Agricultural Water Demand Research Articles

An Optimization Scheme of Water Resources Allocation Based on the Particle Swarm Algorithm

Climate change will exacerbate water scarcity, as warmer temperatures will increase evaporation from soil and water surfaces, raising crop water requirements, and consumers’ demand for energy will increase at higher temperatures, with a concomitant increase in demand for cooling water. Therefore, water scarcity has evolved into a global issue, most notably in the Colorado Basin. As the climate changes, the future of the Colorado River becomes even more tragic. The river’s water levels are dropping year after year, while the water needs of the surrounding urban development are increasing, in what seems to be an endless cycle. At the same time, the entire southwestern United States may be in an unprecedented resource crisis as a result. Therefore, we have modeled water supply, demand, and allocation scenarios and constructed a water allocation model using the Colorado Basin water allocation as an example, combining Lake Mead, Lake Powell, and the agricultural, industrial, and residential demand for water and electricity in five states. To better achieve the optimal balance between the economy and the environment, we set three objectives: economic efficiency, regional surplus water, and total regional electricity generation, and solved the problem using a particle swarm algorithm. By solving the Colorado Basin water allocation problem, we explore a feasible solution with high generalization capability that can be applied in the future to scenarios of rational water allocation in multiple regions around the world.

Governance issues around agricultural land use and water demand for irrigation in the Vinces River basin (Ecuador)

Governance issues around agricultural land use and water demand for irrigation in the Vinces River basin (Ecuador)

The perspective of a smart city by endorsing the nexus Bermuda triangle with the risk assessment of polluted water reuse in integrated water and food security management: the case of Semnan, Iran

AbstractLocated in a semi-arid and arid zone, Iran is suffering from growing challenges of water scarcity. In the paradigm of a circular economy, the reuse of treated wastewater in agriculture is currently regarded as a possible solution for alleviating the issues of water scarcity and pollution. Accordingly, this research aims to assess the use of polluted water in the integrated management of water resources in Semnan. The research used the Water Evaluation and Planning System (WEAP) software package to model and analyze the water sector. Also, the Bayesian network method was used to assess the risk of using polluted water and its effects on humans and plants. The research explored two general scenarios for the study site of Semnan. The first scenario assumes the increase in population, crops (food), and industries, and the second has the same assumptions plus an increase in agricultural efficiency (food production). Based on the results, the agricultural, urban, and industrial water demands are 37, 0.06, and 0.01 million m3 in the base year, respectively. The water demand in the next years will be higher due to population growth. Finally, it is safer to use the wastewater of both treatment plants of the region (Mehdishahr and Semnan) in the industry than in other sectors. Additionally, the wastewater of the Mehdishahr Sewage Treatment Plant is more reliable than that of the Semnan Sewage Treatment Plant.

Alternative Furrow Irrigation Effect on Onion (Allium cepa L.) Yield and Water Use Efficiency: Evidence from Ethiopia

ABSTRACT Agricultural water demand is increasing with inefficient delivery mechanisms in Ethiopia. This experiment was conducted to investigate the effect of alternative furrow irrigation against other irrigation systems on onion crop (Allium cepa L.) yield and water use efficiency in Basketo special district. The experiment comprised of four levels of treatments (alternative furrow irrigation, fixed furrow irrigation, farmers practice) and 50% of alternative furrow irrigation. The analysis of variance for the combined means showed that alternative furrow gave the highest mean total yield (25.32 ton ha−1) and marketable yield (17.35 ton ha−1) without significantly varying farmer practices and fixed, while 50% alternative furrow gave the minimum total yield (21.23 ton ha−1) and marketable yield (10.35 ton ha−1). Alternative and 50% alternative showed 8% of yield and 40% of marketable yield difference. Alternative furrow irrigation also gave higher water use efficiency than others and 50% of alternative furrow irrigation saved higher water (60%) and other saved less. Economic profitability analysis showed that alternative furrow irrigation had given the highest gross field Benet and benefit cost ratio and 50% of alternative furrow irrigation gave the least of both parameters. Alternative furrow irrigation can be appropriate for better onion yielding and water saving in areas where there is water scarcity.

