Application of CSM-CERES-Maize for evaluation of planting date under rainfed conditions in the Northern Guinea Savanna agro-ecology of Nigeria

Transcription

1 Application of CSM-CERES-Maize for evaluation of planting date under rainfed conditions in the Northern Guinea Savanna agro-ecology of Nigeria K.O.OLUWASEMIRE and S.O. OYATOKUN Agronomy Department, University of Ibadan, Nigeria.

2 Introduction AGRICULTURE IS AN INDUSTRY THAT IS MORE DEPENDENT ON WEATHER THAN OTHER SYSTEMS FOR HARNESSING BIOLOGICAL RESOURCES SUCH AS CROP, LIVESTOCK, FORESTRY AND FISHING CLIMATE HAS A MAJOR IMPACT ON SOILS, VEGETATION, WATER RESOURCES AND LAND USE ISSUES REGARDING POPULATION INCREASE, DEGRADATION OF NATURAL RESOURCES AND SUSTAINABLE DEVELOPMENT CALLS FOR GREATER EFFORTS TO DESIGN FUTURE STRATEGIES OF LAND USE BASED ON SOUND UNDERSTANDING OF THE RELATIONSHIP BETWEEN CLIMATE AND AGRICULTURE THEREFORE, IMPROVED ADAPTATION OF FOOD PRODUCTION, PARTICULARLY TO CURRENT CLIMATE VARIABILITY AND OBSERVED CLIMATE CHANGE HOLD THE KEY TO IMPROVING FOOD SECURITY FOR THE GLOBAL POPULATION.

3 The high level of reliance of climate sensitive activities in agriculture such as rain-fed cultivation, limited economic and institutional capacity to cope with and adapt to climatic variability tend to make the Nigerian agricultural environment particularly vulnerable to climate change. Planting opportunities are limited by the amount and timing of rainfall, the decision on when to plant would affect the choice of crop cultivar and yield expectation. Rainfall in West Africa and particularly in Nigeria is strongly dependent on the southwest monsoon flow, which has the unique characteristics of high seasonal, monthly and daily variability in its moisture content and vertical depth (Omotosho et al., 2000).

4 Introduction (Cont d) NIGERIA HAS FOUR AGRO ECOLOGICAL ZONES WITH SIXTEEN PRIMARY CROPPING SYSTEMS IN THIRTEEN HIGH PRODUCTION STATES SAHEL AND SUDAN Sorghum & Millet and Sorghum or Millet & cowpea are the primary cropping systems in the high production states of Kano, Katsina, and Zamfara. GUINEA SAVANNA Sorghum & Groundnut or Cowpea, Maize (only) and Groundnut (only) are the primary cropping systems in the high production states of Niger, Kaduna, and Plateau. DERIVED SAVANNA Maize (only), Maize & Yam, Cowpea or Sweet Potato and Groundnut (only) are the primary cropping systems in the high production states of Oyo, Nasarawa, Kogi, Benue, and Taraba. HUMID FOREST Plantain (only) and Maize & Yam, Sweet Potato or Cassava are the primary cropping systems in the high production states of Cross River, Ondo, and Edo

5 Introduction (Cont d) NIGERIA NATIONAL PRODUCTION IS 9-10 MILLION TONS, GROWN ON OVER 5 MILLION HECTARES ACROSS THE ENTIRE COUNTRY Maize Production Zones Production Statistics for Top Food Crops in Nigeria Maize is grown in all states, however it s only the key crop in a few states Plays larger role in Guinea Savanna & Derived Savanna zones Where maize is important, it is often mono-cropped and farmers are commercially oriented Less than 10% maize is irrigated

6 The objective of this study is to evaluate the effects of planting dates and variability on maize performance under rain-fed conditions in Zaria area (northern Guinea savanna, Nigeria, and to assess CERES-Maize model performance by comparing the measured and simulated maize phenology for improved growth, development and yields as well as forecasting grain yield of maize production system

