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Crop water requirement and irrigation scheduling in Lower Kulfo Catchment of southern Ethiopia have not assessed under climate change scenarios, and the allocation of crop land also not optimal that signifcantly challenges to crop productivity.Therefore, this study was conducted to evaluate the effects of climate change on future crop water requirements, and irrigation scheduling, and to allocate cropland optimally. Bias of projected precipitation and temperature were corrected by utilizing Climate Model data with the hydrologic modeling tool (CMhyd). Alongside, crop water requirements and irrigation scheduling were assessed using Crop Water Assessment Tool. After estimating crop water requirement, crop land were allocated optimally using General Algebraic Modeling System programming with non-negativity constraints (scenario 1), and non-negativity constraints based on farmers adaptation (scenario 2). Average reference evapotranspiration from 2030 to 2050 and 2060 to 2080 was increased by 11.9%, and 16.2%, respectively compared with the reference period (2010-2022). The total seasonal crop water requirements were 4,529mm, 4,866.7mm, and 5,272.2mm under 2010 to 2022, 2030 to 2050, and 2060 to 2080 climate change scenarios, respectively. The meean irrigation interval in 2010 to 2022, 2030 to 2050, and 2060 to 2080 climate change scenarios were 8 days, 7 days, and 5 days, respectively. This irrigation interval was decreased by 14% (2030 to 2050), and 34% (2060 to 2080) compared with the reference period. In 2030 to 2050 and 2026 to 2080 climate change scenarios, the required irrigation water at the inlet of main canal increased by 6.8%, and 18%, respectively. The optimal allocated area for tomato (60.4%), maize (20.8%), and watermelon (18.8%) in scenario 1 with net benefit of 1.47*108 Ethiopian Birr. The allocated areas in scenario 2 were (48%) for maize, (31.6%) for tomato, and (20.4%) for watermelon with 1.34*108 Ethiopian Birr net benefit it was reduced by 19.1% compared with the net benefit in scenario 1. Fruit crops alone may not suffice for local food needs and to address this, small farmers should grow maize, tomato, and watermelon. This research aids policymakers in encouraging climate-resilient agriculture and improving small-scale farmers' awareness through conducting workshops and training. Crop water requirement and irrigation scheduling in Lower Kulfo Catchment of southern Ethiopia have not assessed under climate change scenarios, and the allocation of crop land also not optimal that signifcantly challenges to crop productivity.Therefore, this study was conducted to evaluate the effects of climate change on future crop water requirements, and irrigation scheduling, and to allocate cropland optimally. Bias of projected precipitation and temperature were corrected by utilizing Climate Model data with the hydrologic modeling tool (CMhyd). Alongside, crop water requirements and irrigation scheduling were assessed using Crop Water Assessment Tool. After estimating crop water requirement, crop land were allocated optimally using General Algebraic Modeling System programming with non-negativity constraints (scenario 1), and non-negativity constraints based on farmers adaptation (scenario 2). Average reference evapotranspiration from 2030 to 2050 and 2060 to 2080 was increased by 11.9%, and 16.2%, respectively compared with the reference period (2010-2022). The total seasonal crop water requirements were 4,529mm, 4,866.7mm, and 5,272.2mm under 2010 to 2022, 2030 to 2050, and 2060 to 2080 climate change scenarios, respectively. The meean irrigation interval in 2010 to 2022, 2030 to 2050, and 2060 to 2080 climate change scenarios were 8 days, 7 days, and 5 days, respectively. This irrigation interval was decreased by 14% (2030 to 2050), and 34% (2060 to 2080) compared with the reference period. In 2030 to 2050 and 2026 to 2080 climate change scenarios, the required irrigation water at the inlet of main canal increased by 6.8%, and 18%, respectively. The optimal allocated area for tomato (60.4%), maize (20.8%), and watermelon (18.8%) in scenario 1 with net benefit of 1.47*108 Ethiopian Birr. The allocated areas in scenario 2 were (48%) for maize, (31.6%) for tomato, and (20.4%) for watermelon with 1.34*108 Ethiopian Birr net benefit it was reduced by 19.1% compared with the net benefit in scenario 1. Fruit crops alone may not suffice for local food needs and to address this, small farmers should grow maize, tomato, and watermelon. This research aids policymakers in encouraging climate-resilient agriculture and improving small-scale farmers' awareness through conducting workshops and training. Agriculture plays an important role in driving economic growth within the Ethiopian economy and it covers 40% of gross domestic product 1Tesema T. Gebissa