Agricultural Adaptation to Reconcile Food Security and Water Sustainability Under Climate Change:The Case of Cereals in Iran

In this study, we simulate the crop yield and water footprint (WF) of major food crops of Iran on irrigated and rainfed croplands for the historical and the future climate. We assesse the effects of three agricultural adaptation strategies to climate change in terms of potential blue water savings. We then evaluate to what extent these savings can reduce unsustainable blue WF. We find that cereal production increases under climate change in both irrigated and rainfed croplands (by 2.6-3.1 and 1.4-2.3 million t y-1, respectively) due to increased yields (6.6%-78.7%). Simultaneously, the unit WF (m3 t-1) tends to decrease in most scenarios. However, the annual consumptive water use increases in both irrigated and rainfed croplands (by 0.3-1.8 and 0.5-1.7 billion m3 y-1, respectively). This is most noticeable in the arid regions, where consumptive water use increases by roughly 70% under climate change. Off-season cultivation is the most effective adaptation strategy to alleviate additional pressure on blue water resources, with blue water savings of 14-15 billion m3 y-1. The second most effective is WF benchmarking, which results in blue water savings of 1.1-3.5 billion m3 y-1. The early planting strategy is less effective, but still leads to blue water savings of 1.7-1.9 billion m3 y-1. In the same order of effectiveness, these three strategies can reduce blue water scarcity and unsustainable blue water use in Iran under current conditions. However, we find that these strategies do not mitigate water scarcity in all provinces per se, nor all months of the year

Groundwater saving and quality improvement by reducing water footprints of crops to benchmarks levels

The formulation of water footprint (WF) benchmarks in crop production – i.e. identifying reference levels of reasonable amounts of water consumption and pollution per tonne of crop produced – has been suggested as a promising strategy to counter inefficient water use and pollution. The current study is the first to show how setting WF benchmarks may help alleviate groundwater scarcity and pollution, in a case study for Iran. We advance the field of WF assessment by developing WF benchmark levels for crop production, which we successively use to assess potential groundwater saving, quality improvement and economic water productivity gains. First, we calculate climate-specific WF benchmark levels for both total blue water footprints and nitrogen-related grey groundwater footprints for 26 crops, for all years in the period 1980–2010, at 5 × 5′ spatial resolution. Second, we estimate the water saving potential for total blue water resources and for groundwater resources specifically, as well as the grey groundwater footprint reduction potential. Finally, we compare mean economic water productivities of crop production in the past with productivities if WFs are reduced to benchmark levels. We find that groundwater comprises up to 83% of total blue water consumption of irrigated crops, with the highest share in arid areas and in cereals. Aquifers are under significant to severe stress, except in the dry sub-humid zone, where irrigation mainly relies on surface water. Reducing WFs of crops to 25th percentile benchmark levels can save 32% of groundwater compared to the reference year 2010, and lower the nitrogen-related grey groundwater footprint by 23%. Moreover, it would increase average economic groundwater productivity in Iran by 20% for cereals, and 59% for nuts. We conclude that reducing WFs to climate-specific benchmark levels in a water-stressed country is a promising way to alleviate overexploitation of aquifers and increase national food security

Arjen Y. Hoekstra: A Water Management Researcher to Be Remembered

On 18 November 2019, the life of Arjen Y [...

Two-dimensional modeling of nitrogen and water dynamics for various N-managed water-saving irrigation strategies using HYDRUS

