Land use efficiency under maize-based croping system in Zaria, Nigeria

Herbert, BC

doi:10.4314/joafss.v3i1.33713

Cited by 3 publications

(5 citation statements)

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“…Hence, technologies and methods that can provide leverage for quick and non-destructive data collection are critical for monitoring changes both spatially and temporally. The evolution of remote-sensing technologies for the acquisition and processing of remotely-sensed imageries is considered valuable for the assessment of yield or other agronomic variables at various scales [2,3]. However, access to high-quality imageries is generally constrained by associated costs and time-lag in processing, posing limitations to the prospects of utilizing imagery-derived data for various agronomic use-cases, both in-season and out-of-season.…”

Section: Introductionmentioning

confidence: 99%

“…Yet, there is a critical need to evolve reliable and rapid approaches for timely decision support within smallholder farming systems. Smallholder farming systems of sub-Saharan Africa (SSA) are often characterized by fragmented farmlands and differentiated management practices [3][4][5][6]. Most landscapes are complex mosaics with diffuse field boundaries and trees.…”

Section: Introductionmentioning

confidence: 99%

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Can a Combination of UAV-Derived Vegetation Indices with Biophysical Variables Improve Yield Variability Assessment in Smallholder Farms?

et al. 2020

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The rapid assessment of maize yields in a smallholder farming system is important for understanding its spatial and temporal variability and for timely agronomic decision-support. We assessed the predictability of maize grain yield using unmanned aerial/air vehicle (UAV)-derived vegetation indices (VI) with (out) biophysical variables on smallholder farms. High-resolution imageries were acquired with UAV-borne multispectral sensor at four and eight weeks after sowing (WAS) on 31 farmer managed fields (FMFs) and 12 nearby nutrient omission trials (NOTs) sown with two genotypes (hybrid and open-pollinated maize) across five locations within the core maize region of Nigeria. Acquired multispectral imageries were post-processed into three VIs, normalized difference VI (NDVI), normalized difference red-edge (NDRE), and green-normalized difference VI (GNDVI) while plant height (Ht) and percent canopy cover (CC) were measured within georeferenced plot locations. Result shows that the nutrient status had a significant effect on the grain yield (and variability) in NOTs, with a maximum grain yield of 9.3 t/ha, compared to 5.4 t/ha in FMFs. Generally, there was no relationship between UAV-derived VIs and grain yield at 4WAS (r < 0.02, p > 0.1), but significant correlations were observed at 8WAS (r ≤ 0.3; p < 0.001). Ht was positively correlated with grain yield at 4WAS (r = 0.5, R2 = 0.25, p < 0.001) and more strongly at 8WAS (r = 0.7, R2 = 0.55, p < 0.001), while the relationship between CC and yield was only significant at 8WAS. By accounting for within- and between-field variations in NOTs and FMFs (separately), predictability of grain yield from UAV-derived VIs was generally low (R2 ≤ 0.24); however, the inclusion of ground-measured biophysical variable (mainly Ht) improved the explained yield variability (R2 ≥ 0.62, Root Mean Square Error of Prediction, RMSEP ≤ 0.35) in NOTs but not in FMFs. We conclude that yield prediction with UAV-acquired imageries (before harvest) is more reliable under controlled experimental conditions (NOTs), compared to actual farmer managed fields where various confounding agronomic factors can amplify noise-signal ratio.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Can a Combination of UAV-Derived Vegetation Indices with Biophysical Variables Improve Yield Variability Assessment in Smallholder Farms?

et al. 2020

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“…Similarly, LUE was recorded 44.4% higher on T 4 followed by T 3 (41.85%). This was due to the fact that land was with standing crop for longer time than the traditional method of jhum cultivation (Herbert, 2005). The increment in income and employment was recorded higher on T 4 .…”

Section: Equivalent Yield Production and Land-use Efficiencymentioning

confidence: 93%

“…The duration of rice was about 150 days (dibbling to harvest) and duration of other treatments was calculated by adding the duration of sequential crops with the base crop. Production efficiency (PE) was measured on the basis of yield on per day basis (Azam et al, 2008); similarly land use efficiency (LUE) was measured by comparing the percentage increased in land use after imposing treatments with control (Herbert, 2005). Increase in income and employment were also expressed in percentage by calculating the additional income and employment generated after executing various treatments.…”

Section: Observations Recordedmentioning

confidence: 99%

Improvement of Jhum with Crop Model and Carbon Sequestration Techniques to Mitigate Climate Change in Eastern Himalayan Region, India

Choudhary¹,

Kumar²,

Kanwat³

et al. 2012

JAS

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Slash and burn cultivation (jhum) is the most disadvantageous method of cultivation in the Eastern Himalayan Region. This practice causes soil, nutrient, water erosion and biodiversity loss. Therefore, alternate conservation practice is required urgently. Field experiment was conducted at two villages viz., Digbak and Belo with two sites at each village on Dumporijo circle district Daporijo, Arunachal Pradesh during 2008 to 2010. The experiment was laid out in such a way that farmers practice was considered as control (T 1 ); T 2 : T 1 + Mulching with crop residues; T 3 : Improved crop management (includes plant population, application of manure and fertilizer, pesticides, weed management, hedge row incorporation); T 4 : T 3 + mulching with crop residues. T 4 gave higher yield of rice, maize, chilli, tomato, french bean, okra (69, 107, 163, 211, 98.6 and 126% respectively) over T 1 . Whereas, pea and soybean were additional crop harvested from T 2 , T 3 and T 4 . Similarly the biomass (crop, hedge row plants and weeds) was recorded 225.8, 208.5 and 19.9% higher on T 4 , T 3 and T 2 , respectively over T 1 . The final status of porosity was recorded 7.6, 6.7 and 2.4% respectively higher for T 4 , T 3 and T 2 over traditional jhum cultivation. Similarly the chemical parameters like soil organic carbon (SOC; 43.3, 39.2 and 21.6% respectively), N (25.4, 19.6 and 6.7% respectively), P (45.2, 39.8 and 12.9% respectively) and K (31.3, 25.9, and 5.0% respectively) were recorded higher on T 4 , T 3 and T 2 over traditional jhum cultivation. The rice equivalent yield, production efficiency, land use efficiency and income were recorded higher on T 4 followed by T 3 . However the employment generation was higher for T 3 followed by T 4 . All the economic parameters were recorded higher when crops were grown under the T 4 followed by T 3 except B: C and MC: MR.