B. Multiple Agricultural Production Efficiency in Horro District of Horro Guduru Wollega Zone, Western Ethiopia, Using Hierarchical-Based Cluster Data Envelopment Analysis.Sci. World J. 2022; 2022https://doi.org/10.1155/2022/4436262Crossref Scopus (1) Google Scholar. Irrigation agriculture in dry or semi-dry environments is used to sustain agricultural productivity when available rainfall is insufficient 2Zhang J. et al.Challenges and opportunities in precision irrigation decision-support systems for center pivots.Environ. Res. Lett. 2021; 16https://doi.org/10.1088/1748-9326/abe436Crossref Scopus (39) Google Scholar. Effective irrigation water management across the water conveyance system and demand-based irrigation scheduling are the basic activities to improve the productivity of irrigation schemes 3Létourneau G. Caron J. 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Assessing Climate Change Impacts on Irrigation Water Requirements under Mediterranean Conditions—A Review of the Methodological Approaches Focusing on Maize Crop.Agronomy. 2023; 13https://doi.org/10.3390/agronomy13010117Crossref Scopus (10) Google Scholar. The Coupled Model Intercomparison Project (CMIP) projected climate driving model is organized by the World Climate Research Program (WCRP) it produces ensembles of Earth System Model (ESM) projected future climate conditions based on different CO2 emission scenarios 8Tian X. Dong J. Jin S. He H. Yin H. Chen X. Climate change impacts on regional agricultural irrigation water use in semi-arid environments.Agric. Water Manag. 2023; 281108239https://doi.org/10.1016/j.agwat.2023.108239Crossref Scopus (8) Google Scholar. 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Determining the Changing Irrigation Demands of Maize Production in the Cukurova Plain under Climate Change Scenarios with the CROPWAT Model.Water. 2023; 15Crossref Scopus (0) Google Scholar. Climate Model data with the hydrologic modeling tool (CMhyd) is used to utilize bias correction between historical and projected climate 12Yeboah K.A. Akpoti K. Kabo-bah A.T. Ofosu E.A. Siabi E.K. Assessing climate change projections in the Volta Basin using the CORDEX- Africa climate simulations and statistical bias-correction Assessing climate change projections in the Volta Basin using the CORDEX-Africa climate simulations and statistical bias-correction.Environ. Challenges. 2022; 6100439https://doi.org/10.1016/j.envc.2021.100439Crossref Scopus (21) Google Scholar that is used as input for the CropWat model. Proper irrigation scheduling under climate change scenarios is used to increase yields and manage the amount, and frequency of irrigation 13Betele D. Gebul M.A. Andries J. Plessis D. Assessment of irrigation water allocation.Koftu , Ethiopia. 2023; 18: 1331-1342https://doi.org/10.2166/wpt.2023.080Crossref Scopus (1) Google Scholar and it also gives a direction to adapted climate resilience agriculture. Optimizing agricultural land utilization is important to satisfy household food security by providing most economical crops for the specific area 14Pal J.S. et al.Regional climate modeling for the developing world: The ICTP RegCM3 and RegCNET.Bull. Am. Meteorol. Soc. 2007; 88: 1395-1409https://doi.org/10.1175/BAMS-88-9-1395Crossref Scopus (835) Google Scholar. This crop optimization model contains objective function, decision variables 15Zenis F.M. Supian S. Lesmana E. Optimization of land use of agricultural farms in Sumedang regency by using linear programming models.IOP Conf. Ser. Mater. Sci. Eng. 2018; 332https://doi.org/10.1088/1757-899X/332/1/012021Crossref Scopus (3) Google Scholar, and constraints that depending on nature of the problems 16Sofi N.A. Ahmed A. Ahmad M. Bhat B.A. Decision Making in Agriculture: A Linear Programming Approach.Int. J. Mod. Math. Sci. J. homepage www.ModernScientificPress.com. 2015; 13 ([Online. Available:): 160-169www.ModernScientificPress.com/Journals/ijmms.aspxGoogle Scholar]. Crop allocation model can consider water and land availability 17Nimah M.N. Bsaibes A. Alkahl F. Darwish M.R. Bashour I. Optimizing cropping pattern to maximize water productivity.River Basin Manag. Ii. 2003; 7: 187-198Google Scholar and the aims of optimization also to maximize agricultural net benefit per unit of water or land 18Hao L. Su X. Singh V.P. Cropping pattern optimization considering uncertainty of water availability and water saving potential.Int. J. Agric. Biol. Eng. 2018; 11: 178-186https://doi.org/10.25165/j.ijabe.20181101.3658Crossref Scopus (20) Google Scholar. General Algebraic Modeling systems (GAMS) code programming is the best tool to allocate agricultural land under different cropping patterns and this allocation strategy considers water, land, crop, and economic constraints 19Jayne T.S. Chamberlin J. Headey D.D. Land pressures, the evolution of farming systems, and development strategies in Africa: A synthesis.Food Policy. 