Factors Influencing Seasonal Changes in Inundation of the Daliyaboyi Oasis, Lower Keriya River Valley, Central Tarim Basin, China

The ecological water diversion project (EWDP) in the Tarim River Basin, China, aims to allocate more surface water to downstream reaches to restore the degraded ecosystems. However, seasonal changes in ecological water diversion; the factors (natural and anthropogenic) controlling the ecological water diversion, whether the seasonal delivery of water temporally corresponded to the vegetation’s seasonal water demands; and the benefits of the ecological water diversion through overflowing surface water irrigation are unclear. To address the above issues, this study examines the intra-annual changes and its influencing factors in ecological water diversion (inundation) in the Daliyaboyi Oasis in the lower Keriya River valley within the Tarim Basin, discusses whether the seasonal delivery of water temporally corresponded to the vegetation’s seasonal water demands, and assesses the ecological benefits of overflowing surface water irrigation. Inundation was quantified by digitizing monthly changes in the inundated area from 2000 to 2018 in the oasis using 184 Landsat images. The results demonstrate that seasonal changes in the inundated area varied significantly, with maximum peaks occurring in February and August; a period of minimal inundation occurred in May. Differences in the July/August peak (i.e., July or August) in inundation dominated the inter-annual variations in the inundated area over the 19-year study period. The two peaks in the inundation area were temporally consistent with the vegetation’s seasonal water demand. Local residents have used ecological water to irrigate vegetation in different parts of the oasis during different seasons, an approach that expanded the inundated area. The February peak in the inundated area is closely linked to elevated downstream groundwater levels and the melting of ice along the river. The August peak is related to a peak in runoff from headwater areas. The minimum May value is correlated to a relatively low value in upstream runoff and an increase in agricultural water demand. Thus, natural factors control the intra-annual and inter-annual variations in the inundated area. Humans changed the spatial distribution of the inundated area and enhanced the water’s ecological benefits, but did not alter the correlation between peak periods of inundation and vegetation water demand. The results from this study improve our understanding of the benefits of the EWDP in the Tarim River Basin.

Assessment of groundwater at Kurukshetra district

Abstract Mankind depends on surface water and groundwater resources to meet basic requirements. Groundwater is a limited resource that can be replenished. Instead of surface water, groundwater can be an alternate supply to meet a region's water demand for household, agriculture, and industrial uses. As a result, a thorough assessment is required to ensure the resource's long-term viability. The main aim of the research is to identify potential zones using remote sensing and quantification of groundwater resources. In the present study, the potential zones were identified by using the weighted overlay technique in ArcGIS software by considering eight influencing factors, and the estimation of groundwater was carried out using GEC 2015 methodology. The study discovered that the study area has a good potentiality of groundwater in the southern region. Further estimates were made for the period April 2020–March 2021 by taking into account recharge by rainfall, irrigation return flow, seepage by canals, and draft by industries, domestic, irrigation, and evapotranspiration. The research reveals that the net groundwater recharge of 0.16284361 million ha-m was depleted in the study area. This analysis concludes that the groundwater in the study area is critical and becoming an overexploited zone.

Increasing groundwater storage and maintaining irrigation through managed aquifer recharge

Aquifers providing groundwater for agriculture are in many cases unsustainably managed, resulting in environmental degradation and socioeconomic impacts. In this context, strategies that contribute to recovering groundwater reserves and satisfactorily maintaining irrigation are essential. Los Arenales Aquifer (Spain) experienced a dramatic decline in groundwater level in the last quarter of the 20th century due to intensive agricultural abstractions. We assess whether this aquifer has recovered in recent years and the potential contribution of managed aquifer recharge (MAR). We evaluate the significance of groundwater level trends and their magnitude through the (regional) Mann-Kendall test and the Theil-Sen estimator. We also calculate the average groundwater level and explore factors that could influence the observed trends, such as MAR, land use, agricultural water demand, and water management measures. We also contrast two groundwater management regions located within Los Arenales Aquifer, namely Los Arenales (LA), which has implemented MAR, and Medina del Campo (MC), to obtain further insight into groundwater development. The hydro statistical analyses reveal a continuous drop in groundwater levels of about −1.3 m/year in LA between 1985 and 2001, followed by a consistent recovery. In MC, groundwater levels decrease in two out of three analysis periods. We show that MAR is spatially and temporally correlated with increasing groundwater level trends. Land use, agricultural water demand, and water management measures have remained similar throughout the analysis period or have a limited impact on groundwater use. Furthermore, the only significant water management measure MC differs from LA is MAR. These pieces of evidence point to MAR as the primary mechanism for replenishing Los Arenales Aquifer, showing the potential of this tool to recover stressed aquifers and sustain irrigation in the area and beyond.

