Application of Support Vector Machine Regression and Partial Least-Square Regression Models for Predicting Sugarcane Leaf Nutrients Content Using Near Infra-Red Spectroscopy

Near infra-red spectroscopy (NIRS) has been suggested as a rapid, cost-effective, and accurate diagnostic tool for leaf nutrient analysis that could replace more traditional laboratory diagnostics. To ease operational workflows, there would advantage in estimating nutrients using a single method, namely NIRS. This study evaluated the potential of NIRS as a diagnostic method for the measurement of key macro and micronutrients in sugarcane leaf samples. Three hundred and fifty-one sugarcane leaf samples used in quality control reference analysis in Fertiliser Advisory Service (FAS) were used for model calibration. About 35% of the samples were from growers within South Africa, while the remainder were from estates across southern and eastern Africa. Dried and milled leaf material was scanned on a Bruker MPA-NIRS instrument, and spectral pre-processing was performed. Support vector machine regression (SVMR) and partial least squares regression (PLSR) were used for calibrating the estimation models with the test validation(Tval) procedure. The results showed both the PLSR and SVMR model resulted in the best calibration for (K, Ca, Mg)(R2 >87% and the ratio of performance to inter-quartile distance(RPIQ) > 2.1). With a test validation dataset, the SVMR yielded relatively high accuracy(R2 >85% and the RPIQ > 2.5, while PLSR yielded poor accuracy. The SVMR also outperforms Zn(R2 = 0.86; and RPIQ = 2.5) for model calibration. NIR Spectrometry combined with the SVMR technique resulted in a practical option to accurately measure leaf nutrient concentrations and evaluate sugarcane mineral contents accurately. These results indicate that NIRS can replace the current analytical methods used in FAS.</p

Data_Sheet_1_The spatial effects of the household's food insecurity levels in Ethiopia: by ordinal geo-additive model.docx

BackgroundFood insecurity and vulnerability in Ethiopia are historical problems due to natural- and human-made disasters, which affect a wide range of areas at a higher magnitude with adverse effects on the overall health of households. In Ethiopia, the problem is wider with higher magnitude. Moreover, this geographical distribution of this challenge remains unexplored regarding the effects of cultures and shocks, despite previous case studies suggesting the effects of shocks and other factors. Hence, this study aims to assess the geographic distribution of corrected-food insecurity levels (FCSL) across zones and explore the comprehensive effects of diverse factors on each level of a household's food insecurity.MethodThis study analyzes three-term household-based panel data for years 2012, 2014, and 2016 with a total sample size of 11505 covering the all regional states of the country. An extended additive model, with empirical Bayes estimation by modeling both structured spatial effects using Markov random field or tensor product and unstructured effects using Gaussian, was adopted to assess the spatial distribution of FCSL across zones and to further explore the comprehensive effect of geographic, environmental, and socioeconomic factors on the locally adjusted measure.ResultDespite a chronological decline, a substantial portion of Ethiopian households remains food insecure (25%) and vulnerable (27.08%). The Markov random field (MRF) model is the best fit based on GVC, revealing that 90.04% of the total variation is explained by the spatial effects. Most of the northern and south-western areas and south-east and north-west areas are hot spot zones of food insecurity and vulnerability in the country. Moreover, factors such as education, urbanization, having a job, fertilizer usage in cropping, sanitation, and farming livestock and crops have a significant influence on reducing a household's probability of being at higher food insecurity levels (insecurity and vulnerability), whereas shocks occurrence and small land size ownership have worsened it.ConclusionChronically food insecure zones showed a strong cluster in the northern and south-western areas of the country, even though higher levels of household food insecurity in Ethiopia have shown a declining trend over the years. Therefore, in these areas, interventions addressing spatial structure factors, particularly urbanization, education, early marriage control, and job creation, along with controlling conflict and drought effect by food aid and selected coping strategies, and performing integrated farming by conserving land and the environment of zones can help to reduce a household's probability of being at higher food insecurity levels.</p