2014; 48: 1-17https://doi.org/10.1016/j.foodpol.2014.05.014Crossref Scopus (336) Google Scholar. During dry season, there was water conflict among the water users in Lower Kulfo Catchment due to the scarcity of irrigation water and the amount of irrigation demand showed an increasing trend as observed during problem investigation that may be due to climate change. Absence of estimated crop water requirement and lack of proper irrigation scheduling practices in the Lower Kulfo Catchment was significantly disturbed water management and distribution. Addition to this, rainfall variability and shifting of wet season also the major problems in the lower Kulfo catchment that hinder rainfed/irrigated agriculture in the area. Stream flow of the Kulfo River will be decreased by 2.99% in the 2050s and 5.28% in the 2080s due to climate change impact 20N. G. Demmissie, T. A. Demissie, and F. G. Tufa, "Predicting the Impact of Climate Change on Kulfo River Flow," vol. 6, no. 3, pp. 78–87, 2018, doi: 10.11648/j.hyd.20180603.11.Google Scholar. But there was no any conducted research in Lower Kulfo Catchment to evaluate the impacts of climate change on crop water requirement and irrigation scheduling. Both land and water productivity of crops Arba Minch irrigation in the Lower Kulfo Catchment are low 21Reta B.G. Hatiye S.D. Finsa M.M. Assessment of Irrigation Water Management Performance Indicators and Mitigation Measure in Arba Minch Irrigation.Adv. Agric. 2024; Google Scholar and that may be lack of knowledge about user-friendly crop optimization tools for identifying economical crops to the area. Traditional allocation of cropland was adopted by irrigation users in the lower Kulfo catchment that was due to lack of understanding regarding affordable crop optimization programming like GAMS code. Poor optimal cropland allocation under multiple cropping patterns undermines the effectiveness of an irrigation scheme, leading to reduced crop yields, increased operational costs, market value fluctuations, and environmental degradation 22Yubing Fan S.C.P. R. M.Multi-Crop Production Decisions and Economic Irrigation Water Use Efficiency : The Effects of Water Climatic Determinants.Water. 2018; 10https://doi.org/10.3390/w10111637Crossref Scopus (12) Google Scholar. These agricultural-related problems can be solved by developing reasonable irrigation scheduling and estimating crop water requirements under climate change scenarios, and optimal cropland allocation is also used to identify the most economical crops in the area. Therefore, this study was conducted to estimate crop water requirement and irrigation scheduling for the worst dry season under three climate change scenarios and to allocate cropland under multiple crop systems in the lower Kulfo catchment using GAMS code programming. The significance of this research lies in its potential to address critical challenges in the lower Kulfo catchment, offering solutions for optimizing water resources, improving agricultural productivity, and fostering sustainable practices in the face of climate change. Lower Kulfo catchment was located between 6 2' 0" and 6 5' 0" North latitude and 37 33'0" and 37 36'0" East longitudes of Southern Ethiopia (Figure 1). Elevation of thestudy area was varied from 1200 to 1203.8m above the mean sea. The Lower Kulfo catchment was located near Arba Minch town, running alongside the main road connecting Arba Minch to Mirab Abaya and Wolayita Sodo and this location holds significant importance for the efficient transportation of fruit production to the market. Arba Minch irrigation, Arba Minch University (AMU) farmland, smallhold farmer in the Kola Shara district, and private farmland near the Arba Minch airport were included in the study area. The irrigated area of the Arba Minch irrigation scheme, Arba Minch University farm, Kolla shara farmland, private farmland 1, and private farmland 2 in Lower Kulfo Catchment were 835.22ha, 109.17ha, 160.23ha, 18.44ha, and 52.76ha, respectively, and the total irrigable land was 1175.82ha (Figure 1). The water source of the Lower Kulfo Catchment was Kulfo River and the annual minimum, and maximum flow of the River were 2.35m3/s and 50.73m3/s, respectively. Market survey was conducted to collect local price of the dominant crop in the study area and the survey included both sellers and buyers in the market which helps to identify crops that are used by people since only crop prices are not used to justify whether the crop is profitable or not. Field observation was conducted around the Lower Kulfo Catchment to investigate the most practical agricultural crop and this field observation also used to understand the agronomic performance of crops in the area. Both quantitative and qualitative data regarding crop production practices such as land size, costs of crop production, existing farming practices, and productivity of crops per hectare were collected through household survey questionnaires, key informant interviews, and focus group discussions. This data was used to estimate the costs and revenues of crop production per hectare for each crop. Based on the 23A. Wright, D. Hudson, and M. Mutuc, "A Spatial Analysis of Irrigation Technology," vol. 2013, pp. 307–318, 2013.Google Scholar, simplified formula, the sample size for household interviews in Kolla shara Kebele was calculated as described in Eq 1.