Groundwater nitrate contamination is a significant concern in agricultural watersheds worldwide with it becoming a more pervasive problem in the last three decades. Models are great tools that are used to identify the sources and spatial patterns of nitrate contamination of groundwater due to agricultural activities. This Systematic Review (SR) seeks to provide a comprehensive overview of different models used to estimate nitrate contamination of groundwater due to agricultural activities. We described different types of models available in the field of modeling groundwater nitrate contamination, the models used, the input requirements of different models, and the evaluation metrics used. Out of all the models reviewed, stand-alone process-based models are predominantly used for modeling nitrate contamination, followed by integrated models, with HYDRUS and LEACHM models being the two most commonly used process-based models worldwide. Most models are evaluated using the statistical metric Root Mean Square Error (RMSE) followed by the correlation coefficient (r). This study provides the current basis for model selection in modeling nitrate contamination of groundwater due to agricultural activities. In addition, it also provides a clear and concise picture of the state of the art and implications to the scientific community doing groundwater quality modeling studies.

Understanding the impact of changes in climatic variables on reference evapotranspiration (ETo) is important for predicting possible implications of climate change on the overall hydrology of an area. This study aimed to determine the effects of changes in ETo with respect to changes in climatic variables. In addition, the specific objective was to determine the sensitivity coefficients of ETo in seven different locations in Nigeria with distinct agroecology, namely Maiduguri (Sahel savannah), Sokoto (Sudan savannah), Kaduna (Guinea savannah), Jos (Montane), Enugu (Derived Savannah), Ibadan (tropical rainforest), and Port Harcourt (coastal). The results showed that ETo is most sensitive to changes in maximum temperature (Tmax) in Maiduguri, Sokoto, Kaduna, and Jos. In Enugu and Ibadan, ETo is most sensitive to changes in solar radiation (Rs), while in Port Harcourt, ETo is most sensitive to relative humidity (RH). Overall, based on the average annual sensitivity coefficients (SCs) of the study area, the SC is ranked in the order: RH > Rs > Tmax > U2 > Tmin. Also, the results showed positive SCs of ETo to Rs, Tmax, U2, Tmin, and negative SC for RH. This study can serve as a baseline for sustainable water management in the context of climate change and adapted to areas with a similar climate.

Adaptation measures are essential for reducing the impact of future climate risks on agricultural production systems. The present study focuses on implementing an adaptation strategy to mitigate the impact of future climate change on rainfed maize production in the Eastern Kansas River Basin (EKSRB), an important rainfed maize-producing region in the US Great Plains, which faces potential challenges of future climate risks due to a significant east-to-west aridity gradient. We used a calibrated CERES-Maize crop model to evaluate the impacts of baseline climate conditions (1985-2014), late-term future climate scenarios (under the SSP245 emission pathway and CMIP6 models), and a novel root proliferation adaptation strategy on regional maize yield and rainfall productivity. Changes in the plant root system by increasing the root density could lead to yield benefits, especially under drought conditions. Therefore, we modified the governing equation of soil root growth in the CERES-Maize model to reflect the genetic influence of a maize cultivar to improve root density by proliferation. Under baseline conditions, maize yield values ranged from 6522 to 12,849 kgha^-1, with a regional average value of 9270 kgha^-1. Projections for the late-term scenario indicate a substantial decline in maize yield (36 % to 50 %) and rainfall productivity (25 % to 42 %). Introducing a hypothetical maize cultivar by employing root proliferation as an adaptation strategy resulted in a 27 % increase in regional maize yield, and a 28 % increase in rainfall productivity compared to the reference cultivar without adaptation. We observed an indication of spatial dependency of maize yield and rainfall productivity on the regional precipitation gradient, with counties towards the east having an implicit advantage over those in the west. These findings offer valuable insights for the US Great Plains maize growers and breeders, guiding strategic decisions to adapt rainfed maize production to the region's impending challenges posed by climate change.

Solar radiation (Rs) is an essential input for estimating reference crop evapotranspiration, ETo. An accurate estimate of ETo is the first step involved in determining water demand of field crops. The objective of this study was to assess the accuracy of fifteen empirical solar radiations (Rs) models and determine its effects on ETo estimates for three sites in humid tropical environment (Abakaliki, Nsukka, and Awka). Meteorological data from the archives of NASA (from 1983 to 2005) was used to derive empirical constants (calibration) for the different models at each location while data from 2006 to 2015 was used for validation. The results showed an overall improvement when comparing measured Rs with Rs determined using original constants and Rs using the new constants. After calibration, the Swartman–Ogunlade (R² = 0.97) and Chen 2 models (RMSE = 0.665 MJ∙m⁻²∙day⁻¹) performed best while Chen 1 (R² = 0.66) and Bristow–Campbell models (RMSE = 1.58 MJ∙m⁻²∙day⁻¹) performed least in estimating Rs in Abakaliki. At the Nsukka station, Swartman–Ogunlade (R² = 0.96) and Adeala models (RMSE = 0.785 MJ∙m⁻²∙day⁻¹) performed best while Hargreaves–Samani (R² = 0.64) and Chen 1 models (RMSE = 1.96 MJ∙m⁻²∙day⁻¹) performed least in estimating Rs. Chen 2 (R² = 0.98) and Swartman–Ogunlade models (RMSE = 0.43 MJ∙m⁻²∙day⁻¹) performed best while Hargreaves–Samani (R² = 0.68) and Chen 1 models (RMSE = 1.64 MJ∙m⁻²∙day⁻¹) performed least in estimating Rs in Awka. For estimating ETo, Adeala (R² =0.98) and Swartman–Ogunlade models (RMSE = 0.064 MJ∙m⁻²∙day⁻¹) performed best at the Awka station and Swartman–Ogunlade (R² = 0.98) and Chen 2 models (RMSE = 0.43 MJ∙m⁻²∙day⁻¹) performed best at Abakaliki while Angstrom–Prescott–Page (R² = 0.96) and El-Sebaii models (RMSE = 0.0908 mm∙day⁻¹) performed best at the Nsukka station.