Is cassava yield affected by inverting tillage, chiseling or additional compaction of no-till sandy-loam soil?

Reichert, José Miguel; Fontanela, Eracilda; Awe, Gabriel Oladele; Fasinmirin, Johnson Toyin

doi:10.36783/18069657rbcs20200134

Cited by 20 publications

(10 citation statements)

References 69 publications

(87 reference statements)

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“…K values were lower than critical levels, explaining reduced soil quality in the Aba Gerima catchment. Thus, enhancing the quality of Ethiopian soils requires increasing soil OM content through the implementation of SLM methods and biomass accumulation [ 8 , 74 ].…”

Section: Resultsmentioning

confidence: 99%

“…Improved sustainable land management (SLM) practices including soil bunding may alter key soil qualities [ 2 , 3 ] and control soil loss [ 4 , 5 ], and thereby soil functions [ 6 , 7 ]. SLM activities influence soil physicochemical properties and minimize soil degradation while improving yields [ 8 , 9 ] and they improve people’s livelihoods [ 10 ]. However, livelihoods are at risk because of soil fertility depletion [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

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Use of soil spectral reflectance to estimate texture and fertility affected by land management practices in Ethiopian tropical highland

et al. 2022

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As classical soil analysis is time-consuming and expensive, there is a growing demand for visible, near-infrared, and short-wave infrared (Vis-NIR-SWIR, wavelength 350–2500 nm) spectroscopy to predict soil properties. The objectives of this study were to investigate the effects of soil bunds on key soil properties and to develop regression models based on the Vis-NIR-SWIR spectral reflectance of soils in Aba Gerima, Ethiopia. Soil samples were collected from the 0–30 cm soil layer in 48 experimental teff (Eragrostis tef) plots and analysed for soil texture, pH, organic carbon (OC), total nitrogen (TN), available phosphorus (av. P), and potassium (av. K). We measured reflectance from air-dried, ground, and sieved soils with a FieldSpec 4 Spectroradiometer. We used regression models to identify and predict soil properties, as assessed by the coefficient of determination (R2), root mean square error (RMSE), bias, and ratio of performance to deviation (RPD). The results showed high variability (CV ≥ 35%) and substantial variation (P < 0.05 to P < 0.001) in soil texture, OC, and av. P in the catchment. Soil reflectance was lower from bunded plots. The pre-processing techniques, including multiplicative scatter correction, median filter, and Gaussian filter for OC, clay, and sand, respectively were used to transform the soil reflectance. Statistical results were: R2 = 0.71, RPD = 8.13 and bias = 0.12 for OC; R2 = 0.93, RPD = 2.21, bias = 0.94 for clay; and R2 = 0.85 with RPD = 7.54 and bias = 0.0.31 for sand with validation dataset. However, care is essential before applying the models to other regions. In conclusion, the findings of this study suggest spectroradiometry can supplement classical soil analysis. However, more research is needed to increase the prediction performance of Vis-NIR-SWIR reflectance spectroscopy to advance soil management interventions.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Use of soil spectral reflectance to estimate texture and fertility affected by land management practices in Ethiopian tropical highland

et al. 2022

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“…Various factors from the soil that affect cassava crown formation could be assessed through the change of the CRC shape, size, angle and occupied underground space in 3D as indicated by the CRC compactness and cylinder soil volume. These soil factors may include rotting nematodes 44 , soil nutrition 45 and different subsoiler effects and compacted soil layers 46 . One can test whether the CRC shape would be horizontal in compacted soil and more vertical when the compacted layer has been broken.…”

Section: Discussionmentioning

confidence: 99%

Cassava root crown phenotyping using three-dimension (3D) multi-view stereo reconstruction

Sunvittayakul

Kittipadakul

Wonnapinij

et al. 2022

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Phenotypic analysis of cassava root crowns (CRCs) so far has been limited to visual inspection and very few measurements due to its laborious process in the field. Here, we developed a platform for acquiring 3D CRC models using close-range photogrammetry for phenotypic analysis. The state of the art is a low cost and easy to set up 3D acquisition requiring only a background sheet, a reference object and a camera, compatible with field experiments in remote areas. We tested different software with CRC samples, and Agisoft and Blender were the most suitable software for generating high-quality 3D models and data analysis, respectively. We optimized the workflow by testing different numbers of images for 3D reconstruction and found that a minimum of 25 images per CRC can provide high quality 3D models. Up to ten traits, including 3D crown volumes, 3D crown surface, root density, surface-to-volume ratio, root numbers, root angle, crown diameter, cylinder soil volume, CRC compactness and root length can be extracted providing novel parameters for studying cassava storage roots. We applied this platform to partial-inbred cassava populations and demonstrated that our platform provides reliable 3D CRC modelling for phenotypic analysis, analysis of genetic variances and supporting breeding selection.