Assessment of Agricultural Water Sufficiency under Climate and Land Use Changes in the Lam Takong River Basin

To narrow the gap of agricultural water insufficiency in the Lam Takong River Basin, Thailand, we conducted an assessment of water availability and agricultural water demand under climate and land use changes. The water availability was estimated by SWAT, which was calibrated and validated during 2008–2012 and 2013–2018 against the observed daily discharge at the M.164 station. Measured and simulated discharges showed good agreement during calibration and validation, as indicated by values of 0.75 and 0.69 for R2 and 0.74 and 0.63 for Nash–Sutcliffe Efficiency, respectively. The results of GCMs (IPSL-CM5-MR, NorESM1-M, and CanESM2) under RCPs 4.5 and 8.5 were calculated to investigate changes in rainfall and temperature during 2020–2099. The warming tendencies of future maximum and minimum temperatures were projected as 0.018 and 0.022 °C/year and 0.038 and 0.045 °C/year under RCPs 4.5 and 8.5, respectively. The future rainfall was found to increase by 0.34 and 1.06 mm/year under RCPs 4.5 and 8.5, respectively. As compared to the 2017 baseline, the future planted areas of rice, maize, and cassava were projected to decrease during 2020–2099, while the sugarcane plantation area was expected to increase until 2079 and then decline. The top three greatest increases in future land use area were identified as residential and built-up land (in 2099), water bodies (in 2099), and other agricultural land (in 2059), while the three largest decrease rates were paddy fields (in 2099), forest land (in 2099), and orchards (during 2059–2079). Under the increased reservoir storage and future climate and land use changes, the maximum and minimum increases in annual discharge were 1.4 (RCP 8.5) and 0.1 million m3 (RCP 4.5) during 2060–2079. The sugarcane water demand calculated by CROPWAT was solely projected to increase from baseline to 2099 under RCP 4.5, while the increase for sugarcane and cassava was found for RCP 8.5. The future unmet water demand was found to increase under RCPs 4.5 and 8.5, and the highest deficits would take place in June and March during 2020–2039 and 2040–2099, respectively. In this context, it is remarkable that the obtained results are able to capture the continued and growing imbalance between water supply and agricultural demand exacerbated by future climatic and anthropogenic land use changes. This research contributes new insight for compiling a comprehensive set of actions to effectively build resilience and ensure future water sufficiency in the Lam Takong River Basin.

Optimized water allocation in persistent severe climatic conditions: A novel metaheuristic approach

With increasing global water tensions, the need for a comprehensive water management framework in persistent harsh conditions is crucial. A framework was designed to devise a comprehensive and detailed plan that can strike a balance between all water resources and demands. In contrast to previous studies, this framework can be adopted for long-lasting severe climatic conditions and suggests detailed cultivation patterns and reservoir management. The framework adopts a comprehensive, detailed, novel, multi-objective, and evolutionary-based approach for optimal water management during continuous extreme events in sectors including agriculture, industry, and the environment. Sustainability (ecological water demand and global warming potential), economic welfare, and equitability are allocation principles. This framework utilizes complex, metaheuristic algorithms that enable it to cope with big data and optimize complex systems with high precision. For the case study of the Karkheh basin, the results show that for a full recovery of the Hoor al-Azim wetland, the basin cannot bear the agricultural water demand, and such activities must be decreased drastically. It is also shown that a 10% increase of the agricultural net benefit will result in a 40% increase of the Gini coefficient.