Nitrate losses are the dominant cause of the non-point source pollution under agricultural fields. In this study, the HYDRUS-2D model was first calibrated and validated using data collected during a two-year field investigation in a drip-irrigated maize field and then applied to evaluate the influence of 176 different N-managed water-saving irrigation scenarios on water and N dynamics and maize grain yield. Various scenarios were defined by combining 11 irrigation levels (IL=0–100% with a 10% interval), 8N fertilization rates (NR=0–400kgha−1 with a 50kgha−1 interval) and two water-saving irrigation strategies: deficit irrigation (DI) and partial root-zone drying (PRD). Reliable estimates of soil NO3−-N concentrations (RMSE=0.39–10.9mgl−1 and MBE=−8.9–8.4mgl−1), crop N uptake (RMSE=3.9–8.9kgha−1 and MBE=−5.3–6.25kgha−1), and soil water contents (RMSE=2.3–5.11mm and MBE=1.63–4.93mm) were provided by HYDRUS-2D. Based on the simulated results, the fertigation strategy with NR=200kgha−1 is an optimum strategy. For the higher fertigation rates (NR≥250kgha−1), the NO3−-N leaching out of the surface layers (0–20cm) increased by 0.1–183% while N uptake was enhanced by only 0.3–15%. On the other hand, reducing NR below this level would have resulted in severe economic losses. A 30% reduction in IL at NR=200kgha−1 shows an enormous potential in lowering N leaching below different soil layers (12–99%) while reducing crop N uptake by only 5.4%. In addition, higher crop yield by 0.2–20.2% can be expected under PRD since crop N uptake is enhanced by more water available in the surface layers. While on the one hand, PRD ensures environmentally safer fertilizer applications, on the other hand, the economic objectives are met more easily under PRD than under DI. Additionally, it could be concluded that the HYDRUS-2D model, instead of labor- and time-consuming and expensive field investigations, could be reliably used for determining the optimal scenarios under both the DI and PRD strategies

A comparison of numerical and machine-learning modeling of soil water content with limited input data

Soil water content (SWC) is a key factor in optimizing the usage of water resources in agriculture since it provides information to make an accurate estimation of crop water demand. Methods for predicting SWC that have simple data requirements are needed to achieve an optimal irrigation schedule, especially for various water-saving irrigation strategies that are required to resolve both food and water security issues under conditions of water shortages. Thus, a two-year field investigation was carried out to provide a dataset to compare the effectiveness of HYDRUS-2D, a physically-based numerical model, with various machine-learning models, including Multiple Linear Regressions (MLR), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and Support Vector Machines (SVM), for simulating time series of SWC data under water stress conditions. SWC was monitored using TDRs during the maize growing seasons of 2010 and 2011. Eight combinations of six, simple, independent parameters, including pan evaporation and average air temperature as atmospheric parameters, cumulative growth degree days (cGDD) and crop coefficient (Kc) as crop factors, and water deficit (WD) and irrigation depth (In) as crop stress factors, were adopted for the estimation of SWCs in the machine-learning models. Having Root Mean Square Errors (RMSE) in the range of 0.54–2.07 mm, HYDRUS-2D ranked first for the SWC estimation, while the ANFIS and SVM models with input datasets of cGDD, Kc, WD and In ranked next with RMSEs ranging from 1.27 to 1.9 mm and mean bias errors of −0.07 to 0.27 mm, respectively. However, the MLR models did not perform well for SWC forecasting, mainly due to non-linear changes of SWCs under the irrigation process. The results demonstrated that despite requiring only simple input data, the ANFIS and SVM models could be favorably used for SWC predictions under water stress conditions, especially when there is a lack of data. However, process-based numerical models are undoubtedly a better choice for predicting SWCs with lower uncertainties when required data are available, and thus for designing water saving strategies for agriculture and for other environmental applications requiring estimates of SWCs

Controversies in Obesity Treatment

The markedly high prevalence of obesity contributes to the increased incidence of chronic diseases, such as diabetes, hypertension, sleep apnea, and heart disease. Because of high prevalence of obesity in almost all countries, it has been the focus of many researches throughout the world during the recent decades. Along with increasing researches, new concepts and controversies have been emerged. The existing controversies on the topic are so deep that some researches argue on absolutely philosophical questions such as “Is obesity a disease?” or “Is it correct to treat obesity?” These questions are based on a few theories and real data that explain obesity as a biological adaptation and also the final results of weight loss programs. Many people attempt to lose weight by diet therapy, physical activity and lifestyle modifications. Importantly, weight loss strategies in the long term are ineffective and may have unintended consequences including decreasing energy expenditure, complicated appetite control, eating disorders, reducing self-esteem, increasing the plasma and tissue levels of persistent organic pollutants that promote metabolic complications, and consequently, higher risk of repeated cycles of weight loss and weight regain. In this review, major paradoxes and controversies on obesity including classic obesity paradox, pre-obesity; fat-but-fit theory, and healthy obesity are explained. In addition, the relevant strategies like “Health at Every Size” that emphasize on promotion of global health behaviors rather than weight loss programs are explained