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“…However, higher OC and TN contents observed in some parcels of land could be due to compost, manure, and crop residues combined with mineral fertilizers, among other factors (Bedada et al 2014;Vanlauwe et al 2010;Agegnehu et al 2016). Building soil organic matter is important not only for soil chemical and biological quality, but also to improve soil physical environment for augmented water retention, improved soil aggregation/structure, resistance to soil compaction/degradation, and increased root and crop growth (e.g., Vaz et al 2005;Braida et al 2008Braida et al , 2010Fasinmirin and Reichert 2011;Vogelmann et al 2010;Holthusen et al 2018;Awe et al 2021;Reichert et al 2021). And these effects should also be evaluated for Ethiopian soils since organic matter is of paramount importance in soil chemical, physical, and biological quality.…”

Section: The Implication For Soil Fertility Managementmentioning

confidence: 99%

Spatial variability modeling of soil fertility for improved nutrient management in Northwest Ethiopia

et al. 2021

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In the study, two geostatistical methods-Ordinary Kriging (OK) and Least Distance Weighting (IDW)-were utilized to predict the spatial variability and distribution of soil properties for improved nutrient management in the Fogera plain, Northwest Ethiopia. In an area of 5646 ha, 60 composite soil samples were collected at a 0-20-cm soil depth and analyzed for soil pH, organic carbon (OC), total nitrogen (TN), available phosphorus (av.P), exchangeable calcium (Ca 2+ ), potassium (K + ), sodium (Na + ), and magnesium (Mg 2+ ), electrical conductivity (EC), and cation exchange capacity (CEC) using standard analytical procedures. The data were then incorporated into a GIS database and semi-variogram, and geostatistical analyses were performed with ArcGIS software version 10.5. Descriptive statistical treatments were applied using IBM Statistical Packages for Social Sciences (SPSS) software version 24. The performance of interpolation methods was assessed using the mean error (ME), root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), and coefficient of determination (R 2 ) extracted from cross-validation of predicted maps. Analysis of different semi-variogram models depicts different degrees of spatial dependence. An exponential model is detected for the soil pH, CEC, OC, and TN; a spherical one for EC and Ca 2+ ; a moderate one for soil K + ; and a weak spatial dependence for soil av. P. This result demonstrates a high spatial continuity and dependence between adjacent soil samples. The inverse distance weighting (IDW) and ordinary kriging (OK) models well described the variation of all soil fertility parameters except organic carbon (OC) and available phosphorus (av. P), which had low NSE (≤ 50%) for both IDW and OK methods. Consequently, the generated maps revealed that the spatial variability of soil properties was adequate to predict the values of soil fertility indicators in the non-sampled locations within the study area and similar regions. The geostatistical-based soil fertility maps will be helpful for farmers, researchers, and policymakers to improve soil management methods, optimize fertilization strategies, and enhance crop productivity. By means of the methodological approach applied, we have succeeded in demonstrating the strong spatial dependence of the studied soils; however, a particular attention to the implementation of site-specific soil management practices in the Fogera plain is essential.

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Is cassava yield affected by inverting tillage, chiseling or additional compaction of no-till sandy-loam soil?

Abstract: How to cite: Reichert JM, Fontanela E, Awe GO, Fasinmirin JT. Is cassava yield affected by inverting tillage, chiseling or additional compaction of no-till sandy-loam soil? Rev Bras Cienc Solo. 2021;45:e0200134.

Cited by 20 publications

References 69 publications

Use of soil spectral reflectance to estimate texture and fertility affected by land management practices in Ethiopian tropical highland

Use of soil spectral reflectance to estimate texture and fertility affected by land management practices in Ethiopian tropical highland

Cassava root crown phenotyping using three-dimension (3D) multi-view stereo reconstruction

Spatial variability modeling of soil fertility for improved nutrient management in Northwest Ethiopia

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