**4.3 Contribution of precision irrigation technologies for sustainable maize production**

Smart Irrigation and Internet of Things (IoT) technologies consistently contributed to improve water and maize crop productivity. The power of the Internet of Things (IoT) can be used with sensors to monitor various factors like soil moisture levels, weather conditions, and plant water requirements. By collecting real-time data, smart irrigation systems can optimize water use during the cropping system to ensure precise irrigation schedules for more water use efficiency and productivity [34, 35]. Several approaches integrating use of IoT and sensor network have been implemented to efficiently collect and analyze data for promoting more sustainability in the irrigated cropping systems. Use of processed data at the edge server and transferred to the main IoT server is a real-time process of great utility to continuously manage the crops water requirements using only an Android smartphone application [36–38]. By implementing precise irrigation based on soil moisture sensors and IoT, maize producers can achieve higher yields while optimizing the use of resources such as fertilizers, water, and seeds [39, 40]. The comparison between precise irrigation using sensors and traditional flood irrigation showed that it is possible to conserve water by 50% and increase crop yield by 35% [41]. Integration of IoT technology is also of great importance to adapt for monitoring irrigation data for diverse crops. Singh et al. [26] evaluated an automated irrigation system for Maize, Paddy and Wheat crops to monitor soil moisture and soil temperature and transmit data to a cloud system for digital control of pump to efficiently satisfy the irrigation requirements. By considering Maize as the most important cereal crop worldwide [42], emerging sensors technologies can be of great importance to implement powerful tools helping for more sustainability in producing maize silage and grains. It helps farmers to implement decisional tools based on real-time data [43]. Sharifnasab et al. [44] tested smart irrigation for producing maize grain to show possibility of using only 40% of the farm's moisture discharge capacity. Compared to conventional practice of using meteorological data to guide irrigation decisions, the implementation of a smart irrigation system resulted in accelerated plant growth, earlier harvesting, and reduced water use (from 8839.5 to 5675.67 m3 /ha) for more grain yield and water productivity [44]. Kumar et al. [45] evaluated an irrigation method based on IoT to monitor soil moisture monitoring with reference to use of evapotranspiration-based strategy to manage sweet corn irrigation. The first IoT-based method implemented for two irrigation regimes of 43.5% and 34.8% of the soil field capacity (FC) is compared to the evapotranspiration method (ETc 100%) with 80% of FC. They find that the IoT method based on regime of 43.5% resulted in an increase yield of 12% and water savings of 11% compared to the ETc 100% irrigation method. Asiimwe et al. [46] compared and evaluated sweet corn yield, biomass, water productivity, and other morphometric characteristics based on irrigation scheduling using the irrigation amounts estimated from ET (60%, 90%, and 120% of ETc) and SM irrigation regimes (25%, 30%, and 35% of soil moisture) on sweet corn. The results showed that the average soil moisture levels using both treatments soil moisture (SM35%) and evapotranspiration (ET120%) were identic to show that irrigation can be reduced by 8% for the same grain yield and the highest irrigation level can result in an increase of fresh cob weight by 27%. Such smart irrigation innovations can help to elevate productivity levels while also ensuring sustainable agricultural practices [47]. Considered as a key component of precision farming, this advanced technology

*Sustainable Maize Production and Carbon Footprint in Arid Land Context: Challenges… DOI: http://dx.doi.org/10.5772/intechopen.112965*

**Figure 2.** *Smart irrigation system structures (From www.flaticon.com).*

is becoming affordable to be adopted by small-irrigated farms to optimize water productivity and enhance crop yield through implementation of irrigation best practices (**Figure 2**) [48].

#### **4.4 Crop growth modeling for sustainable maize production**

Maize crop as other crops is subjected to effect of the current meteorological conditions of climate change. Which affect negatively the yield of the crop. For this, growth simulation models are used to simulate different scenarios under the actual climate change [49, 50]. The most affected regions by climate change could be China, Africa, European Union and India, with a maximum decrease in maize yield of 86%, 201%, 71% and 45%, respectively [49]. The major factor affecting the rise in maize yield under climate change is the temperature [50]. The use of models to simulate and forecast the response of maize crop to different environmental conditions are used in several regions as an alternative tool to analyze the response to climate change conditions [49]. However, the simulation models are observed to give mixed results depending on the region and the crop. The parametrization, calibration and validation were found to be the source of uncertainties in model predictions [50]. Different situations could be simulated: those related to optimal conditions with restricted effect of climate conditions (T°, radiation and CO2), those related to resource availability (water and nutrient), and finally, those related to the reel conditions including all environmental, biological and management variables [49].

Actually, the complexity of the biophysical agricultural system is mathematically formulated by models helped to understand them [49]. The models used to simulate maize production are different in terms of information required, and the end user interface [49]. Climate, plant, soil and crop management are the input data needed by mechanistic models, such as AquaCrop, APSIM, DSSAT-CERES, CropSyst and EPIC [49–51]. Most of the studies revealed that corn yield decreases under climate

change projections, due to temperature increase which reduces vegetative period and dry matter production in some regions, while, there are other regions where the conditions of corn crop growth will be favorable (temperate regions) [49]. The use of experiment data is needed to calibrate and validate each model [51]. The calibration of DSSAT-CERES is made for each genotype of maize and estimate the genetic coefficient. In WOFOST model, the calibration is carried out in the different phenological stages [49].

Otherwise, AquaCrop is used as water-driven crop model under varying irrigation and nitrogen level in [51–54]. Model efficiency (E), coefficient of determination (R2), Root Mean Square error (RMSE) and Mean Absolute Error (MAE) Nash–Sutcliffe Efficiency (NSE) were used to test the model performance [51–54]. Appropriate levels of irrigation for maize crop were investigated by using AquaCrop model [51, 53, 55]. The prediction error of the model varied from 2.35 to 27.5% for different levels of irrigation and nitrogen [51]. Some extreme conditions may limit the performance of the model mainly, water stress, excess water and high evaporative demand conditions [52], and the accuracy of the model need more evaluation under field conditions of maize crop. AquaCrop model give good accuracy for field-measured trait for instance soil water, canopy cover, grain yield and total biomass [52, 53, 55]. The methods of field assessment to assessing maize crop yield are expensive, laborious and inaccurate. To overcome this, considerable efforts were made in the development and application of maize crop yield models for yield estimation. Such as the development of the use of models with remote sensing tools [56].