A field-modeling study for assessing temporal variations of soil-water-crop interactions under water-saving irrigation strategies

Simulation models are useful tools that may help to improve our understanding of soil-water-plant interactions under innovative water-saving irrigation strategies. In this study, the HYDRUS-2D model was applied to evaluate the influence of deficit irrigation (DI) and partial root-zone drying (PRD) on maize water extractions during two cropping cycles of 2010 and 2011. The model was calibrated and validated using measured soil water content data (expressed as equivalent water depths). Reliable estimates of soil water content were provided by HYDRUS-2D, with root mean square error and mean bias error values of 2.3–5.11 and 1.63–4.93mm, respectively. Root water uptake and maize grain yields were reduced by 13.2–28.8% and 13.6–52.8%, respectively, under different water-saving irrigation treatments compared to full irrigation. However, different root and water repartitions in the PRD treatment with a 25% reduction in the irrigation depth (PRD75) improved soil water utilization and consequently, crop growth. Increased root water uptake (2.2–4.4 times higher than in other treatments) from the 60–100cm soil depth in the PRD75 treatment maintained a favorable daily evapotranspiration rate, resulting in no significant reduction in maize grain yield compared to full irrigation. Consequently, a 15.7–85% increase in water use efficiency for maize cultivation under PRD75 ensured 25% water savings without threatening food security in the study area. It can be concluded that HYDRUS-2D can be successfully used to optimize water management under local water-stress conditions

An application of the water footprint assessment to optimize production of crops irrigated with saline water: A scenario assessment with HYDRUS

Agriculture, due to a growing scarcity of fresh water resources, often uses low-quality waters for irrigation, such as saline waters. However, unmanaged applications of such waters may have negative environmental and economic consequences. Based on the concept of the water footprint (WF), a measure of the consumptive and degradative water use, the field-calibrated and validated HYDRUS (2D/3D) model was applied to find optimal management scenarios (from 1980 different evaluated scenarios). These scenarios were defined as a combination of different salinity rates (SR), irrigation levels (IL, the ratio of an actual irrigation water deth and a full irrigation water depth), nitrogen fertilization rates (NR), and two water-saving irrigation strategies, deficit irrigation (DI) and partial root-zone drying (PRD). The consumptive WF was defined as the crop water consumption divided by the crop yield. The grey WF was calculated for the N fertilizer and defined as the volume of freshwater required to dilute nitrogen (N) in recharge so as to meet ambient water quality standards. Simulated components of water and solute dynamics were used to calculate criteria indices, which were divided into two groups: (a) environmental indices, including the degradative grey water footprint (GWF) and the apparent N recovery rate efficiency (ARE), and (b) economic indices, including economic water (EWP) and land (ELP) productivities. While significant improvements of 3.9–59.2%, 0.1–165.8%, and 0.01–166.5% in ARE, EWP, and ELP, respectively, were obtained when NR varied within the range of 0–200 kg ha−1, changes in these indices were relatively minor when NR was higher than 200 kg ha−1. At a given NR, GWF tends to increase considerably by up to 180% when DI-crops are subject to low-intermediate salt (SR < 7 dS m−1) and water (IL > 70%) stresses. This is at the expense of up to a 55% reduction in ELP and up to a 120% increase in EWP. With N uptake 0.2–17.3% higher, PRD seems to be a more viable agro-hydrological option than DI in reducing a pollutant load into regional aquifers as well as in sustaining farm economics. The entire analysis reveals that the PRD strategy with N-fertilization rates of 100-200 kg ha−1, a moderate salinity stress (SR < 5 dS m−1), and irrigation levels of 60–90% represents the best management scenario. It can be concluded that, while there is a substantial need for rescheduling irrigation and fertilization managements when crops are irrigated with saline waters, HYDRUS modeling may be a reliable alternative to extensive field investigations when determining the optimal agricultural management practices