OPTIMIZING PRODUCTIVITY OF MAIZE IN NIGERIAN SAVANNA AGRO- ECOLOGICAL ZONE: INFLUENCE OF NUTRIENT LIMITATIONS AND IMBALANCES BELLO MUHAMMAD SHEHU

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1 OPTIMIZING PRODUCTIVITY OF MAIZE IN NIGERIAN SAVANNA AGRO- ECOLOGICAL ZONE: INFLUENCE OF NUTRIENT LIMITATIONS AND IMBALANCES BELLO MUHAMMAD SHEHU TAMASA PhD STUDENT (NIGERIA) KATHOLIEKE UNIVERSITEIT LEUVEN (KU LEUVEN), BELGIUM SUPERVISORS (KU LEUVEN) PROF. ROEL MERCKX PROF. JAN DIELS PROF. MIET MAERTENS SUPERVISORS (NIGERIA) PROF. JIBRIN M. JIBRIN DR. ALPHA Y. KAMARA 1

2 TABLE OF CONTENT 1.0 DETAILED REPORT (SUMMARY) BACKGROUND INFORMATION HYPOTHESES MATERIALS AND METHODS RESULTS DISCUSSION AND CONCLUSION CONTRIBUTION OF THE WORK TO TAMASA OBJECTIVES AND NEED FROM TAMASA FOR FUTURE WORK REFERENCES LIST OF TABLES Table 1: Treatments Description... 7 Table 2: Nutrient Application Rates... 8 Table 3: Soil Physical and Chemical Characteristics of the Study Fields Table 4: Multinomial Logistic Regression Showing Soil Properties Responsible for Allocation of Field to a Specific Cluster in OPV trials Table 5: Multinomial Logistic Regression Showing Soil Properties Responsible for Allocation of Field to a Specific Cluster in Hybrid trials LIST OF FIGURES Figure 1: Map of Nigeria Showing the Trial Sites... 7 Figure 2: OPV Maize Grain Yield Vs Fertilizer Response Clusters Following K-means Multivariate Cluster Analysis Figure 3: OPV Maize Grain Yield Vs Fertilizer Response Clusters Following K-means Multivariate Cluster Analysis

3 1.0 DETAILED REPORT (SUMMARY) Maize yield in Nigeria has remained low and stagnant (<2 t ha -1 ) over five decades and far below the potential of the crop. One of the most important constraints is poor soil fertility. The poor soil fertility problem could be inherent because the dominant soil types are Lixisol, Cambisol and Acrisol. The first and latter have characterized with a dominance of low activity clays and small organic carbon leading to their low nutrient reserves. On the other hand, single blanket fertilizer recommendation is still a norm all over the country despite variable biophysical conditions. This type of recommendation was estimated based on small data for the larger area causing several nutrient imbalances, thus reducing the yield. Therefore there is a need to evaluate nutrient related constraints limiting maize yield and develop site-specific viable ways to counteract such constraints. About 198 diagnostic nutrient omission trials were conducted in 2015 and 2016 rainy seasons across TAMASA Nigeria focal area. In each of the site two sets of the trials were conducted, one with hybrid and the other with OPV, respectively. Yield response data as well as nutrients concentration in the soil, ear leaf, grain and stover have been determined. From the preliminary results. Three and five major yield response clusters to the nutrient omission application have been identified in OPV and hybrid trials, respectively. For both two varieties, the largest cluster constituting 62% of the study field are the soils with high response to N and P, no to low response to K and secondary macro- and micro-nutrients. The major significant soil variables deciding allocation of fields into a specific cluster are ph, P, Zn and Fe contents. This showed a wide variation in yield response to the nutrient application in the Nigerian Sudan Savanna zone and this variation was largely attributed to the variation in soil characteristics. Therefore, there a need to direct nutrient management strategies toward site-specific level in order optimize maize yield in the Nigerian Savanna agro-ecological zone. The information from this study (diagnostic nutrient omission trials) is necessary to calibrate NE expert tool as the major TAMASA output in work stream 3 (WR3). 3