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“…However, this is limited by associated costs and availability of quality images for in-season and out-of-season applications. Smallholder farming systems of sub-Saharan Africa (SSA) are often characterized by fragmented farmlands and differentiated management practices (Herbert, 2005;Giller et al, 2011;Onuk et al, 2015;Vanlauwe, et al, 2015). Most landscapes are complex mosaics with diffuse field boundaries and trees.…”

Section: Introductionmentioning

confidence: 99%

<strong></strong>Can Combination of UAV-derived Vegetation Indices with Biophysical Variable(s) Improve Yield Variability Assessment in Smallholder Farms?

Adewopo¹,

Peter²,

Kamara³

et al. 2020

Preprint

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Rapid assessment of maize yields in smallholder farming system is important to understand its spatial and temporal variability and for timely agronomic decision-support. Imageries acquired with unmanned air vehicles (UAV) offer opportunity to assess agronomic variables at field scale, however, it is not clear if this can be translated into reliable yield assessment on smallholder farms where field conditions, maize genotypes, and management practices vary within short distances. This study was conducted to assess the predictability of maize grain yield using UAV-derived vegetation indices (VI), with(out) biophysical variables, in smallholder farms. High-resolution images were acquired with UAV-borne multispectral sensor at 4 and 8 weeks after sowing (WAS) on 31 farmers’ managed fields (FMFs) and 12 nearby Nutrient Omission Trials (NOT), all distributed across 5 locations within the core maize region of Nigeria. The NOTs included non-fertilized and fertilized plots (with and without micronutrients), sown with open pollinated or hybrid maize genotypes. Acquired multispectral images were post-processed into several three (s) vegetation indices (VIs), normalized difference vegetation index (NDVI), normalized difference red-edge (NDRE), green-normalized difference vegetation index (GNDVI). Biophysical variables, plant height (Ht) and percent canopy cover (CC), were measured with the georeferenced plot locations recorded. In the NOTs, the nutrient status, not genotype, influenced the grain yield variability and outcome. The maximum grain yield observed in NOTs was 9.3 tha-1, compared to 5.4 tha-1 in FMF. Without accounting for between- and within-field variations, there was no relationship between UAV-derived VIs and grain yield at 4WAS (r<0.02, P>0.1), but significant correlations were observed at 8WAS (r≤0.3; p<0.001). Ht was positively correlated with grain yield at 4WAS (r=0.5, R2=0.25, p<0.001), and more strongly at 8WAS (r=0.7, R2=0.55, p<0.001), while relationship between CC and yield was only significant at 8WAS. By accounting for within- and between-field variations in NOTs and FMF (separately) through linear mixed-effects modeling, predictability of grain yield from UAV-derived VIs was generally (R2≤0.24), however, the inclusion of ground-measured biophysical variable (mainly Ht) improved the explained yield variability (R2 ≥0.62, RMSEP≤0.35) in NOTs but not in FMF. We conclude that yield prediction with UAV-acquired imageries (before harvest) is more reliable under controlled experimental conditions (NOTs), compared to actual farmer-managed fields where various confounding agronomic factors can amplify noise-signal ratio.

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Land use efficiency under maize-based croping system in Zaria, Nigeria

Cited by 3 publications

References 0 publications

Can a Combination of UAV-Derived Vegetation Indices with Biophysical Variables Improve Yield Variability Assessment in Smallholder Farms?

Can a Combination of UAV-Derived Vegetation Indices with Biophysical Variables Improve Yield Variability Assessment in Smallholder Farms?

Improvement of Jhum with Crop Model and Carbon Sequestration Techniques to Mitigate Climate Change in Eastern Himalayan Region, India

<strong></strong>Can Combination of UAV-derived Vegetation Indices with Biophysical Variable(s) Improve Yield Variability Assessment in Smallholder Farms?

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Land use efficiency under maize-based croping system in Zaria, Nigeria

Cited by 3 publications

References 0 publications

Can a Combination of UAV-Derived Vegetation Indices with Biophysical Variables Improve Yield Variability Assessment in Smallholder Farms?

Can a Combination of UAV-Derived Vegetation Indices with Biophysical Variables Improve Yield Variability Assessment in Smallholder Farms?

Improvement of Jhum with Crop Model and Carbon Sequestration Techniques to Mitigate Climate Change in Eastern Himalayan Region, India

<strong>­­­</strong>Can Combination of UAV-derived Vegetation Indices with Biophysical Variable(s) Improve Yield Variability Assessment in Smallholder Farms?

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<strong></strong>Can Combination of UAV-derived Vegetation Indices with Biophysical Variable(s) Improve Yield Variability Assessment in Smallholder Farms?