Search Results (5)

Within the prevalent challenges posed by climate change and decreasing resources, this research underscores the importance of adopting sustainable agricultural practices combined with efficient water resource management. Employing comprehensive climate and soil suitability analyses, this research analyzed the capacity of hemp (Cannabis sativa L.) to adapt to Lebanon’s heterogeneous environmental landscapes across two distinct growing seasons (autumn and spring). Both climate and edaphic suitability mapping were conducted to study hemp’s suitability. AquaCrop v.7.1 was used to simulate seed yield, biomass production, irrigation needs and yield water productivity in the different agro-homogeneous zones of Lebanon for the two considered seasons. The findings revealed that approximately 30% and 19% of Lebanon’s land exhibit suitability for hemp cultivation during the spring and autumn seasons, respectively. According to AquaCrop model simulations, under the prevailing climatic conditions, the predicted seed yield will range from 3.7 to 5.6 t ha⁻¹ under rainfed conditions and will reach 11.1 t ha⁻¹ for irrigated cultivation. Moreover, employing efficient irrigation techniques during the spring season showed a significant improvement in both yield and biomass of hemp. The enhancement was evident, with notable increases of 112.22% in yield and 96.43% in biomass compared to rainfed conditions. This research highlights the importance of identifying suitable regions within Lebanon capable of supporting hemp cultivation in a sustainable manner. Such research not only promises economic development but also aligns with broader global sustainability objectives. Full article

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. Full article

With the impacts of climate change and human activities, agricultural drought disaster losses have increased remarkably. Drought disaster risk assessment is a prerequisite for formulating disaster reduction strategies and ensuring food security. However, drought disaster risk is a complex system, and quantitative assessment methods reflecting the risk formation mechanism are still rarely reported. This study presented a chain transmission system structure of drought disaster risk, which meant that drought disaster loss risk R was derived from drought hazard H by the transformation of drought disaster vulnerability V. Based on this point, firstly, a drought hazard curve between drought intensity and drought frequency was established using remote sensing data and the copula function. Then, a crop loss calculation approach under various drought events and drought resistance capacity scenarios was achieved by two-season field experiments and the AquaCrop model. Finally, a loss risk curve cluster of “drought frequency–drought resistance capacity–yield loss rate” was proposed by the composition of the above two quantitative relationships. The results of the case study for summer maize in Bengbu City indicated that the average yield loss rate under 19 droughts occurring during the growth period of maize from 1982 to 2017 was 24.51%. High risk happened in 1988, 1992, 1994, 2001, and 2004, with the largest loss rate in 2001, up to 65.58%. Overall, droughts with a return period less than two years occurred frequently during the growth period of summer maize in Bengbu, though the loss risk was still controllable. In conclusion, the results suggest that the loss risk curve provides a new effective method of drought disaster risk quantitative assessment from a physical mechanism perspective, which lays a scientific foundation for decision-making in risk management. Full article

Northeast China is China’s primary grain production base. A large amount of crop straw is incinerated every spring and autumn, which greatly impacts air quality. To study the degree of influence of straw burning on urban pollutant concentrations, this study used The Moderate-Resolution Imaging Spectroradiometer/Terra Thermal Anomalies & Fire Daily L3 Global 1 km V006 (MOD14A1) and The Moderate-Resolution Imaging Spectroradiometer/Aqua Thermal Anomalies and Fire Daily L3 Global 1 km V006 (MYD14A1) data from 2015 to 2017 to extract fire spot data on arable land burning and to study the spatial distribution characteristics of straw burning on urban pollutant concentrations, temporal variation characteristics and impact thresholds. The results show that straw burning in Northeast China is concentrated in spring and autumn; the seasonal spatial distributions of PM_2.5, PM₁₀ andAir Quality Index (AQI) in 41 cities or regions in Northeast China correspond to the seasonal variation of fire spots; and pollutants appear in the peak periods of fire spots. In areas where the concentration coefficient of rice or corn is greater than 1, the number of fire spots has a strong correlation with the urban pollution index. The correlation coefficient R between the number of burned fire spots and the pollutant concentration has a certain relationship with the urban distribution. Cities are aggregated in geospatial space with different R values. Full article

Crop growth and yield are affected by water use during the season: the green water footprint (WF) accounts for rain water, the blue WF for irrigation and the grey WF for diluting agri-chemicals. We calibrated crop yield for FAO’s water balance model “Aquacrop” at field level. We collected weather, soil and crop inputs for 45 locations for the period 1992–2012. Calibrated model runs were conducted for wheat, barley, grain maize, oilseed rape, potato and sugar beet. The WF of cereals could be up to 20 times larger than the WF of tuber and root crops; the largest share was attributed to the green WF. The green and blue WF compared favourably with global benchmark values (R² = 0.64–0.80; d = 0.91–0.95). The variability in the WF of arable crops across different regions in Europe is mainly due to variability in crop yield ( $\overline{cv}$ = 45%) and to a lesser extent to variability in crop water use ( $\overline{cv}$ = 21%). The WF variability between countries ( $\overline{cv}$ = 14%) is lower than the variability between seasons ( $\overline{cv}$ = 22%) and between crops ( $\overline{cv}$ = 46%). Though modelled yields increased up to 50% under sprinkler irrigation, the water footprint still increased between 1% and 25%. Confronted with drainage and runoff, the grey WF tended to overestimate the contribution of nitrogen to the surface and groundwater. The results showed that the water footprint provides a measurable indicator that may support European water governance. Full article