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

Water deficitIrrigation schedulingMaizeSoil moisturePlant growthCrop yield

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

Simulation of Moisture Dynamics of the Soil Profile of a Maize Crop under Deficit Irrigation Scheduling

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 9 (2007): Simulation of Moisture Dynamics of the Soil Profile of a Maize Crop under Deficit Irrigation Scheduling. LW 06 015. Vol. IX. July, 2007