Short- and mid-term forecasts of actual evapotranspiration with deep learning

• Deep learning is applied for nowcasting and forecasting of evapotranspiration. • Weather variables and soil moisture are used as input for the deep learning models. • The models were applied to seven distinct climate zones within the continental U.S. • Results reveal great potential for evapotranspiration forecasting. Evapotranspiration is a key component of the hydrologic cycle. Accurate short-, medium-, and long-term forecasts of actual evapotranspiration (ET a ) are crucial not only for quantifying the impacts of climate change on the water and energy balance, but also for real-time estimation of crop water demand and irrigation water allocation in agriculture. Despite considerable advances in satellite remote sensing technology and the availability of long ground-measured and remotely sensed ET a timeseries, real-time ET a forecasts are deficient. Applying a state-of-the-art deep learning (DL) approach, Long Short-Term Memory (LSTM) models were employed to nowcast (real-time) and forecast (ahead of time) ET a based on (1) major meteorological and ground-measured (i.e., soil moisture) input variables and (2) long ET a timeseries from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard of the NASA Aqua satellite. The conventional LSTM and convolutional LSTM (ConvLSTM) DL models were evaluated for seven distinct climatic zones across the contiguous United States. The employed LSTM and ConvLSTM models were trained and evaluated with data from the National Climate Assessment-Land Data Assimilation System (NCA-LDAS) and with MODIS/Aqua Net Evapotranspiration MYD16A2 product data. The obtained results indicate that when major atmospheric and soil moisture input variables are used for the conventional LSTM models, they yield accurate daily ET a forecasts for short (1, 3, and 7 days) and medium (30 days) time scales, with normalized root mean squared errors (NRMSE) and Nash-Sutcliffe efficiencies (NSE) of less than 10% and greater than 0.77, respectively. At the watershed scale, the univariate ConvLSTM models yielded accurate weekly spatiotemporal ET a forecasts (mean NRMSE less than 6.4% and NSE greater than 0.66) with higher computational efficiency for various climatic conditions. The employed models enable precise forecasts of both the current and future states of ET a , which is crucial for understanding the impact of climate change on rapidly depleting water resources.

Expanding the Irrigated Areas in the MENA and Central Asia: Challenges or Opportunities?

Middle Eastern, North African countries (MENA), and Central Asian countries are considered the countries most facing water and food scarcity. The current water exploitation indicates that a few countries are overexploiting their water resources and using the fossil water available. This study reviews each country’s renewable water resources volume and evaluates the resources available to expand the agricultural area. Different scenarios are considered, using both irrigated and rainfed farming options, for concluding the most sustainable farming method in each country. Different scenarios are considered using irrigated and rainfed farming options to recommend the most sustainable farming method for each country. Results show that the countries in the MENA and Central Asia can be divided into three main categories: (1) Countries whose expansion of agricultural area can only be applied by using fossil water resources (Bahrain, Egypt, Kuwait, Libya, Qatar, Saudi Arabia, Turkmenistan, United Arab Emirates, and Uzbekistan); (2) Countries where the agricultural area can be expanded to a certain limit, by sustainably using both irrigated and rainfed farming (Afghanistan, Algeria, Iran, Palestine, Jordan, Kazakhstan, Morocco, Oman, Syria, Tajikistan, Tunisia, and Yemen); (3) Countries that have enough renewable water resources to farm all their agricultural area (Lebanon, Iraq, Turkey, and Kyrgyzstan). However, the aim of this study and its results are only to assess the renewable water resources available to sustain the increased agricultural water demand by setting aside other agricultural factors that constrain the sector.

