Balanced nutrient requirements for maize in the Northern Nigerian Savanna: Parameterization and validation of QUEFTS model

Shehu, Bello Muhammad; Lawan, Bassam A.; Jibrin, J.M.; Kamara, Alpha Y.; Mohammed, Ibrahim; Rurinda, Jairos; Zingore, Shamie; Craufurd, Peter; Vanlauwe, Bernard; Merckx, Roel

doi:10.1101/609602

Cited by 9 publications

(20 citation statements)

References 48 publications

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“…This translates to an increased recovery of 0.093 for N, 0.044 for P and 0.16 for K, resulting in an estimated recovery for normal seasons of 0.45 for N, 0.15 for P and 0.45 for K. These values are very much in line with expectations considering that fertilizer P was placed close to the planting hole (Van der Eijk et al, 2006). Shehu et al (2019) recently reported N, P and K recovery rates of 0.32-0.42, 0.08-0.16, and 0.37-0.54 respectively in farms in Nigeria following the application of 120-140, 40-50 and 40-50 kg ha -1 of fertilizer N, P and K respectively. The lower recovery rates were observed in the Sudan Savanna zone, with poor rainfall conditions that limited yields, while values in the Guinea Savanna zone were comparable to default values in QUEFTS, (Shehu et al, 2019).…”

Section: Discussionsupporting

confidence: 80%

“…Poor relationship between soil nutrient supply and soil properties in Step 1 of QUEFTS was also reported by Smaling and Janssen (1993), Saïdou et al (2003) and Shehu et al (2019), with re-calibration required to improve relationships between soil properties and soil nutrient supply. Recently recalibrated relationships for soil nutrient supply for on-farm studies in Nigeria (Shehu et al, 2019) did not result in better uptake estimates (not shown) for our study in western Kenya, indicating the need for site specific calibration of soil nutrient supply relations. This requirement presents a challenge for the effective use of QUEFTS for crop yield predictions and responses to fertilizer applications on individual smallholder farms.…”

Section: Discussionmentioning

confidence: 82%

“…Shehu et al (2019) recently reported N, P and K recovery rates of 0.32-0.42, 0.08-0.16, and 0.37-0.54 respectively in farms in Nigeria following the application of 120-140, 40-50 and 40-50 kg ha -1 of fertilizer N, P and K respectively. The lower recovery rates were observed in the Sudan Savanna zone, with poor rainfall conditions that limited yields, while values in the Guinea Savanna zone were comparable to default values in QUEFTS, (Shehu et al, 2019). This is in line with our observed effects of poor rainfall conditions on recovery of fertilizer N, P and K.…”

Section: Discussionmentioning

confidence: 99%

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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

Kinyanjui

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Section: Discussionsupporting

confidence: 80%

Section: Discussionmentioning

confidence: 82%

Section: Discussionmentioning

confidence: 99%

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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

Kinyanjui

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“…The study was carried out in three representative sites of the southern Guinea, northern Guinea and Sudan savannas of northern Nigeria (Fig. 1 ), which largely covers the maize cultivation area of Nigeria 3 , 4 . Kano represents the Sudan savanna (SS) zone and lies 484 m above sea level (a.s.l.).…”

Section: Methodsmentioning

confidence: 99%

“…Despite increase in current production, Nigeria’s average maize yield of 2 t ha –1 is among the lowest of the top 10 maize producers in Africa 1 . The bulk of the maize is produced in the Northern region where the savannas have favourable conditions required for maize growth 3 , 4 . Major constraints to maize production in the Nigeria savannas are poor soil fertility 5 , 6 , Striga hermonthica parasitism 7 , 8 , drought and unsuitable temperatures 9 .…”

Section: Introductionmentioning

confidence: 99%

Assessing the use of a drought-tolerant variety as adaptation strategy for maize production under climate change in the savannas of Nigeria

