Efficient water management is crucial for sustainable agriculture and water resource conservation, particularly in water-scarce regions. This study investigated the effect of different irrigation depths on onion (Allium cepa L.) yield and water use patterns in a semi-arid tropical region of Colombia, using a completely randomized design with five treatments. The treatments ranged from 0–100% of total available water (TAW), T1 (100% of TAW), T2 (80% of TAW), T3 (60% of TAW), T4 (40% of TAW), and T5 (20% of TAW). The experiment was conducted in a greenhouse during one growing season (2022–2023). The normalized water productivity (WP *), irrigation water productivity (IWP), consumptive water productivity, blue water footprint (WF_blue), marginal water use efficiency (MWUE), and elasticity of water productivity (EWP), as well as some parameters of quality onion, were determined. The soil in the experimental field was classified as sandy loam; the results show that the WP * of onion is 17.42 g m⁻², the water production function shows the maximum production will be achieved at a water application depth of approximately 943 mm, and beyond that, the biomass yield will decrease with additional water application, IWP values for onion ranged from 2.18 to 3.42 kg m⁻³, the highest Wf_blue was in T5 (34.10 m³ t⁻¹), and low Wf_blue was T1 (20.95 m³ t⁻¹). In terms of quality, treatment T1 had the most favorable effects on bulb weight, polar diameter, and equatorial diameter, while treatment T5 had the least favorable effects. The study highlights the importance of efficient irrigation on sandy loam soils to maximize yield and water use efficiency. It provides valuable data for evaluating the potential yield benefits of precision irrigation in the study area. Optimizing irrigation depth can significantly improve onion yield and water use efficiency in semi-arid regions.

Crop models that can accurately estimate yield and final biomass have been used for major herbaceous crops and to a lesser extent in forage systems. The AquaCrop version 7.0 contains new modules that have been introduced to simulate the growth and production of perennial herbaceous forage crops. Simulated forage yields as a function of water consumption provide valuable information that allows farmers to make decisions for adapting to both climate variability and change. The study aimed to calibrate and validate the AquaCrop model for perennial ryegrass (Lolium perenne L.) in the high tropics of Colombia (South America). The experiments were conducted during two consecutive seasons, in which perennial ryegrass meadows were subjected to two irrigation regimes: full irrigation and no irrigation. The model was evaluated using precision, accuracy, and simulation error indices. The overall performance of AquaCrop in simulating canopy cover, biomass and soil water content showed a good match between measured and simulated data. The calibration results indicated an acceptable measurement of simulated canopy cover (CC) (R² = 0.95, d-index = 0.41, RMSE = 9.4%, NRMSE = 12.2%, and FE = −21.72). The model satisfactorily simulated cumulative dry mass (R² = 0.95, d-index = 0.98, RMSE = 2. 63 t ha⁻¹, NRMSE = 11.8%, and FE = 0.94). Though the biomass values obtained in the end-of-season cuts were underestimated by the model, soil water content was simulated with reasonable accuracy (R² = 0.82, d-index = 0.84, RMSE = 6.10 mm, NRMSE = 4.80%, and FE = 0.32). During validation, CC simulations were good, except under water deficit conditions, where model performance was poor (R² = 0.42, d-index = 0.01, RMSE = 40.60%, NRMSE = 40.90%, and FE = −25.71); biomass and soil water content simulations were reasonably good. The above results confirmed AquaCrop’s (v 7.0) suitability for simulating responses to water for perennial ryegrass. A single crop file was developed for managing a full season and can be confidently applied to direct future research to improve the understanding of the necessary processes and interactions for the development of perennial herbaceous forage crops.

Climate variability is a major threat to maize (Zea mays) crops in Colombia, posing a risk to food security and compliance with Sustainable Development Goals. Therefore, this study aimed to assess the physical vulnerability of maize crops to climate variability in the semi-arid region of the Department of Cesar, Colombia, using the AquaCrop-OS model. Specifically, the study evaluated the vulnerability of maize crops to three typical meteorological years (dry, intermediate, and wet) adjusted to the growth cycle in two semesters of the year (periods I and II). An analysis of 43 years of data (1980–2022) revealed that most years in the area can be classified as intermediate, whereas the number of wet years was higher than the number of dry years. However, under the intermediate typical meteorological year scenario, maize cultivation in the department of Cesar experienced severe drought conditions during periods I and II. The study’s vulnerability curve showed an increasing rate within the yield loss index when the average water stress index was between 0.5 and 0.8 for period I. The rate of increase slowed when the index exceeded 0.9. For period II, the maize crop presented a lower degree of vulnerability, with 64% of the area experiencing a yield loss rate between 5% and 30%. The study also determined the required irrigation depth of water for optimal yields, which ranged from 70 to 160 mm during the growing season for all maize crop scenarios in the Department of Cesar. The results of this study can contribute to the consolidation of a database of physical vulnerability and threats of precipitation anomalies on regional and national scales. Overall, this study’s evaluation of the physical vulnerability of maize crops can help producers develop better strategies to mitigate the impacts of climate variability and ensure regional food security.