Migration to solar water pump system: Environmental and economic benefits and their optimization using genetic algorithm Based MPPT

The amount of rainfall in Meknes region in Morocco is insufficient to meet agricultural water demand. Pumping methods based on diesel and butane are thus an unavoidable option for local farmers. Nonetheless, the region receives an average of 4.72 kW.m−2.day−1 of solar irradiation per day, thus solar pumping systems can cover irrigation needs while also provide other benefits such as lowering greenhouse gas emissions, minimizing energy expenditures, and aiding in environmental preservation. Farmers in the Meknes region, on the other hand, are hesitant to employ photovoltaic energy to power their equipment. This work has two aims: the first is to explore the solar energy resource in the region. This is to analyze the capacity of the energy resource to adequately meet the energy needs of solar water pumping systems in the region. Secondly, based on the coupling theory, a comparative study was conducted. The objective was to evaluate the two methods of operation of solar systems, namely direct coupling and coupling with a DC–DC adapter with a genetic algorithm. The findings of this study reveal that with this adaptation stage, this solution doubles energy efficiency.

Causal network inference in a dam system and its implications on feature selection for machine learning forecasting

A fundamental goal across many research fields is to explain possible mechanisms behind a phenomenon and infer the correct causal relationships between variables. In this work, we employed various causal inference methods to derive the causal network of a dam system from time series data. Here we explored the lagged effects of water levels in two dams, climate and weather variables, and domestic and agricultural water demands on each other. Among the methods considered, we demonstrated that convergent cross mapping (CCM), a method for inferring causal relationships in complex systems using time series data, is the most consistent with an actual dam system: (1) causal links were consistent with the direction of the physical flow of water, (2) the effects of climate and weather variables were successfully captured, (3) the time lags shed light on the dynamics of the dam system and possibly reflected planning schedules which are not explicit in the data. Our results captured both intuitive and counter-intuitive causal links, some of which were validated by domain experts. Using the resulting causal links to pre-select the input variables in machine learning-based forecasting models significantly reduces the prediction errors compared to using randomly selected features. Specifically, the best reduction in MAE is 4.2–4.4 meters, which corresponds to an improvement of 2.8–3.0 times lower than using random selection of features. CCM was also able to filter the top 20 significant predictors, where further addition of other variables yielded negligible improvement in the MAE. This is the first work that demonstrates successful inference of time-lagged causal network of endogenous and exogenous variables in a dam system.

Application of EPA-SWMM with Modulated Controls for Agricultural Water Balance Analysis

Due to the recent spring drought, it has been difficult to supply agricultural water owing to an insufficient water supply before irrigation, thereby illuminating the importance of efficient water management for allocating optimal agricultural water amounts. Thus, improved management plans for irrigation facilities are required as well as an optimal water management plan for agricultural water supplies and equitable distribution. In this study, we applied modulated controls to the EPA-SWMM and analyzed the agricultural water supply and demand. The EPA-SWMM simulates the hydrological water supply and can be applied to a water supply network. Modulated controls can simulate continuous, intermittent, and rotational irrigation methods by setting the supply time series, and adjusting the water supply and drainage based on ponding depth conditions in the paddy field. We applied the EPA-SWMM with modulated controls to Gosam reservoir in Anseong to analyze the agricultural water supply and irrigation efficiency. The water supply via canals and irrigated areas was analyzed to identify areas of water shortages or oversupply. According to the results of a quantitative water supply analysis, we determined that it is necessary to control the water supply using gate opening rate controls. The results obtained herein can contribute to efficient water management by evaluating the efficiency of irrigation organizations during a drought when insufficient agricultural water is available via preliminary analysis of agricultural water supply methods.

Performance Evaluation of Artificial Recharge–Water Intake System Using 3D Numerical Modeling

In this study, 3D detailed numerical modeling was performed to evaluate the performance of an artificial recharge–water intake system installed to secure agricultural water in drought areas. Using a 3D irregular finite element grid, a conceptual model was constructed that reflected the actual scale of the study area and artificial recharge–water intake system and considered the characteristics of saturated–unsaturated aquifers. The optimal design factors for the artificial recharge system were derived through the constructed conceptual model, and were reflected to evaluate the individual performance of the artificial recharge and water intake system in the study area. Finally, an optimal operating scenario for the artificial recharge and water intake system was developed. The operation scenarios were composed of an appropriate injection rate and water withdrawal for each period from March, when the demand for agricultural water was low, to June, when the dry season and farming season overlapped, considering the target water withdrawal amount (30,000 tons) of the region, derived from water budget analysis. The proposed results are expected to be very useful in the future for the efficient operation and management of artificial recharge–water intake systems installed in drought areas.