(1) Where n and N are sample size and total population size respectively, and e is expected error (5%) at a 95% confidence level. The total population and available agricultural land were collected from Arba Minch Zuria Woreda office. The total irrigated land in Kolla Shara Kebele that will be irrigated with the Kulfo River was estimated with ArcGIS software after collecting of ground control point (Table 1).Table 1population and sample size to conduct the household interview in Kolla shareTotal population10,794Number of farmers886Available agricultural land (ha)1,974Probable area irrigated by Kulfo River (ha)160.2Number of farmers under irrigated land (N)72Expected error (e)5% @95% confidence levelSample size to conduct a household interview61 Open table in a new tab Methods of soil sampling were composite techniques and the maximum sampling depth is 0.9m. Soil texture was evaluateing using a hydrometer test and bulk density also evaluated by dividing dry mass of soil sample by volume of after drying in oven dry at 105 for 24 hours. Soil chemical properties such as soil organic matter and electric conductivity also estimated laboratory that used to justify present status of soil fertility. Soil field capacity and permanent wilting point were estimated by using pressure plate apparatus in the laboratory. Based on 24Goebel T.S. Lascano R.J. Acosta-Martinez V. Evaluation of Stable Isotopes of Water to Determine Rainwater Infiltration in Soils under Conservation Reserve Program.J. Agric. Chem. Environ. 2016; 05: 179-190https://doi.org/10.4236/jacen.2016.54019Crossref Google Scholar, infiltration characteristics of soil were determined by using a double-ring infiltrometer. These estimated soil physical properties were used as input for CropWat model to estimate crop water requirement and irrigation scheduling. Based on 25Chen C. Hsu H. Liang H. Evaluation and comparison of CMIP6 and CMIP5 model performance in simulating the seasonal extreme precipitation in the Western North Pacific and East Asia.Weather Clim. Extrem. 2021; 31https://doi.org/10.1016/j.wace.2021.100303Crossref Scopus (92) Google Scholar, Climate Model Intercomparison Project 6 (CMIP6) has good performance compared with Phase 3 (CMIP3) and Phase five (CMIP5) in predicting future climate trends. As a result, the future temperature and precipitation of the current study was derived from the sixth phase of the Climate Model Intercomparison Project 6 (CMIP6). To utilized bias correction, historical temperature and precipitation were collected from the Arba Minch meteorological station. Temperature, and precipitation network Common Data Form (netCDF) files were extracted using coordinate and elevation of Arba Minch meteorological station. The Climate Model data for hydrologic modeling tool (CMhyd) was employed for bias correction of rainfall and temperature and based on 26Leander R. Buishand T.A. Resampling of regional climate model output for the simulation of extreme river flows.J. Hydrol. 2007; 332: 487-496https://doi.org/10.1016/j.jhydrol.2006.08.006Crossref Scopus (353) Google Scholar, bias correction was addressed by Eq 2 and 3.(2) Where p* is the bias-corrected rainfall, P is the uncorrected rainfall amount, and a P is the uncorrected rainfall amount; a and b are factors Crop water assessment tool (CropWat) software is a computer program it was used estimate crop water requirements and irrigation scheduling using soil, climate, and crop data as input. Based on 27R. G. Allen, L. S. Pereira, D. Raes, and M. Smith, "FAO Irrigation and Drainage Paper No. 56 - Crop Evapotranspiration," no. November 2017, 1998.Google Scholar, reference evapotranspiration (ETo), crop water requirement (CWR), effective rainfall (Pe), irrigation water requirement (IWR), and irrigation interval (i) were evaluated through CropWat under different climate change scenarios. This climate scenario was 2010 to 2022 (reference), 2030 to 2050, and 2060-2080 and this future climate change was only temperature and rainfall. Projected solar radiation, humidity, wind speed and sunshine hours are not available in the Intercomparison Project 6 (CMIP6) model as result historical value of reference period was used to estimate reference evapotranspiration in all climate change scenarios. The driest season of study area was from January