Summary Grain sorghum [Sorghum bicolor (L.) Moench] is the major cereal crop used as staple crop in the arid and semi-arid regions of Ethiopia. Low sorghum yields are attributed to soil, climate and topographic factors. We investigated sorghum yield response to factorial combination of nitrogen and phosphorous (NP) as well as potassium (K), sulphur (S) and zinc (Zn), and how the position of farmers’ fields belonging to different landscape positions (i.e., upslope, mid-slope, and foot slope) could explain fertilizer response and yield variability. The analysis in this study made use of dataset from two sets of on-farm experiments where trials were set at two farmers’ fields for NPKS and three farmers’ fields for NPZn experiments in each landscape position. The experiments were implemented at two sorghum-growing locations (i.e., Hayk and Sirinka) in parts of the north-eastern Amhara region in Ethiopia. Sorghum yield response to fertilizer application was strongly linked to the spatial variation along landscape positions and varied over locations. Fertilizer response was significantly higher at foot slopes compared to mid-slopes and upslope positions, where fields at foot slopes exhibited relatively homogeneous responses. Application of combined nitrogen (N) and phosphorus (P) fertilizers, landscape position and the interaction of fertilizer application and landscape positions strongly affected sorghum yield. There was a linear and significant increase in sorghum yield with the increase in the NP rates. The combined application of NP with different levels of KS as well as NP with Zn fertilizer rates did not result in significant yield difference. The results indicated that local factors were much more influential when accounting for the heterogeneity in sorghum yield response to fertilizer. This further acknowledges the importance of a landscape-based fertilizer management approach to respond yield potential variability related with the farmers’ fields and landscape environment. Further investigation is needed to develop homogeneous fertilizer response units based on spatial variability of soil and topographic attributes along the landscape.
Agronomic biofortification, encompassing the use of mineral and organic nutrient resources to improve micronutrient concentration in staple crops, is a potential strategy to promote production and access of micronutrient-dense foods at farm-level. However, the heterogeneity of smallholder farming landscapes presents challenges on implementing agronomic biofortification. Here, we test the effects of zinc (Zn) and selenium (Se) containing fertilizer on micronutrient concentration of wheat ( Triticum aestivum L.) and teff ( Eragrostis tef (Zucc.) Trotter) grown under different landscape positions and with different micronutrient fertilizer application methods; in the western Amhara region of Ethiopia. Field experiments were established in three landscape positions at three sites, with five treatments replicated across five farms per landscape position and over two cropping seasons (2018 and 2019). Grain Zn concentration ranged from 26.6-36.4 mg kg -1 in wheat and 28.5-31.2 mg kg -1 in teff. Grain Se concentration ranged from 0.02-0.59 mg kg -1 in wheat while larger concentrations of between 1.01-1.55 mg kg -1 were attained in teff. Larger concentrations of Zn and Se were consistently attained when a foliar fertilizer was applied. Application of ⅓ nitrogen (N) yielded significantly larger grain Se concentration in wheat compared to a recommended N application rate. A moderate landscape effect on grain Zn concentration was observed in wheat but not in teff. In contrast, strong evidence of landscape effect was observed for wheat and teff grain Se concentration. There was no evidence for any interaction of the treatment contrasts with landscape position except in teff where an interaction effect between landscape position and Se application was observed. Our findings indicate an effect of Zn, Se, N, landscape position, and its interaction effect with Se on grain micronutrient concentration. Targeting of micronutrient fertilizer application in ongoing agronomic biofortification interventions is likely to be influenced by landscape position and more so by micronutrient fertilizer application method and N fertilization.
<p>Proximal soil spectroscopy can be useful to estimate relevant soil properties in real time and cheaply for agricultural decision support and soil health monitoring. However, prediction performance of plant available soil phosphorus by the visNIR has been unsatisfactory as it is considered among the least spectrally active soil properties. Hence, we compared prediction performance among plant available soil phosphorus (Olsen P), extractable soil phosphorus (ammonium-oxalate extract of P - AmOxP), total soil phosphorus (Aqua regia extract of P - TP) and phosphorus buffer index (PBI) using visNIR soil spectral sensing instrumentations (Neospectra and Fieldspec-4) using East African agricultural soils. The comparison was made by scanning 360 archived soil samples which were collected from 0-20cm soil depth in Ethiopia, Kenya and Tanzania. The spectra data was pre-treated with SavitskyGolay smoothing + first derivative and a PLSR was used to develop the predictive models from a 75% of the dataset (#270) subsampled by a conditioned Latin Hypercubic sampling (cLHS) method using the spectra space. The model performance was evaluated by an independent set of samples (#90) by calculating the concordance correlation coefficient (CCC), ratio of performance to interquartile range (RPIQ), bias and root mean square error of prediction (RMSEP). &#160;The most important wavelengths for all soil P indicators in the NIR instrument ranged between 2150 -2400 nm whereas it included 500-570 nm for the visNIR instrument. PBI was predicted with higher CCC value of 0.94 and 0.89 for visNIR and NIR, respectively, however it has the least RPIQ (0.4 and 0.3, respectively) values when compared to other soil P prediction by both instruments. TP and AmOxP were predicted with higher accuracy and model consistency when compared to OlsenP and PBI. The visNIR range gave better prediction accuracy and model consistency for all soil P indicators than the NIR range. Hence, our findings indicated that TP and AmOxP could be preferred to predict soil phosphorus status for any agricultural and soil health monitoring using soil spectroscopic techniques.</p> <p><em>Keywords: proximal soil spectroscopy, PLSR model, Savitzky-Golay smoothing filter, first derivative, Neospectra, Fieldspec-4, East Africa.</em></p>
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