Quantifying the impacts of agricultural alteration and climate change on the water cycle dynamics in a headwater catchment of Lake Urmia Basin

The rapid shrinkage of Lake Urmia over the past two decades has raised serious environmental concerns. Several restoration plans have been proposed to reduce agricultural water consumption to supply a remarkable fraction of the lake’s environmental flow requirement. However, an accurate and realistic evaluation of the effectiveness of these plans in reducing agricultural water consumption across the basin is still poorly understood. This study assessed the water-saving potential of agricultural alteration in a snow-dominated catchment in the Lake Urmia Basin. In particular, we explored the impact of crop pattern alteration via reducing the cultivation area and employing different irrigation systems. We further studied the degree of extra stress on the agricultural water demand under the future trends of air temperature and precipitation due to climate change. To this end, we developed a three-dimensional physically-based hydrological model that simulated the main processes involved in the water and energy cycles. In this setting, a surface-groundwater model was coupled to a land-surface model that integratively computed the spatiotemporal variability of evapotranspiration, snowmelt, groundwater, and soil moisture in the root zone, among other processes. Pumping from wells and different irrigation systems were also included in the model. The results showed that a reduction in the cultivation area does not necessarily lead to a significant saving in the total water loss due to evapotranspiration, which strongly depends on the trade-off between the decrease in transpiration and the increase in soil evaporation. We further found that saving in agricultural water consumption is partly controlled by topography and the vertical distribution of soil moisture. Also, the drip irrigation system showed lower evapotranspiration rates than the spray irrigation system. Finally, the study of the future impacts of climate change revealed that the agriculture sector would be under more intensive stress, which poses a severe threat to the long-term restoration of Lake Urmia if effective agricultural management practices are not taken into consideration. The findings of this study highlight important issues for the Lake Urmia restoration plans on agricultural water management. Given the fragility of the lake ecosystem, this study suggests a reliable modeling framework for water and agricultural managers to identify the most appropriate regions in the basin that would yield the highest saving in agricultural water consumption by improving the current crop patterns and irrigation systems.

Influence of natural and anthropogenic controls on runoff in the Keriya River, central Tarim Basin, China.

The potential impact of natural factors on the runoff of intermittent rivers and ephemeral streams (IRES) has been largely ignored in the Tarim Basin, China. A representative example is the Keriya River. To quantify the long-term dynamic variations in lower reach surface runoff of IRES, river length, defined as the distance between a selected fix point along the perennial river segment to its dynamic, ephemeral end, was used as an indicator. Using a total of 272 remote sensing images, we digitized and measured the distance (river length) between the center of Yutian County and the river’s end point on each image, and then calculated monthly inter-annual and intra-annual variations in length of the lower Keriya River from 2000 to 2019. Hydrometeorological data were combined with descriptors of anthropogenic disturbances to assess the relative influence of natural factors and anthropogenic disturbances on lower reach river runoff. The results showed that intra-annual variations in river length fluctuated seasonally, with the minimum value occurring in June; two main peaks occurred in March and August. The minimum June value in river length was closely linked to an increase in agricultural water demand and a decrease in upper reach runoff. The August peak in river length was related to the peak values in upper reach runoff and agricultural water demand; upper reach runoff made a significant contribution because the former was about 20% more than the latter in summer. The March peak corresponded to elevated lower reach groundwater levels and to the melting of ice along river channels. Inter-annual variations in river length were due to inter-annual variations in upper reach runoff and middle reach agricultural water use which increased slightly during the study period. Inter-annual variations in frequency and amplitude of the fluctuations in river length were mainly controlled by changes in upper reach runoff. The minimum in river length in 2009 was consistent with the low in upper reach runoff of the Keriya River and other rivers in the Tarim Basin. The most significant factors controlling variations in river length are natural in origin.

