrose:starch ratio, which indicates an altered pattern of carbon allocation. Our results suggest that CsCMV induces chloroplast distortion associated with progressive chloroplast function loss and diversion of carbon flux in source leaf tissue, leading to the loss of cassava tuber yield.
On-farm precision experimentation (OFPE) is increasingly conducted using variablerate technology and precision agriculture (PA) equipment to measure the effect of changes in input application rates on yields and profits at specific fields. Classical linear regression models and new Bayesian and machine learning regressions for spatial data can be used to investigate site-specific crop response from georeferenced data. The objective of this work was to compare statistical models that can be used by researchers analyzing OFPE data to estimate crop response and better describe its spatial within-field variability. Three statistical models estimating the responses to N rates, seed rates, and site-specific soil properties from eight OFPEs were compared:(a) linear regression (LR) for spatially correlated errors, (b) Bayesian regression (BR) with random site effects, and (c) random forest regression (RF) with kriged residuals. Models were adjusted to account for spatial variation in yield response, and with and without field characteristic covariates. Modeling spatial correlation and including plot covariates improved yield predictions. Differences among methods proved to be indistinguishable with respect to average explained variance, correlation between predicted and observed values, and mean square prediction errors (PEs).However, BR and RF outperformed LR in site-specific prediction accuracy, with BR predictions having lower predictive uncertainty than RF predictions. The hierarchical Bayesian model for spatial data is a useful tool to process OFPE data, allowing direct derivation of linear coefficients and prediction uncertainty measures related to site-specific yield responses.
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