4 1.1 BACKGROUND INFORMATION The total production of maize per annum in Nigeria has increased from 1.1 million metric tons in 1961 to about 10.4 million metric tons in 2013, this ranks the country as the 11 th largest producer of maize in the world (FAOSTAT, 2015). The increase in production was to a great extent attributed to the expansion in cultivated area from 1.38 million hectares in 1961 to about 5.2 million hectares in 2013 rather than intensification (FAOSTAT, 2015). The development of early and extra-early maize varieties which enabled production even in the drier Sudan Savanna agroecological zone where rainfall is low and erratic has been one of the most dominant factors for the area expansion of maize cultivation in Nigeria (Kamara et al., 2009). However, despite the increase in production, yield levels have remained very low (<2 t ha -1 ) and far below the potential of the crop. Soil nutrient depletion has been reported as one of the most important abiotic constraints contributing to the persistent small yields in Sub-Saharan Africa (SSA) including the Nigerian Savanna (Henao and Baanante 2006; Vanlauwe et al., 2006). The soils of the Nigerian Savanna are predominantly Lixisols, Cambisols and Acrisols, the first and the latter especially characterized by low activity clays. Most of the soils have very small organic matter contents, are depleted in nutrients and are susceptible to water and wind erosion (Jones and Wild 1975; FDALR 1999; FFD 2012). In addition, despite highly variable soil fertility conditions across Nigeria, a single fertilizer recommendation is still the norm for maize production across the country (FFD, 2012).This recommendation is considered to be semi-site-specific focusing on mainly three primary macronutrients (N, P, and K). Semi-site specificity here resides in the fact that recommendations were estimated based on few data for large areas. This type of recommendation generally leads to inappropriate fertilizer use, which eventually results in nutrient imbalances which affect the overall soil fertility, the maize productivity and in turn farmers returns. Therefore, to optimize the productivity of maize in the Nigerian Savanna zone, evaluation of site-specific nutrient related constraints and viable ways to counteract such constraints become imperative. 4

5 1.2 HYPOTHESES 1. Variability in soil fertility and productivity limits maize yield under current fertilizer recommendation in Nigerian Sudan Savanna agro-ecological zone. 2. Nutritional balances in maize production system in Nigerian Savanna zone are negative. 3. QUEFTS model can be used as a tool for making site-specific nutrient requirements that will improve nutrient balances and yield of maize in the Nigerian Savanna zone. 5

6 1.3 MATERIALS AND METHODS One hundred and ninety-eight (198) diagnostic nutrient omission trials (NOTs) were conducted in 2015 (No = 95) and 2016 (No = 103) rainy seasons respectively. The NOTs were conducted in three states of Nigeria within TAMASA project focal area namely; Kano, Katsina and Kaduna (Figure 1). All the sites fell within Northern Guinea and Sudan Savanna agro-ecological zones, respectively. The trial consists 6 nutrient treatments (Table 1 and 2): Control (no fertilizer) PK (-N) NK (-P) NP (-K) NPK NPK+S+Ca+Zn+Mg+B (+secondary macro- and micro-nutrients) Two set of the trial were conducted in each of the site, one with a hybrid variety and the other with OPV. OBA SUPA 9 and OBA SUPA 1 were the hybrids used for 2015 and 2016, respectively. The latter hybrid was used in 2016, because producing firm withdrew the earlier hybrid from the market due to some unexplained technical problems. IWD C2 SYN and EVDT W STR were the OPVs used in both two years for Northern Guinea Savanna and Sudan Savanna agro-ecological sites, respectively. Soil, ear leaf, grain and stover samples were collected and analyzed for all plant essential nutrients concentration at IITA Ibadan, Nigeria. The available yield data were subjected to K-means multivariate cluster analysis to understand the trend of variation of yield response to nutrient omission application using control treatment as base category. Also, to identify a soil parameter responsible for an allocation of a site into a specific cluster a multinomial logistic regression analysis was carried out. 6

7 Figure 1: Map of Nigeria Showing the Trial Sites Table 1: Treatments Description Plot Control PK NK NP NPK NPK+S+Ca+Zn+Mg+B Description No fertilizer application. Used to measure grain yield as an indicator of the effective indigenous NPK supply from soil, rain water, crop residue and atmosphere. N omission plot with recommended P and K amounts applied. Used to measure grain yield as an indicator of the effective indigenous N supply from soil, rain water, crop residue and atmosphere. P omission plot with recommended N and K amounts applied. Used to measure grain yield as an indicator of the effective indigenous P supply from soil, rain water, crop residue and atmosphere. K omission plot with recommended N and P amounts applied. Used to measure grain yield as an indicator of the effective indigenous K supply from soil, rain water, crop residue and atmosphere. N, P and K applied at recommended rate. Used to estimate the nutrient limited yield gap and evaluate agronomic use efficiencies of N, P, and K. This treatment will be used to assess the contribution of secondary and micronutrients to maize productivity. 7