Integrated strategic planning and multi-criteria decision-making framework with its application to agricultural water management

Sustainable water resources management involves social, economic, environmental, water use, and resources factors. This study proposes a new framework of strategic planning with multi-criteria decision-making to develop sustainable water management alternatives for large scale water resources systems. A fuzzy multi-criteria decision-making model is developed to rank regional management alternatives for agricultural water management considering water-resources sustainability criteria. The decision-making model combines hierarchical analysis and the fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The management alternatives were presented spatially in the form of zoning maps at the level of irrigation zones of the study area. The results show that the irrigation management zone No.3 (alternative A3) was ranked first based on agricultural water demand and supply management in five among seven available scenarios, in which the scenarios represents a possible combination of weights assigned to the weighing criteria. Specifically, the results show that irrigation management zone No.3 (alternative A3) achieved the best ranking values of 0.151, 0.169, 0.152, 0.174 and 0.164 with respect to scenarios 1, 4, 5, 6 and 7, respectively. However, irrigation management zone No.2 (alternative A2) achieved the best values of 0.152 and 0.150 with respect to the second and third scenarios, respectively. The model results identify the best management alternatives for agricultural water management in large-scale irrigation and drainage networks.

Effect of Hydrocarbon Contaminated Irrigation Water On Some Selected Soil Physical and Chemical Properties of Sudan Savannah Alfisols

Due to fresh water scarcity challenges around the world, high increase in the demand for agricultural irrigation water from different sources including hydrocarbon contaminated wastewater increases. However, these wastewaters can alter soil physical and chemical properties. The aim of this study is to evaluate the impact of the hydrocarbon contaminated irrigation water on some selected soil physical and chemical properties from three phases of Sharada industrial area, Kano, Nigeria. Soil and water samples were collected from three different phases of the industrial area and analyzed using standard laboratory procedures. The hydrocarbon irrigation water quality analyses indicated that the hydrocarbon concentration in all phases was low and below irrigation reuse standard. However, the major irrigation water quality parameters; chemical oxygen demand (COD), biochemical oxygen demand (BOD), nitrate nitrogen (NO3-N), hydrogen carbonate (HCO3), ammonium-nitrogen (NH4-N), potassium (K), chlorine (Cl) and orthophosphate phosphorus (PO4-P) in all phases were all high and above irrigation reuse standard in comparison to other parameters that were compliant to standard according to literature. Pertaining the impact of the hydrocarbon irrigation water on soil properties, results revealed that soil texture was sandy loam and loamy sand, pH was neutral while bulk density (BD), electrical conductivity (EC), organic carbon (OC), total nitrogen (TN), potassium (K), sodium (Na), calcium (Ca), magnesium (Mg), cation exchange capacity (CEC) and phosphorus (P) were increased and higher compared with control soils. However, they are low in terms of concentration when compared with literature rating standard except P. Moreover, Ca, Mg, CEC and P of the soils showed significant variation statistically (P<0.05) in comparison to the remaining soil properties that recorded no significant statistical difference (P>0.05) in all phases and control soils after irrigation with the hydrocarbon wastewater. Overall, the research indicated that, the hydrocarbon wastewater was not fit for irrigation and has impacted relatively on some physical and chemical soil properties. Hence, all industries in Sharada should be directed by authorities to avoid direct discharge of their effluents without thorough quality investigation. In addition, farmers should also be educated to periodically monitor and assess the irrigation water quality of the effluents prior to application as well as incorporation of organic and inorganic amendments in the irrigated soils to uplift the concentration of the soil properties with concomitant improvement in soil fertility and quality of the study area.

Governance of technological innovations in water and energy use in Uzbekistan

ABSTRACT Increasing water and energy demand in agriculture due to changing climatic conditions and high resource consumption is widespread in Central Asia. Under these constraints, compounded by lack of irrigation water, farmers are opting to use drainage water for crop production, with negative consequences. Focusing on Uzbekistan, we evaluate the governance of farmer adoption of selected technological innovations (i.e., constructed wetlands and biogas) to improve crop productivity while providing water and energy savings. We build upon farmers’ expressed views of the strategic decisions they would take regarding constructed wetlands and biogas plants. We use institutional analysis to identify relevant action situations, highlight potential institutional complementarity between these situations, and indicate promising areas for policy intervention.