Soil spatial variation to guide the development of fertilizer use recommendations for smallholder farms in western Kenya

Ichami, Stephen M.; Hoffland, Ellis; Karuku, George N.; Stoorvogel, J.J.

doi:10.1016/j.geodrs.2020.e00300

Cited by 17 publications

(13 citation statements)

References 61 publications

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“…Based on critical values suggested by Havlin et al (2013), nearly 94% of the soils contain TN above or equal to the optimum range (!0.15%). Similar to the report of Fageria and Baligar (2005), total N was higher at the highlands of the study area compared to the lowlands; because lowlands are warmer, leading to OC decomposition and associated loss of N. When CV of soil nutrients increases, the response of crops to a specific rate of fertilization would become non-uniform (Cao et al, 2012;Ichami et al, 2020). Here, the spatial map of soil nutrients could aid variable rate fertilization programs according to the site-specific needs of soils and crops.…”

Section: Critical Levels Of Soil Nutrients and Their Managementsupporting

confidence: 67%

Use of infrared spectroscopy and geospatial techniques for measurement and spatial prediction of soil properties

Takele

Iticha

2020

Heliyon

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The main aim of this research was to assess the use of mid-infrared (MIR) spectroscopy and geostatistical model for the evaluation and mapping of the spatial variability of some selected soil properties across a field. It is with the view of aiding site-specific soil management decisions. The performance of the model for the prediction of the components (soil parameters) was reported using the coefficient of determination (R 2) and root mean square error (RMSE) values of the validation data set. Results revealed that least square regression model performed better in predicting cation exchange capacity-CEC (R 2 ¼ 0.88 and RMSE ¼ 8.98), soil organic carbon-OC (R 2 ¼ 0.88, RMSE ¼ 0.55), and total nitrogen-TN (R 2 ¼ 0.91 and RMSE ¼ 0.04). The first five principal components (PC) accounted for 78.17% of the total variance (PC1 ¼ 25.75%, PC2 ¼ 18.06%, PC3 ¼ 13.85%, PC4 ¼ 11.12%, and PC5 ¼ 9.39%) and represented most of the variation within the data set. The coefficient of variation ranged from 6.73% for soil pH to 57.02% for available phosphorus (av. P). The soil pH values ranged from 4.21 to 6.57. The mean soil OC density was 2.14 kg m À2 within 50 cm soil depth. Nearly 96-97% of the soils contained av. P and sulfur (SO 4 2À-S) below the critical levels. The overall results revealed that soil properties varied spatially. Hence, we suggest that mapping the spatial variability of soils across a field is a cost-effective solution for soil management.

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Section: Critical Levels Of Soil Nutrients and Their Managementsupporting

confidence: 67%

Use of infrared spectroscopy and geospatial techniques for measurement and spatial prediction of soil properties

Takele

Iticha

2020

Heliyon

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“…Using CV, Tittonell et al (2007) reported up to 60 % variability among smallholder farms in Kenya. Similarly, Ichami et al (2020) showed between farm heterogeneity can range from 50 to 89% in smallholder systems, attributing 31 % of the variation to soil properties. Although significant heterogeneity has been documented in many African smallholder production systems, agronomic research for development generally ignores such heterogeneity in the formulation of productivity-boosting recommendations and programs ( Vanlauwe et al, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

Implications of intra-plot heterogeneity for yield estimation accuracy: Evidence from smallholder maize systems in Ethiopia

Sida

Chamberlin

Ayalew

et al. 2021

Field Crops Research

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Highlights We examine inter- and intra-plot heterogeneity of productivity in maize plots. We find high inter- and intra-plot variabilities in plant population and yield. Intra-plot heterogeneity overrides inter-plot heterogeneity in smallholder maize. The magnitude and distribution of yield estimation errors are method-dependent. More intensively managed plots show lower intra-plot heterogeneity and lower yield.

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“…Details on the study area, characteristic, and selection of the sites are provided by Ichami et al . [ 47 ]. The smallholder agroecosystem landscapes are characterized by subsistence farming systems, where farmers grow food crops such as maize ( Zea mays L .…”

Section: 0 Materials and Methodsmentioning

confidence: 99%

“…A farm survey was conducted using a Land Degradation Sampling Framework (LDSF) scheme [ 47 ], to obtain population estimates on soil and plant NPK nutrients status on targeted maize fields (Step 1, Fig 2 ). The farm survey was stratified into unfertilized maize fields, to capture variation in soil and plant information at different spatial scales.…”

Section: 0 Materials and Methodsmentioning

confidence: 99%

Spatial approach for diagnosis of yield-limiting nutrients in smallholder agroecosystem landscape using population-based farm survey data

et al. 2022

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Adept use of fertilizers is critical if sustainable development goal two of zero hunger and agroecosystem resilience are to be achieved for African smallholder agroecosystems. These heterogeneous systems are characterized by poor soil health mainly attributed to soil nutrient depletion. However, conventional methods do not take into account spatial patterns across geographies within agroecosystems, which poses great challenges for targeted interventions of nutrient management. This study aimed to develop a novel population-based farm survey approach for diagnosing soil nutrient deficiencies. The approach embraces principles of land health surveillance of problem definition and rigorous sampling scheme. The advent of rapid soil testing techniques, like infrared spectroscopy, offers opportune avenues for high-density soil and plant characterization. A farm survey was conducted on 64 maize fields, to collect data on soil and plant tissue nutrient concentration and grain yield (GY) for maize crops, using hierarchical and purposive sampling. Correlations between soil test values with GY and biomass were established. The relationship between GY, soil NPK, and the tissue nutrient concentrations was evaluated to guide the setting up of localized critical soil test values. Diagnosis Recommendation Integrated System (DRIS) indices for total nitrogen (N), total phosphorus (P), and total potassium (K) were used to rank and map the prevalence of nutrient limitations. A positive correlation existed between plant tissue nutrient concentration with GY with R2 values of 0.089, 0.033, and 0.001 for NPK, respectively. Soil test cut-off values were 0.01%, 12 mg kg-1, 4.5 cmolc kg-1 for NPK, respectively, which varied slightly from established soil critical values for soil nutrient diagnostics. N and K were the most limiting nutrients for maize production in 67% of sampled fields. The study demonstrates that a population-based farm survey of crop fields can be a useful tool in nutrient diagnostics and setting priorities for site-specific fertilizer recommendations. A larger-scale application of the approach is warranted.

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Soil spatial variation to guide the development of fertilizer use recommendations for smallholder farms in western Kenya

Cited by 17 publications

References 61 publications

Use of infrared spectroscopy and geospatial techniques for measurement and spatial prediction of soil properties

Use of infrared spectroscopy and geospatial techniques for measurement and spatial prediction of soil properties

Implications of intra-plot heterogeneity for yield estimation accuracy: Evidence from smallholder maize systems in Ethiopia

Spatial approach for diagnosis of yield-limiting nutrients in smallholder agroecosystem landscape using population-based farm survey data

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