Sustainable agricultural development is one of the most important tools for the economic growth of a country. Therefore, water and land use management is considered a priority. This research aimed to develop a framework to optimize crops’ spatial and temporal distribution in an irrigation district. The AquaCrop- OS (FAO) water productivity model was integrated with a nonlinear optimization model to maximize the annual net profitability and minimize the water consumption of three crops (rice, corn, and forage). It was applied at a regional level to 905 simulation sub-units in the Zulia irrigation district (Colombia), in three typical climatic years’ scenarios, and at a multi-period level (monthly). The results indicated that: i) crop simulation for the study area was applicable and feasible; ii) rice can be combined with forage and corn; iii) corn is a viable option under dry year conditions; iv) under a wet year, forage production is the best option. On average, in the dry year, profitability decreased by 14.5% compared to the normal year in half of the study area, and in some areas, economic losses of up to 53% were obtained. In the wet year, profitability remained at the same level as the normal year in 43.8% of the area. However, there were significant decreases in profitability in 23.1% of the district. In the normal year, the water demand of the crops in each simulated period allows savings of up to 50% of water compared to the current concession amount, which is 1000 mm. This study is useful for making decisions on sustainable resources management and optimal irrigation water and land use under different biophysical and economic conditions.

Perennial ryegrass (Lolium perenne) is the predominant forage crop in the equatorial highland zones of Colombia due to its high nutritional value and versatility to produce both milk and meat. This study aimed to determine the relationship between the relative depletion of usable soil water and the Ks values of canopy expansion and closure stomatal of perennial ryegrass, as well as to identify the threshold values of water stress. The experiment was carried out in pots under a controlled environment condition. These pots were arranged in a completely randomized manner. The experiment consisted of five treatments—including control treatment—of water deficits in the soil that progressively increased the depletion level as the crop cycle developed. This generated a wide range of conditions in the growth stages. For each treatment, four repetitions were performed Biomass production was significantly affected by water stress. The results show that the upper and lower thresholds of Ks were 0.28 and 1.3 of the depletion level (p) of the total available water (TAW) in the soil for the expansion of the canopy (CE), and 0.25 and 1.1 p of the TAW for stomatal closure (gs). Quadratic functions were fitted for both the CE (R² = 0.72) and CS (R² = 0.73); moreover, the Ks function of FAO-AquaCrop with positive shape factor (sf) was as follows: sf = 11, RMSE 0.22 for CE, and sf = 4.3, RMSE 0.19 for gs. Our results indicate that ryegrass is moderately sensitive to water stress. The differences found between the Ks function of FAO and the experimental data call for the need to use modeling with parameters adapted for each case.

The perennial herbaceous forage crops’ (PHFC) biomass as bioindustry feedstock or source of nutrients for ruminants is very important from their final utilization point of view. In 2022, the AquaCrop-FAO version 7.0 model has been opened for PHFC. In this context, this study aimed to test for the first time the ability of the AquaCrop-FAO model to simulate canopy cover (CC), total available soil water (TAW), and biomass (B) of Guinea grass (Megathyrsus maximus cv. Agrosavia sabanera) under different water regimes at the Colombian dry Caribbean, South America. The water regimes included L1—irrigation based on 80% field capacity (FC), L2—irrigation based on 60% FC, L3—irrigation based on 50% FC, L4—irrigation based on 40% FC, L5—irrigation based on 20% FC, and L6—rainfed. The AquaCrop model uses the normalized water productivity—WP* (g m⁻²)—to estimate the attainable rate of crop growth under water limitation. The WP* for Guinea grass was 35.9 ± 0.42 g m⁻² with a high coefficient of determination (R² = 0.94). The model calibration results indicated the simulated CC was good (R² = 0.84, RMSE = 17.4%, NRMSE = 23.2%, EF = 0.63 and d = 0.91). In addition, cumulative biomass simulations were very good (R² = 1.0, RMSE = 5.13 t ha⁻¹, NRMSE = 8.0%, EF = 0.93 and d = 0.98), and TAW was good (R² = 0.85, RMSE = 5.4 mm, NRMSE = 7.0%, EF = 0.56 and d= 0.91). During validation, the CC simulations were moderately good for all water regimes (0.78 < R² < 0.97; 12.0% < RMSE < 17.5%; 15.9% < NRMSE < 28.0%; 0.47 < EF < 0.87; 0.82 < d < 0.97), the cumulative biomass was very good (0.99 < R² < 1.0; 0.77 t ha⁻¹ < RMSE < 3.15 t ha⁻¹; 2.5% < NRMSE < 21.9%; 0.92 < EF < 0.99; 0.97 < d < 1.0), and TAW was acceptable (0.70 < R² < 0.90; 5.8 mm < RMSE < 21.7 mm, 7.6% < NRMSE < 36.7%; 0.15 < EF < 0.58 and 0.79 < d < 0.9). The results of this study provide an important basis for future research, such as estimating biomass production of high-producing grasses in tropical environments, yield effects under scenarios of climate variability, and change based on the presented parameterization and considering a wide range of environments and grazing variations.