8 Table 2: Nutrient Application Rates Nutrient Application Rate Type of Fertilizer to be Time of Application Used N 120 kg.ha -1 for SS and 140 kg.ha -1 for NGS Urea 3 splits (1/3 basal, 1/3 21 DAE and 1/3 42 DAE) P 40 kg.ha -1 for SS and 50 kg.ha -1 for TSP Basal NGS K 40 kg.ha -1 for SS and 50 kg.ha -1 for MOP Basal NGS S 20 kg.ha -1 As Ca, Mg, and Zn sulphates Basal Ca 10 kg.ha -1 Ca sulphate Basal Mg 10 kg.ha -1 Mg sulphate Basal Zn 5 kg.ha -1 Zn Basal B 5 kg.ha -1 Borax Basal DAE = Days after Emergence, SS = Sudan Savanna, NGS= Northern Guinea Savanna 8

9 1.4 RESULTS The soil characteristics of the study fields in Table 3 showed a high variation in organic carbon, available phosphorus and exchangeable cations (Ca, Mg and K) were they fell between low to moderate or high according to ESU (1991) and NSPFS (2005) fertility ratings of Nigeria. But on average organic carbon, total nitrogen and ECEC were low in the study fields. In contrary, available micronutrients (Fe, Cu, Zn, and Mn) were in moderate to high conditions. In terms of the response of yield to nutrient application, three major clusters with explained variance have been obtained in OPV trials (Figure 2). The attribute of each cluster is given below: Cluster I (8.1%): No to low responsive soils, adding nutrient does not improve yield. The attainable yield is 3 to 3.7 t ha -1 Cluster II (62.7%): Soils with high response to N and P, negative response to K and low response to secondary macro- and micro-nutrients. Attainable yield of 4.5 to 4.8 t ha -1. Cluster III (28.7%): Soils with high response to N only, low response to P and K, and negative response to secondary macro- and micro-nutrients. Attainable yield of 4.7 to 5.8 t ha -1 From Table 4, it can be seen that a decrease in soil Zn and Fe contents were significantly important in transforming non-responsive fields (Cluster I) into cluster II with high response to N and P. Meanwhile, a significant increase in P content will transform Cluster I (non-responsive) into cluster III with high response to N and low to P and K. In hybrid trials, five major clusters have been identified in terms yield response to nutrient omission relative to control which showed hybrid to be more responsive to nutrient application than OPV. The attribute of each cluster is given below: Cluster I (16.1%): No to low responsive soils, adding nutrient does not improve yield. Attainable yield between 2.7 to 3.8 t ha -1 Cluster II (4.6%): Soils with high response to N, low response to P, negative response to K, and a negative response to secondary macro- and micro-nutrients. Attainable yield of 5.8 to 7 t ha -1 9

10 Cluster III (62.7%): Soils with high response to N and P, no response K, and low response to secondary macro- and micro-nutrients. Attainable yield of 4.8 to 5.3 t ha -1 Cluster IV (4.0%): -Soils with high response to N and secondary macro- and micronutrients, negative response to P, and low response to K. Attainable yield of 6.4 to 8.3 t ha - 1 Cluster V (12.6%): Soils with high response to N, low response to P and K, and negative response to secondary macro- and micro-nutrients. Attainable yield of 5.2 to 6.6 t ha -1 Multinomial logistic regression in hybrid clusters (Table 5) revealed that significant decrease in soil ph and Fe are needed to transform Cluster I (non-responsive) to Cluster III (high response to N and P). Also, a significant increase in soil P content is required to transform the non-responsive cluster (cluster I) into cluster IV with high response to N and negative response to P. None of the soil parameter related to Cluster I and V meaning that they are explained by other soil physical and chemical constraints not measured from the preliminary results (probably soil S and B contents which are yet to be analyzed). Grain Yield (t ha -1 ) Control -N -P -K NPK +SMM 1 0 Cluster I Cluster II Cluster III Cluster Figure 2: OPV Maize Grain Yield Vs Fertilizer Response Clusters Following K-means Multivariate Cluster Analysis 10

11 Table 3: Soil Physical and Chemical Characteristics of the Study Fields Table 4: Multinomial Logistic Regression Showing Soil Properties Responsible for Allocation of Field to a Specific Cluster in OPV trials Soil Property Cluster II (High response to N and P, negative to K and low SMM) Exp(B) Cluster III (High response to N, low to P & K, negative to SMM) Exp(B) P * Silt Ca K Zn 0.722* Cu Fe 0.977** Reference Category = Cluster I (No to low responsive soils), Model p-value =0.003**, Exp(B)= odd ratio 11

