Linking soil adsorption-desorption characteristics with grain zinc concentrations and uptake by teff, wheat and maize in different landscape positions in Ethiopia

AimZinc deficiencies are widespread in many soils, limiting crop growth and contributing to Zn deficiencies in human diets. This study aimed at understanding soil factors influencing grain Zn concentrations and uptake of crops grown in different landscape positions in West Amhara, Ethiopia.MethodsOn-farm experiments were conducted in three landscape positions, with five farmers’ fields as replicates in each landscape position, and at three sites. Available Zn from the soil (Mehlich 3, M3, Zn) and applied fertilizer (NET_FERT Zn, estimated based on adsorption/desorption characteristics and applied Zn) were related to the actual grain Zn concentration and uptake of teff, wheat, and maize. Zinc fertilizer treatments tested were Zn applied at planting (basal), basal plus side dressing and a control with no Zn applied.ResultsZn treatments had a significant effect on grain Zn concentration (increase by up to 10%) but the effect on grain yield was variable. Differences in crop Zn concentrations along the landscape positions were observed but not at all sites and crops. Trial results showed that soils with higher soil pH and Soil Organic Carbon (SOC) (typical of footslope landscape positions) tended to adsorb more applied Zn (reduce NET_FERT Zn) than soils with lower soil pH and SOC (typical of upslope landscape positions). Zn availability indicators (M3, NET_FERT Zn, clay%) explained 14-52% of the observed variation in grain Zn concentrations, whereas macronutrient indicators (Total N, exchangeable K) together with M3 Zn were better in predicting grain Zn uptake (16 to 32% explained variability). Maize had the lowest grain Zn concentrations but the highest grain Zn uptake due to high yields.ConclusionWe found that the sum of indigenous and fertilizer Zn significantly affects grain Zn loadings of cereals and that the associated soil parameters differ between and within landscape positions. Therefore, knowledge of soil properties and crop characteristics helps to understand where agronomic biofortification can be effective

An interlaboratory comparison of mid-infrared spectra acquisition: Instruments and procedures matter

Diffuse reflectance spectroscopy has been extensively employed to deliver timely and cost-effective predictions of a number of soil properties. However, although several soil spectral laboratories have been established worldwide, the distinct characteristics of instruments and operations still hamper further integration and interoperability across mid-infrared (MIR) soil spectral libraries. In this study, we conducted a large-scale ring trial experiment to understand the lab-to-lab variability of multiple MIR instruments. By developing a systematic evaluation of different mathematical treatments with modeling algorithms, including regular preprocessing and spectral standardization, we quantified and evaluated instruments' dissimilarity and how this impacts internal and shared model performance. We found that all instruments delivered good predictions when calibrated internally using the same instruments' characteristics and standard operating procedures by solely relying on regular spectral preprocessing that accounts for light scattering and multiplicative/additive effects, e.g., using standard normal variate (SNV). When performing model transfer from a large public library (the USDA NSSC-KSSL MIR library) to secondary instruments, good performance was also achieved by regular preprocessing (e.g., SNV) if both instruments shared the same manufacturer. However, significant differences between the KSSL MIR library and contrasting ring trial instruments responses were evident and confirmed by a semi-unsupervised spectral clustering. For heavily contrasting setups, spectral standardization was necessary before transferring prediction models. Non-linear model types like Cubist and memory-based learning delivered more precise estimates because they seemed to be less sensitive to spectral variations than global partial least square regression. In summary, the results from this study can assist new laboratories in building spectroscopy capacity utilizing existing MIR spectral libraries and support the recent global efforts to make soil spectroscopy universally accessible with centralized or shared operating procedures