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Rurinda, J.

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Rurinda
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Rurinda, J.

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    Science-based decision support for formulating crop fertilizer recommendations in sub-Saharan Africa
    (Elsevier, 2020) Rurinda, J.; Shamie Zingore; Jibrin, J.M.; Balemi, T.; Masuki, K.; Andersson, J.A.; Pampolino, M.F.; Mohammed, I.B.; Mutegi, J.; Kamara, A.Y.; Vanlauwe, B.; Craufurd, P.
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    Balanced nutrient requirements for maize in the Northern Nigerian Savanna: parameterization and validation of QUEFTS model
    (Elsevier, 2019) Shehu, B.M.; Lawan, B.A.; Jibrin, J.M.; Kamara, A.Y.; Mohammed, I.B.; Rurinda, J.; Shamie Zingore; Craufurd, P.; Vanlauwe, B.; Adam, A.M.; Merckx, R.
    Establishing balanced nutrient requirements for maize (Zea mays L.) in the Northern Nigerian Savanna is paramount to develop site-specific fertilizer recommendations to increase maize yield, profits of farmers and avoid negative environmental impacts of fertilizer use. The model QUEFTS (QUantitative Evaluation of Fertility of Tropical Soils) was used to estimate balanced nitrogen (N), phosphorus (P) and potassium (K) requirements for maize production in the Northern Nigerian Savanna. Data from on-farm nutrient omission trials conducted in 2015 and 2016 rainy seasons in two agro-ecological zones in the Northern Nigerian Savanna (i.e. Northern Guinea Savanna “NGS” and Sudan Savanna “SS”) were used to parameterize and validate the QUEFTS model. The relations between indigenous soil N, P, and K supply and soil properties were not well described with the QUEFTS default equations and consequently new and better fitting equations were derived. The parameters of maximum accumulation (a) and dilution (d) in kg grain per kg nutrient for the QUEFTS model obtained were respectively 35 and 79 for N, 200 and 527 for P and 25 and 117 for K in the NGS zone; 32 and 79 for N, 164 and 528 for P and 24 and 136 for K in the SS zone; and 35 and 79 for N, 199 and 528 for P and 24 and 124 for K when the data of the two zones were combined. There was a close agreement between observed and parameterized QUEFTS predicted yields in each of the agro-ecological zone (R2 = 0.69 for the NGS and 0.75 for the SS). Although with a slight reduction in the prediction power, a good fit between the observed and model predicted grain yield was also detected when the data for the two agro-ecological zones were combined (R2 = 0.67). Therefore, across the two agro-ecological zones, the model predicted a linear relationship between grain yield and above-ground nutrient uptake until yield reached about 50 to 60% of the yield potential. When the yield target reached 60% of the potential yield (i.e. 6.0 t ha−1 ), the model showed above-ground balanced nutrient uptake of 20.7, 3.4 and 27.1 kg N, P, and K, respectively, per one tonne of maize grain. These results suggest an average NPK ratio in the plant dry matter of about 6.1:1:7.9. We concluded that the QUEFTS model can be widely used for balanced nutrient requirement estimations and development of site-specific fertilizer recommendations for maize intensification in the Northern Nigerian Savanna.
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    Maize crop nutrient input requirements for food security in sub-Saharan Africa
    (Elsevier, 2019) Berge, H.F.M. ten; Hijbeek, R.; Van Loon, M.P.; Rurinda, J.; Tesfaye, K.; Shamie Zingore; Craufurd, P.; Heerwaarden, J. van; Brentrup, F.; Schröder, J.J.; Boogaard, H.; De Groote, H.; Ittersum, M.K. van
    Nutrient limitation is a major constraint in crop production in sub-Saharan Africa (SSA). Here, we propose a generic and simple equilibrium model to estimate minimum input requirements of nitrogen, phosphorus and potassium for target yields in cereal crops under highly efficient management. The model was combined with Global Yield Gap Atlas data to explore minimum input requirements for self-sufficiency in 2050 for maize in nine countries in SSA. We estimate that yields have to increase from the current ca. 20% of water-limited yield potential to approximately 50–75% of the potential depending on the scenario investigated. Minimum nutrient input requirements must rise disproportionately more, with N input increasing 9-fold or 15-fold, because current production largely relies on soil nutrient mining, which cannot be sustained into the future.
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    A spatial framework for ex-ante impact assessment of agricultural technologies
    (Elsevier, 2019) Andrade, J.F.; Rattalino Edreira, J.I.; Farrow, A.; Van Loon, M.P.; Craufurd, P.; Rurinda, J.; Shamie Zingore; Chamberlin, J.; Claessens, L.; Adewopo, J.; Ittersum, M.K. van; Cassman, K.G.; Grassini, P.
    Traditional agricultural research and extension relies on replicated field experiments, on-farm trials, and demonstration plots to evaluate and adapt agronomic technologies that aim to increase productivity, reduce risk, and protect the environment for a given biophysical and socio-economic context. To date, these efforts lack a generic and robust spatial framework for ex-ante assessment that: (i) provides strategic insight to guide decisions about the number and location of testing sites, (ii) define the target domain for scaling-out a given technology or technology package, and (iii) estimate potential impact from widespread adoption of the technology(ies) being evaluated. In this study, we developed a data-rich spatial framework to guide agricultural research and development (AR&D) prioritization and to perform ex-ante impact assessment. The framework uses “technology extrapolation domains”, which delineate regions with similar climate and soil type combined with other biophysical and socio-economic factors that influence technology adoption. We provide proof of concept for the framework using a maize agronomy project in three sub-Saharan Africa countries (Ethiopia, Nigeria, and Tanzania) as a case study. We used maize area and rural population coverage as indicators to estimate potential project impact in each country. The project conducted 496 nutrient omission trials located at both on-farm and research station sites across these three countries. Reallocation of test sites towards domains with a larger proportion of national maize area could increase coverage of maize area by 79–134% and of rural population by 14–33% in Nigeria and Ethiopia. This study represents a first step in developing a generic, transparent, and scientifically robust framework to estimate ex-ante impact of AR&D programs that aim to increase food production and reduce poverty and hunger.
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