Feed the crop, not the soil! : Explaining variability in maize yield responses to nutrient applications in smallholder farms of western Kenya

Kinyanjui, S. Njoroge

doi:10.18174/503185

Cited by 4 publications

(4 citation statements)

References 131 publications

(316 reference statements)

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“…Farm-level yields obtained during the programme (measured) aligned with yields from earlier field-level experimental work in western Kenya by Njoroge et al [ 17 ] and Vanlauwe et al [ 20 ] and were larger than the relatively poor yields described by Roobroeck et al [ 67 ], in particular during the long rains cropping seasons. The immediate response to increased input use as found in our study was similar to the response found by Njoroge [ 68 ] in western Kenya, when they applied full fertilisation after eight seasons of no or unbalanced fertilisation. During monitoring of input use we did not observe any new subsidy schemes or other changes in input availability in our research locations.…”

Section: Discussionsupporting

confidence: 89%

Narrowing yield gaps does not guarantee a living income from smallholder farming–an empirical study from western Kenya

et al. 2023

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Crop yields in sub-Saharan Africa need to increase to keep pace with food demands from the burgeoning population. Smallholder farmers play an important role in national food self-sufficiency, yet many live in poverty. Investing in inputs to increase yields is therefore often not viable for them. To investigate how to unlock this paradox, whole-farm experiments can reveal which incentives could increase farm production while also increasing household income. In this study we investigated the impact of providing farmers with a US$ 100 input voucher each season, for five seasons in a row, on maize yields and overall farm-level production in two contrasting locations in terms of population density, Vihiga and Busia, in western Kenya. We compared the value of farmers’ produce with the poverty line and the living income threshold. Crop yields were mainly limited by cash constraints and not by technological constraints as maize yield immediately increased from 16% to 40–50% of the water-limited yield with the provision of the voucher. In Vihiga, at best, one-third of the participating households reached the poverty line. In Busia half of the households reached the poverty line and one-third obtained a living income. This difference between locations was caused by larger farm areas in Busia. Although one third of the households increased the area farmed, mostly by renting land, this was not enough for them to obtain a living income. Our results provide empirical evidence of how a current smallholder farming system could improve its productivity and value of produce upon the introduction of an input voucher. We conclude that increasing yields of the currently most common crops cannot provide a living income for all households and additional institutional changes, such as alternative employment, are required to provide smallholder farmers a way out of poverty.

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Section: Discussionsupporting

confidence: 89%

Narrowing yield gaps does not guarantee a living income from smallholder farming–an empirical study from western Kenya

et al. 2023

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“…It is grown by over 3 million smallholder farmers, accounting for 70% of the nation's output (D'Alessandro et al., 2015). In recent years, maize production has encountered many challenges, including the high cost of inputs, declining soil fertility, pest and disease invasions, and poor coordination of extension service delivery (Kinyanjui, 2019; Tittonell et al., 2008). The challenges faced by rainfed maize production are further exacerbated by climate change (Salami et al., 2010).…”

Section: Introductionmentioning

confidence: 99%

Agronomic management response in maize (Zea maysL.) production across three agroecological zones of Kenya

Kipkulei,

Bellingrath‐Kimura,

Lana

et al. 2024

Agrosystems Geosci & Env

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Maize (Zea mays L.) productivity in Kenya has witnessed a decline attributed to the effects of climate change and biophysical constraints. The assessment of agronomic practices across agroecological zones (AEZs) is limited by inadequate data quality, hindering a precise evaluation of maize yield on a large scale. In this study, we employed the DSSAT‐CERES‐Maize crop model (where CERES is Crop Environment Resource Synthesis and DSSAT is Decision Support System for Agrotechnology Transfer) to investigate the impacts of different agronomic practices on maize yield across different AEZs in two counties of Kenya. The model was calibrated and evaluated with observed grain yield, biomass, leaf area index, phenology, and soil water content from 2‐year experiments. Remote sensing (RS) images derived from the Sentinel‐2 satellite were integrated to delineate maize areas, and the resulting information was merged with DSSAT‐CERES‐Maize yield simulations. This facilitated a comprehensive quantification of various agronomic measures at pixel scales. Evaluation of agronomic measures revealed that sowing dates and cultivar types significantly influenced maize yield across the AEZs. Notably, AEZ II and AEZ III exhibited elevated yields when implementing combined practices of early sowing and cultivar H614. The impacts of optimal management practices varied across the AEZs, resulting in yield increases of 81, 115, and 202 kg ha−1 in AEZ I, AEZ II, and AEZ III, respectively. This study underscores the potential of the CERES‐Maize model and high‐resolution RS data in estimating production at larger scales. Furthermore, this integrated approach holds promise for supporting agricultural decision‐making and designing optimal strategies to enhance productivity while accounting for site‐specific conditions.

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“…Nitrogen use efficiency and N surplus Nitrogen (N) use efficiency of maize was calculated at farm level per season: the total N outputs in maize grain (kg N ha -1 ) divided by the N inputs on all fields with maize (kg N ha -1 ). N output was calculated using the weighted average maize grain yield per farm and a fixed N content in maize grain of 1.54% (Njoroge, 2019). A farm level weighted average for N inputs was calculated based on the mineral fertiliser used per field, as reported in the monitoring survey.…”

Section: Environmentmentioning

confidence: 99%

“…However, when good yields and thereby soil N mining are continued, N and other nutrients (e.g. K) may become limiting (Njoroge, 2019) and fertilizer rates needs to be adjusted.…”

Section: Efference Of Concurrent Pathways Of Intensification and Extensificationmentioning

confidence: 99%

It’s time to harvest! : Towards sustainable farming systems in the East African highlands

Marinus

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Propositions 1. Land and capital limit smallholder farmers' incomes in sub-Saharan Africa (SSA), not technology. (this thesis) 2. Changes required for future farming systems need to be tested at farm level.(this thesis) 3. Education and training of field assistants is more important for good quality data than developing smart survey apps. 4. Cooperation and co-learning with local partners, from field assistants up to professors, should be considered as important as the scientific outcomes of research projects in SSA. 5. Governments calling farmers with low productivity "not serious", do not take their farmers serious. 6. Social media make us less social.

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Feed the crop, not the soil! : Explaining variability in maize yield responses to nutrient applications in smallholder farms of western Kenya

Abstract: With references, with summaries in English and Dutch.

Cited by 4 publications

References 131 publications

Narrowing yield gaps does not guarantee a living income from smallholder farming–an empirical study from western Kenya

Narrowing yield gaps does not guarantee a living income from smallholder farming–an empirical study from western Kenya

Agronomic management response in maize (Zea maysL.) production across three agroecological zones of Kenya

It’s time to harvest! : Towards sustainable farming systems in the East African highlands

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