**Abstract**

Prediction of yearly mid-growing season first and second critical dry spells using artificial neural networks (ANN) for enhanced maize yield in nine stations in Nigeria is performed. The ANN model uses nine meteorological parameters to predict onset dates and lengths of the critical dry spells. The daily dataset is from 1971 to 2013 of which about 70% is used for training while 30% is for testing. Seven ANN models are developed for each station with a view to measuring their predictive ability by comparing predicted values with the observed ones. Prediction lead times for the two critical dry spell onset dates generally range from about 2 weeks to 2 months for the nine stations. Error range during testing for the onset dates and lengths of first and second critical dry spells is generally ±4 days. The root-meansquare error (RMSE), coefficient of determination, Nash-Sutcliffe coefficient of efficiency, Wilmott's index of agreement, and RMSE observation standard deviation ratio range from 0.46 to 3.31, 0.58 to 0.93, 0.51 to 0.90, 0.82 to 0.95, and 0.30 to 0.69, respectively. These results show ANN capability of making the above reliable predictions for yearly supplementary irrigation planning, scheduling, and various other decision makings related to sustainable agricultural operations for improved rain-fed maize crop yield in Nigeria.

**Keywords:** Nigeria, rain-fed maize, critical dry spells, yearly prediction, artificial neural network

## **1. Introduction**

Variability of rainfall in Nigeria as well as in West Africa, etc., leads to the occurrence of wet and dry spells within the growing season. Short- and longduration dry spells are noted during the period of crop growth and development on yearly basis. Song et al. [1] using weather- and county-level maize yield data estimated the drought risk for maize in China for the period from 1971 to 2010. They noted that drought risk had increased in China over the last 40years and that the reasons for the observed changes were increased drought hazard associated with climate change and increased exposure of maize to drought due to an expanded production area. Significant drought incidents have seriously affected sustainable agriculture,

people's living condition, and the economy of many developing and under-developed countries [2, 3]. The occurrence and distribution of dry spells, especially the longer ones at critical times during growing season, generally have negative impact on maize crop development and yield under rain-fed farming in Nigeria. According to Mugalavai et al. [4] and Gao et al. [5], the most critical growth stages for maize crop in terms of dry spell occurrences are the germination, tasseling, and flowering. Germination is within the initial stage, while tasseling and flowering occur during the mid-season stage of growing season. The four crop growth stages are initial, development, mid-season, and late season [6]. Advance knowledge on critical dry spell onset dates and lengths for rain-fed maize crop on yearly basis is very important in supplementary irrigation planning, scheduling, and various other decision makings related to sustainable agricultural operations for improved maize yield.

Sharma [7] noted that a major challenge of drought research was to develop suitable methods and techniques for forecasting the onset and termination points of droughts. Successful development of suitable methods will enable stakeholders in agricultural and water resource sectors of the economy to embark upon risk-based (proactive) rather than crisis-based (reactive) approach to drought management in areas prone to drought [8, 9]. This is also applicable to dry spell management. Most publications are concerned with probabilistic, statistical, and stochastic modeling, and the most widely used stochastic models are autoregressive integrated moving average (ARIMA) models [10]. A dynamical model and a statistical model have been used to determine trends and make seasonal predictions of rainfall and dry spells occurrence in Ghana [11].

In recent years, neural-based models have been gaining attention over statistical models, possibly owing to the simplicity in modeling complex problems when many parameters are taken into consideration [12]. Abrishami et al. [13] used artificial neural network (ANN) model for estimating wheat and maize daily standard evapotranspiration. The results showed the suitable capability and acceptable accuracy of ANN. Mulualem and Liou [14] developed seven ANN predictive models incorporating hydro-meteorological, climate, sea surface temperatures, and topographic attributes to forecast the standardized precipitation evapotranspiration index (SPEI) for seven stations in the Upper Blue Nile basin (UBN) of Ethiopia from 1986 to 2015. Statistical comparisons of the different models showed that accurate results in predicting SPEI values could be achieved by including large-scale climate indices. Morid et al. [15] were able to show the efficiency of ANN when it was used for forecasting some drought indices in some selected places in Iran for up to 12 months lead times [3]. One neural network model was developed to forecast precipitation occurrences such as "rain" or "no-rain," while another model was developed to predict the amount of precipitation at several sub-levels using fuzzy techniques in Sri Lanka [16]. The ability of neural network model to predict "no-rain" situation gives it credence to forecast dry spell. Mathugama and Peiris [17] therefore recommended the exploration of the use of artificial neural network (ANN) to predict dry spell properties and that the models had to be statistically validated. Studies related to forecasting critical dry spell onset dates and lengths (especially mid-growing season dry spells) in Nigeria and other places are scarce. Farmers (especially maize farmers) in Nigeria desire to know on yearly basis when dry spells—critical dry spells—will occur after planting their crops to enable them plan their yearly agricultural operations effectively. In Nigerian Meteorological Agency (NiMet), numerical model have been used for sub-seasonal to seasonal forecasts of weather elements [18], while statistical models are used in seasonal rainfall forecasts for agricultural activities. Probabilistic forecasts have been made [19] for severe dry spell occurrences of lengths 10–21 days and moderate ones of lengths 8–15 days for 10 northern States for the month of June for year 2020; however, specific dry spells onset dates are not given.

*Critical Dry Spell Prediction in Rain-Fed Maize Crop Using Artificial Neural Network in Nigeria DOI: http://dx.doi.org/10.5772/intechopen.100627*

These informed our embarking on this study in aid of effective yearly agricultural operations for improved crop yield and maize in particular. The objective therefore of the present work is to predict the onset dates and lengths of midgrowing season critical dry spells for rain-fed maize crop *on yearly basis* in Nigeria using artificial neural network (ANN) model to enable farmers in those stations plan *yearly* agricultural operations for enhanced maize yield.