12 10 Control -N -P -K 8 NPK +SMM Grain Yield (t ha -1 ) Cluster I Cluster II Cluster III Cluster IV Cluster V Response Cluster Figure 3: OPV Maize Grain Yield Vs Fertilizer Response Clusters Following K-means Multivariate Cluster Analysis Table 5: Multinomial Logistic Regression Showing Soil Properties Responsible for Allocation of Field to a Specific Cluster in Hybrid trials Soil Property Cluster II (High response to N, low to P, negative to K & SMM) Exp(B) Cluster III (High response to N & P, no to K, low to SMM) Exp(B) Cluster IV (High response to N & SMM, negative to P, low to K) Exp(B) Cluster V (High response to N, low to P & K, negative to SMM) Exp(B) ph ** OC P * Fe **

13 1.5 DISCUSSION AND CONCLUSION The average low organic carbon, total N, and ECEC indicates the potential of development of deficiency of basic cations and high N leaching potential of the study fields. The high contents of available micronutrients could be foreseen that the fields might not have a potential deficiency of those micronutrients. Available P, ph and exchangeable cations (Ca, Mg, and K) which showed a wide variability in the study fields explained the need for targeting soil nutrient management at the site-specific level to improve and sustain maize production in Nigerian Savanna agroecological zone. Yield response clustering was used in order to simplify the complex maize yield response pattern in the Nigerian Savanna. It is evident that an increase in soil P content above 12 mgkg -1 will make the field no to low response to P application irrespective of the variety (i.e. hybrid or OPV). The concentration of zinc and phosphorus in the soil and maize plant have shown to be mutually antagonistic and this relationship has been reported in various literature. In all the clusters there was no response to K, meaning that K is not a major maize limiting nutrient in the study fields. The no to low response to K application could be related to moderate and high soil available K content (>0.15) observed in all the study fields. To conclude, a wide variation exists in the response of maize yield to the nutrient application in Nigerian Savanna agro-ecological zone and this is attributed to a wide variation in soil characteristics. This showed the limit of current blanket fertilizer recommendation. Therefore, to optimize and sustained maize yield in Nigerian Savanna agro-ecological there is need to develop a site-specific nutrient management strategies. 13

14 1.6 CONTRIBUTION OF THE WORK TO TAMASA OBJECTIVES AND NEED FROM TAMASA FOR FUTURE WORK The information from this study (diagnostic nutrient omission trials) such as nutrient concentration in the soil, ear leaf, grain and stover as well the yield responses are necessary for the generation of algorithms to calibrate NE tool as one of the major output of TAMASA project in work stream 3 (WR3). The need from TAMASA is to complete the outstanding laboratory analyses arranged to be conducted at IITA Ibadan, Nigeria in order to have a complete results to enable complete analyses of fertility status of the soil, then use the nutrients concentration in the ear leaf to diagnose nutrient imbalances, and also use nutrients concentration in the grain and stover to calibrate parameters of QUEFTS for site-specific nutrient recommendation. 14

15 1.7 REFERENCES ESU, I. E. (1991). Detailed Soil Survey of NIHORT Farm at Bunkure Kano State, Nigeria. Ahmadu Bello University Zaria. Kaduna, Nigeria. FAOSTAT. (2015). Prodiction Statistics (prodstat), Rome: Food and Agriculture Organization of the United Nations. Retrieved from faostat3.fao.org/download/q/qc/e FDALR. (1999). Assessment of soil degradation in Nigeria. Project report. Ferderal Department of Agricultural Land Resources, Abuja. Abuja, Nigeria. FFD. (2012). Fertilizer use and management practices for Nigeria. 4th Edition. Federal Fertilizer Department, Federal Ministry of Agriculture and Rural Development, Abuja. Abuja, Nigeria. Henao, J., & Baanante, C. (2006). Agricultural Production and Soil Nutrient Mining in Africa Prepared by Summary of the Paper Agricultural Production and Soil Nutrient Mining in Africa Policy Development, (March). Jones, M. J., & Wild, A. (1975). Soils of West African Savanna. Technical communication No.55. Common Wealth Bereau of Soils. Harpenden, England. Kamara, A. Y., Ekeleme, F., Chikoye, D., & Omoigui, L. O. (2009). Planting date and cultivar effects on grain yield in dryland corn production. Agronomy Journal, 101, NSPFS. (2005). Nigerian Soil Fertility Rating and Thematic Maps. National Special Programme for Food Security (NSPFS). Abuja, Nigeria. Vanlauwe, B., Ramisch, J. J., & Saginga, N. (2006). Integrated Soil Fertility Management in Africa: from knowledge to implementation. Boil Approaches Sustainable Syst, 113,