Impact of Methods of Administering Growth-Stage Deficit Irrigation on Yield and Soil Water Balance of a Maize Crop (SAMAS TZEE)

Field experiments were conducted in 2009/10 and 2010/11 irrigation seasons at the Institute for Agricultural Research, Samaru Zaria, to assess the impact of two methods of administering Growth-stage deficit irrigation scheduling (GSDIS) on yield and soil water balance of an early maturing maize variety. The two methods include reducing water application depth at selected crop growth stages and skipping regular irrigation interval at selected crop growth stages. The test crop was SAMAS TZEE early maturing maize variety. Grain yield, biomass yield, harvest index, seasonal water applied, evapotranspiration and deep percolation and crop water productivity were determined. Grain and biomass yields ranged from 2.12 to 3.01 t/ha and 7.57 to 10.0t/ha, respectively, while seasonal evapotranspiration varied from 366 to 486.8 mm across the seasons. Thisstudy reveal that at vegetative growth stage of the maize crop, it is better to skip weekly irrigation (to irrigation every other week) and apply water to meet full water requirement than to maintain regular weekly irrigation butapply water at half water requirement. A grain filling to maturity stage, it is more advantageous to reduce irrigation water application by half water requirement than to skip weekly irrigation. Grain yield, biomass yieldand seasonal evapotranspiration from such scheduling were not significantly different from that which received weekly irrigation throughout the crop growing season. Moreover, the productivity of water applied was higher while water loss to deep percolation was drastically reduced.Keywords: Deficit irrigation scheduling, Economic net return, Maize crop, Irrigation water managemen

Productivity of water and economic benefit associated with deficit irrigation scheduling in maize

Water deficitIrrigation schedulingMaizeSoil moisturePlant growthCrop yield

Irrigation Scheduling Scenarios Studies for a Maize Crop in Tanzania Using a Computer-based Simulation Model

Rosana G. Moreira, Editor-in-Chief; Texas A&M UniversityThis is a paper from International Commission of Agricultural Engineering (CIGR, Commission Internationale du Genie Rural) E-Journal Volume 8 (2006): Irrigation Scheduling Scenarios Studies for a Maize Crop in Tanzania Using a Computer-based Simulation Model. Manuscript LW 06 007. Vol. VIII. November, 2006

Productivity of water and economic benefit associated with deficit irrigation scheduling in maize

Water deficitIrrigation schedulingMaizeEvapotranspirationCrop yieldEconomic aspects

Trends of productivity of water in rain-fed agriculture: historical perspective

Rain-fed farmingProductivityCrop productionWater requirementsEvapotranspiration

Trends of productivity of water in rain-fed agriculture

Rain-fed farmingProductivityCrop productionWater requirementsEvapotranspiration

Calibrating and validating AquaCrop model for maize crop in Northern zone of Nigeria

Farmers in the northern Guinea Savannah ecological zone of Nigeria have been experiencing declining crop yield due to erratic water supply. In recent times, research on better water management and interaction between effects of climate, soil and field management on crop production is fast gaining grounds with the use of models. Models can be used to predict the impact of long-term climate variability, thus providing an opportunity of better techniques compared with the traditional multi-location trials. This study presents the calibration and validation of AquaCrop model for drip irrigated maize (Zea mays). Calibration was done using data of 2013, while validation across seasons was done with data of 2014. The modelling efficiency of grain yield, biomass yield and crop water use were 81%, 90%, and 85% when calibration was done, while during the validation the modelling efficiency were 86%, 74% and 50%, respectively. This indicates a good fit between the simulated output and measured data. The model has a tendency to over-predict grain and biomass yield at harvest by 3%-4%, under-predict seasonal evapotranspiration by 2%, and over-predict grain water productivity by 3% and biomass water productivity by 24% according to the coefficient of residual mass. The AquaCrop model high reliability for the simulations indicates it can be useful for on-the-desk assessing of the impact of irrigation scheduling protocols when properly calibrated

Crop Coefficient of Tomato under Deficit Irrigation and Mulch Practices at Kano River Irrigation Project, Nigeria

This work determined the effects of deficit irrigation and mulching practices on crop coefficient (Kc) of tomato in the Kano River Irrigation Project (KRIP) Kadawa, Kano, Nigeria. Experiments comprised of four levels of water application depths (40, 60, 80, and 100% of weekly reference evapotranspiration) and four levels of mulching (No-Mulch (NM), Rice-Straw-Mulch (RSM), Wood-Shaving-Mulch (WSM) and White-Polyethylene-Mulch (WPM)) was conducted to examine changes in Kc value. The mean Kc values (early, developmental, mid and late stages) of fully irrigated treatments were 0.70, 0.81, 1.07 and 0.78; 0.64, 0.76, 0.99 and 0.71; 0.60, 0.73, 0.94 and 0.69; and 0.53, 0.66, 0.86 and 0.62 for NM, RSM, WSM and WPM respectively while that of deficit irrigation ranged from 0.17 to 1.13 across the treatments, noting that the highest Kc was observed under NM treatments. Statistical analysis reveals that the effect of various levels of irrigation and mulching practices on Kc of tomato was highly significant at P＜0.05 level of significance with a high mean value of 1.13 obtained at I100 and NM respectively. It was concluded to encourage tomato farmers in KRIP to adopt the use of their rice straw for mulching cum deficit irrigation (20%) towards conserving irrigation water for sustainability. Also, results obtained from this study can be used as a guide to farmers in irrigating tomato crop and to engineers in the design of irrigation systems

PERFORMANCE OF MULTIPLE LINEAR REGRESSION AND AUTOREGRESSIVE INTEGRATED MOVING AVERAGE MODELS IN PREDICTING ANNUAL TEMPERATURES OF OGUN STATE, NIGERIA

The performance of Autoregressive Moving Average and Multiple Linear Regression Models in predicting minimum and maximum temperatures of Ogun State is herein reported. Maximum and Minimum temperatures data covering a period of 29 years (1982 -2009) obtained from the Nigerian Meteorological Agency (NiMet), Abeokuta office, Nigeria, were used for the analyses. The data were first processed and aggregated into annual time series. Mann-Kendal non-parametric test and spectral analysis were carried out to detect whether there is trend, seasonal pattern, and either short or long memory in the time series. Mann-Kendal Z-values obtained are –0.47 and –2.03 for minimum and maximum temperatures respectively, indicating no trend, though the plot shows a slight change. The Lo’s R/S Q(N,q) values for minimum and maximum temperatures are 3.67 and 4.43, which are not within the range 0.809 and 1.862, thus signifying presence of long memory. The data was divided into two and the first 20 years data was used for model development, while the remaining was used for validation. Autoregressive Moving Average (ARMA) model of order (5, 3) and Autoregressive (AR) model of order 2 are found best for predicting minimum and maximum temperatures respectively. Multiple Linear Regression (MLR) model with 4 features (moving average, exponential moving average, rate of change and oscillator) were fitted for both temperatures. The ARMA and AR models were found to perform better with Mean Absolute Percentage Error (MAPE) values of -2.89 and -1.37 for minimum and maximum temperatures, compared with the Multiple Linear Regression Models with MAPE values of 141 and 876 respectively. Results of ARMA model can be relied on in generating forecast of temperature of the study area because of their minimal error values. However, it is recommended other climatic elements that were not captured in this paper due to unavailability of information be considered too in order to see which model is best for them. &nbsp