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Accueil du site → Doctorat → Belgique → Optimizing site-specific variety, sowing density and nitrogen fertilizer recommendations for maize in the Nigerian Savannas using field experiments and modelling

Katholieke Universiteit Leuven (2020)

Optimizing site-specific variety, sowing density and nitrogen fertilizer recommendations for maize in the Nigerian Savannas using field experiments and modelling

Adnan, Aminu Adnan

Titre : Optimizing site-specific variety, sowing density and nitrogen fertilizer recommendations for maize in the Nigerian Savannas using field experiments and modelling

Optimalisatie van locatie-specifieke aanbevelingen voor keuze van variëteit, zaaidichtheid en stikstofbemesting voor maïs in de Nigeriaanse savanne met behulp van veldexperimenten en modellering

Auteur : Adnan, Aminu Adnan

Université de soutenance : Katholieke Universiteit Leuven

Grade : Thesis-dissertation 2020

Résumé partiel
Maize (Zea mays L.) has over the years become an important crop in the Nigerian Savannas including the semi-arid Sudan Savanna zone where production was initially not feasible. The annual maize output in the country changed from 1.06 million tonnes in 1976 to about 11.6 million tonnes in 2017, but the increase is due to expansion of area and not the much-needed intensification. The average yield per hectare has been below 2 Mg ha-1 since the 1970s, although yields >7 Mg ha-1 have been reported in research stations and best farmer fields. The reasons for the low per hectare yield have been attributed to the inherently poor soils, frequent droughts, pests & diseases and most importantly to lack of adherence to improved agronomic practices and use of improved inputs like fertilizers and seeds. In recent years, new maize varieties that are tolerant to most of the biotic and abiotic constraints have been developed for the Nigerian Savannas by the International Institute for Tropical Agriculture (IITA) and its partners. Several agronomic technologies have also been developed to increase the productivity of these varieties with a view to increasing maize yields. Dissemination of such varieties and technologies and their subsequent adoption requires setting up expensive and time-consuming multi-locational trials for evaluation. Selection of appropriate varieties across agro-ecologies and adoption of appropriate agronomic practices like optimum sowing density and site-specific fertilizer applications will be the key requirements for increase in production per unit area. Crop simulation modeling offers an opportunity to explore the potential of new varieties and crop management practices in different environments (soil, climate, management) prior to their release. Since most models have been developed elsewhere in Europe and USA, their use outside their domain of development requires a great deal of data for their calibration and evaluation. In addition, the shortage of technical know-how makes the use of those models more difficult especially by policy makers, farmers, technologists and extension agents. Overall, this research was conducted to evaluate the ability of a dynamic crop simulation model (DSSAT-CSM-CERES-Maize model) in matching maize varieties to the Sudan and Northern Guinea Savannas of Nigeria. The research also aims to use the model in making agronomic recommendations with respect to optimum sowing densities of the different varieties produced in the Nigerian maize belt. To achieve the set aims and objectives, data sets were collected from three different sources. Two of the data sets were collected by setting up field experiments while the third was collected from maize breeders in IITA. The first set of experiments were conducted in the rainy and dry seasons of 2016 in four research stations in the Nigerian Savanna. In the experiments, 26 maize varieties were planted under near-optimal environments (moisture and nutrient non-limiting). Growth, phenology and yield characteristics of each variety were measured with a view to developing "virtual" genotypic characteristics and incorporating it into the model. In addition to crop data, detailed soil data was collected from two profiles pits dug in each location together with daily weather data (minimum and maximum temperatures, daily rainfall and solar radiation).

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Page publiée le 18 mars 2021