Tofa

Kamara

Babaji

et al. 2021

Sci Rep

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The Decision Support System for Agricultural Technology Transfer (DSSAT) was used to quantify the impact of climate change on maize yield and the potential benefits of the use of drought-tolerant maize variety over non-drought tolerant variety in savanna ecological zones of Nigeria. Projections of maize yields were estimated for three locations representing different agro-climatic zones and soil conditions, in the mid-century (2040–2069) and end-century (2070–2099) under representative concentration pathways scenarios (RCP 4.5 and 8.5) against the baseline period (1980–2009). Relative to the baseline period, the ensemble Global Circulation Models (GCMs) predicted significant increase in minimum and maximum temperatures and seasonal rainfall across the sites. In the mid-century, ensemble GCMs predicted temperatures increase between 1.7–2.4 °C for RCP4.5 and 2.2–2.9 °C for RCP8.5. By end-century, the temperature increases between 2.2–3.0 °C under RCP4.5 and 3.9–5.0 °C under RCP8.5. Predicted seasonal rainfall increase between 1.2–7% for RCP4.5 and 0.03–10.6% for RCP8.5 in the mid-century. By end of century, rainfall is expected to increase between 2–6.7% for RCP4.5 and 3.3–20.1% for RCP8.5. The DSSAT model predictions indicated a negative impact on maize yield in all the selected sites, but the degree of the impact varies with variety and location. In the mid-century, the results showed that the yield of the non–drought tolerant maize variety, SAMMAZ-16 will decline by 13–19% under RCP4.5 and 19–28% under RCP8.5. The projection by end-century indicates a decline in yield by 18–26% under RCP4.5 and 38–47% under RCP8.5. The yield of the drought-tolerant variety is projected to decline by 9–18% for RCP4.5 and 14–25% for RCP8.5 in the mid-century and 13–23% under RCP4.5 and 32–43% under RCP8.5 by the end-century. The higher temperatures by both emission scenarios (RCP 4.5 and 8.5) were primarily shown to cause more yield losses for non-drought-tolerant variety than that of the drought-tolerant variety. There will be 1–6% less reduction in yield when drought-tolerant variety is used. However, the higher yield reductions in the range of − 13 to − 43% predicted for the drought-tolerant variety by the end of the century across the study areas highlighted the need to modify the maize breeding scheme to combine both tolerances to drought and heat stresses in the agro-ecological zones of northern Nigeria.

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Compositional nutrient diagnosis and associated yield predictions in maize: A case study in the northern Guinea savanna of Nigeria

Shehu

Garba

Jibrin

et al. 2022

Soil Science Soc of Amer J

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Developing optimal strategies for nutrient management of soils and crops at a larger scale requires an understanding of nutrient limitations and imbalances. The availability of extensive data (n = 1,781) from 2‐yr nutrient omission trials in the most suitable agroecological zone for maize (Zea mays L.) in Nigeria (i.e., the northern Guinea savanna) provides an opportunity to assess nutrient limitations and imbalances using the concept of multi‐ratio compositional nutrient diagnosis (CND). We also compared and contrasted the use of linear regression models and bootstrap forest machine learning to predict maize yield based on nutrient concentration in ear leaves. The results showed that 35% of the experimental plots had low yields due to nutrient imbalances (hereafter referred to as low yield imbalanced [LYI]). These experimental plots were dominated by control plots (without any nutrients applied), plots without N fertilization, and plots without P fertilization. Using the control plot as the ultimate indicator of nutrient imbalance, the significantly limiting nutrients in order of decreasing frequency of deficiency were N, P, S, Ca > Cu, and B. Both linear regression and bootstrap forest machine learning models fairly predicted maize grain yield based on nutrient concentration in ear leaves only in the LYI group and when examining all data with an independent validation dataset. These results suggest that nutrient management strategies, especially through the site‐specific management approach, should consider S, Ca, Cu, and B in addition to the existing nutrients N, P, and K to improve nutrient balance and maize yield in the study area.

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Balanced nutrient requirements for maize in the Northern Nigerian Savanna: Parameterization and validation of QUEFTS model

Cited by 9 publications

References 48 publications

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

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

Assessing the use of a drought-tolerant variety as adaptation strategy for maize production under climate change in the savannas of Nigeria

Compositional nutrient diagnosis and associated yield predictions in maize: A case study in the northern Guinea savanna of Nigeria

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