7 Materials and Methods 2.1 Site Description The data from a field study conducted during the 2006 rainy season at the experimental farm of the Institute for Agricultural Research (IAR), Ahmadu Bello University (ABU), Samaru-Zaria, Nigeria (11 09 N, E; 695 m above sea level) were considered. Weather data were recorded by an Automatic Weather Station-AWS (Minimet, Eijkelkamp, The Netherlands) about 20m from the experimental field. Data provided on measured variables include air temperature, relative humidity, wind speed and direction, net radiation and rainfall. These data were averaged and stored at two hours interval. The mean annual rainfall is mm ( ) with a coefficient of variability of 18 percent (Oluwasemire and Alabi, 2004). The rainy season starts by mid May and ends before mid October. Mean monthly temperature varies within C with a maximum and minimum of 38 C and 13 C, respectively. The soil temperature and moisture regimes are Isohyperthermic and Typic Tropustic (Uyovbisere et al., 1984).

8 Experimental Procedures The predicted onset window (dates) of growing season for Zaria from NIMET range from 13 th 23 rd May 2006 at a probability level of 70%. Four planting dates were adopted viz: planting date within one week before the predicted window, (PD1); planting date early within the predicted window, (PD2); planting date late within the window, (PD3) and planting date within the week after the window, (PD4). These corresponded to May 12, 2006 (PD1), May 16, 2006 (PD2), May 22, 2006 (PD3) and May 27, 2006 (PD4). The experiment was laid out as a Randomized Complete Block Design, with planting date as the treatment and replicated 4 times. 2 seeds of maize were planted per hole and later thinned ten days after emergence to one plant per stand. The maize variety was SAMMAZ 14 (Obatanpa) Fertilizer application rates were 60kg N, 30 kg P and 30 kg K ha -1 ; applied 12 days after planting when soil was moist enough from rain water. The fertilizer used was N.P.K 15:15:15 fertilizer brand. Thirty five days after planting, the maize was top dressed with urea (46% N) at the rate of 60 kg N ha -1. The plant spacing was 75cm x 25cm.

9 Plant Sampling and Measurements Records of crop emergence and daily records of weather parameters (maximum air temperature, minimum air temperature, relative humidity, wind speed and rainfall) were collected close to the experimental site. Records of date of planting, date of emergence (>50%), date of thinning, plant population after thinning were recorded. Destructive plant sampling commenced from emergence and was subsequently carried out at two week intervals. Plant stands from plot areas outside the designated final harvest area were sampled, oven-dried and weighed to obtain their dry matter. Whole plant leaves were also detached and measured for leaf area. Yield and yield parameters recorded included days to first bud appearance, first flower appearance, 50% tasseling, 50% silking, 50% flowering, 50% cob filling and physiological maturity. Others included date of harvest, Plant stand per plot, number of leaves on stem, number of grains per maize cob, grain weight per plot at harvest and haulm/stover weight per plot at harvest.

10 Evaluation of the model The CSM-CERES-Maize model was calibrated with the data obtained from the field experiment for the treatment that consisted of four planting dates of SAMMAZ 14 (Obatanpa) maize variety. The cultivar coefficients were determined sequentially, starting with phenological parameters followed by the grain filling parameters and finally total biomass and grain yield (Hunt and Boote, 1998). The experimental data collected were used for model evaluation. As part of the calibration and evaluation process the simulated data for physiological maturity date, leaf area index, dry shoot weight, grain yield and harvest index were compared with the observed values.

11 Statistical analysis of model data and maize yield prediction The statistical index used for model calibration and evaluation is the root mean square error (RMSE) method (Wallach and Goffinet, 1987). A simple way of expressing error is to express root mean square error as percentage of means of observation often referred to as normalized RMSE (n-rmse) The simulation is considered excellent with a PE less than 10 %, good if greater than 10% and less than 20%, fair if greater than 20% and less than 30%, and poor if greater than 30% (Jamieson et al., 1991). The equations are as written below: RMSE = [ (Pi-Oi) 2 /n] (1) PE = RMSE / Ō x 100 (2) Where RMSE = Root mean square error, Pi = predicted value, Oi = observed value, Ō = mean of observed value, n = number of replicates/locations, = Summation sign and = Square root sign. Maize yield forecasts were computed using 15 years ( ) daily weather data generated from MarkSim TM GCM-DSSAT weather file generator using the Representative Concentration Pathway - RCP 2.6 (RCP 3PD) climate change scenario and used to predict maize performance over period. The RCP 2.6 was developed by the IMAGE modeling team of the PBL Netherlands Environmental Assessment Agency.

12 Results and Discussion

13 Table 1: Physico-chemical and Hydrological properties of the soil profile in the study area Profile depth SMH SLL DUL SSAT RGF SSKS SBD Clay Silt Sand Textura l Class ph TOC TN Avail P 0-18 AP L AP L BT CL BT C BT C BT CL BT CL L- Loam; C - Clay; CL - Clay loam; SMH Soil morphological horizon; SLL Soil water lower limit (permanent wilting point); DUL Drained upper limit of soil (field capacity); SSAT Soil saturation; RGF Soil root growth factor; SSKS Hydraulic conductivity; SBD Soil bulk density; Data not available TOC - Total Organic Content, TN - Total Nitrogen,

14 Table 2: Genetic Coefficient of Maize variety, SAMMAZ 14 Variables Values P1=Thermal time from emergence to the end of the juvenile phase (degree days calculated with a base temperature of 8 C) P2= Delay in completing flower induction (d) for each hour increase in photoperiod above which development proceeds at a maximum rate (12.5 h) P5= Thermal time from silking to physiological maturity (degree days calculated with a base temperature of 8 C). G2= Potential number of kernels per plant G3= Potential kernel growth rate during the 8.80 linear grain filling stage PHINT= Phyllochron, thermal time between the appearance of consecutive leaf tips (degree days calculated with a base temperature of 8 C)

15 Table 3. Main growth and development variables at different planting dates Variable Simulated Measured % error (PE) 1 st Planting Date Physiological maturity day (dap) Yield at harvest maturity (kg [dm] Tops weight at maturity (kg [dm]/ha Leaf area index, maximum Harvest index at maturity nd Planting Date Physiological maturity day (dap) Yield at harvest maturity (kg [dm] Tops weight at maturity (kg [dm]/ha Leaf area index, maximum Harvest index at maturity rd Planting Date Physiological maturity day (dap) Yield at harvest maturity (kg [dm]

16 Table 3 contd.: Main growth and development variables at different planting dates Variable Simulated Measured % error (PE) 4 th Planting Date Physiological maturity day (dap) Yield at harvest maturity (kg [dm] Tops weight at maturity (kg [dm]/ha Leaf area index, maximum Harvest index at maturity

17 Leaf area index May May 22-May May Predicted year Fig. 1. Trend of predicted maize maximum leaf area over years as influenced by planting date

18 Shoot weight (kg/ha) May 16-May 22-May 27-May Predicted year Fig. 2. Trend of predicted maize shoot dry weight over the years as influenced by planting date

19 Days to physiological maturity May 16-May 22-May 27-May Predicted year Fig. 3. Trend of predicted days to physiological maturity of maize over the years as influenced by planting date

20 Grain yield (kg/ha) May 16-May 22-May 27-May Predicted year Fig. 4. Trend of predicted maize yield over the years as influenced by planting date

21 Harvest index May 16-May 22-May 27-May Predicted year

22 CONCLUSIONS The CSM-CERES-Maize model was able to accurately simulate phenology and yield of SAMMAZ grown during the rainy season in a northern Guinea savanna agroecological environment in Nigeria. In general, total biomass and LAI were also reasonably well simulated. For both rainfed maize production, average grain yield decreased with later planting dates. This study also showed that the CSM-CERES-Maize model can be a promising tool for yield forecasting for rain-fed maize varieties grown in the Nigerian Guinea savanna within predicted planting windows. This information is considered to be timely and useful for decision makers, farmers, climate-smart agriculture with veritable platform for precision agricultural practitioners and weather index insurance providers. Further research is needed to apply this methodology to different locations in order to be able to make practical decisions with respect to grain stock management. Additional model calibration and evaluation might also be needed for other varieties of high value rain-fed crops varieties used by farmers.

23 Acknowledgements The Commission for Agricultural Meteorology (CAgM), World Meteorological Organization (WMO) and the Korea Meteorological Administration (KMA) are highly appreciated for this inaugural workshop and for the invitation and sponsorship in respect of my participation. The Nigerian Meteorological Agency (NIMET) for providing information and opportunity to participate in this workshop. Thank you all for listening.