to April as a result, reference evapotranspiration, crop water requirement, and irrigation scheduling were evaluated for this worst condition in all climate change scenarios. Crop coefficients of each crop for initial, mid, and late stages were collected from irrigation and drainage manual paper number 56 (FAO 56) as presented in Table 2 but crop coefficients of teff were not found in the FAO paper. Length of crop growing stage, root depth, yield reduction factor, allowable management depletion, and planting and harvesting date of crops were also collected from FAO irrigation and drainage paper. The crop coefficients of teff in central Rift Valley Lake Basin, Ethiopia were 0.46 (initial stage), 0.88 (development stage), 1.03 (mid-stage), and 0.57 (late stage) 28T. Hordofa, "Crop Water Requirement and Crop Coefficient of Tef ( Eragrostis tef ) in Central Rift Valley of Ethiopia," vol. 11, no. 15, pp. 34–39, 2020, doi: 10.7176/JNSR/11-15-0.Google Scholar. Reference evapotranspartion, crop water requirement, effective rainfall, irrigation requirement, and irrigation interval were evaluated by using Eq 4, Eq 5, Eq 6/7, Eq 8 and Eq 9, respectively.Where; Rn=net radiation at the crop surface (MJ m-2 day-1), G=Soil heat flux density (MJ m-2 day-1), T=Mean daily air temperature at 2m height (oc), U2=Wind speed at 2m height (ms-1), es =Saturation vapour pressure (kPa), ea= actual vapour pressure (kpa), (es -ea) =Saturated vapour pressure deficit, (kpa), Δ=slope vapour pressure curve (kPa oc-1) and r=psychrometric constant (kPa oc-1), Kc=crop coefficients (-), P is the total rainfall (mm), d is net irrigation depth (mm), and CWR is daily crop water requirement (mm/day).Table 2crop coefficients as a function of crop type and crop growth stage 27R. G. Allen, L. S. Pereira, D. Raes, and M. Smith, "FAO Irrigation and Drainage Paper No. 56 - Crop Evapotranspiration," no. November 2017, 1998.Google Scholar(4) (5) (6) (7) (8) (9) Crop/Growth stageInitial stageMid-stageLate-stageWheat0.31.150.25-0.4 (0.325)Maize0.31.20.6Watermelon0.410.75Pepper0.61.050.9Onion0.71.050.75Banana0.61.11.05Tomato0.151.10.6-0.8 (0.7) Open table in a new tab General Algebraic Modeling System (GAMS) code was used to solve mixed-integer, linear, and nonlinear optimization problems 29Hooper B.P. Integrated Water Resources Management and River Basin Governance.Water Resour. 2003; (Updat): 12-20Google Scholar. Objective function of this research was to maximize the net profits of crop production in the catchment and it was develop based on 30Bowen R.L. Young R.A. Financial and Economic Irrigation Net Benefit Functions for Egypt's Northern Delta.Water Resour. Res. 1985; 21: 1329-1335https://doi.org/10.1029/WR021i009p01329Crossref Scopus (23) Google Scholar as described in Eq 10..(10) Where; Pi, Yi, Xi, and Ci were price for the crop "i" (birr/ton), the yield of the crop "i" (ton/ha), allocated area for crop "i" (ha) and production cost for the crop "i" (birr/ha), respectively. Total production cost was including labor, fertilizer, pesticides, and insecticides cost and total irrigated land, water availability, expected outcome, total production cost and non-negativity were constraints of the objective function. Lengths of crop development stages for various planting periods were collected from 27R. G. Allen, L. S. Pereira, D. Raes, and M. Smith, "FAO Irrigation and Drainage Paper No. 56 - Crop Evapotranspiration," no. November 2017, 1998.Google Scholar and crop planting was start in January according to the guidelines outlined in the FAO Irrigation and Drainage paper and only permanent banana crops persist year-round. Onions can be harvested by the end of March, while wheat crops require five months from the time of planting and irrigation will be stope after four months except perennial banana crop. The sum of allocated areas for each crop will not exceed the total available land (At) as describe in Eq 11.(11) Where X1, X2, X3, X4, X5, X6, X7, and X8 are allocated areas for crop onion, maize, watermelon, pepper, wheat, banana, Teff, and tomato, respectively (ha) and At=total irrigated land (ha). The multiple product of irrigated land (ha) and gross irrigation depth (mm) was used to calculate the total volume of irrigation water. This value to be less than or equal to the seasonal minimum amount of water that could be obtained from the sources. Crop water requirement and effective rainfall under the reference period (2010 to 2022) was used to develop the Equation of water availability constraint and crop water requirement of banana after four months not included (Eq 12).(12) Where; CWR=crop water requirement for the crop "i" (m), Peff=effective rainfall (m), Vmin= annual minimum volume of water supply (ha-m). The expected crop yield represents the highest achievable productivity of a crop per hectare when soil quality is optimal, irrigation is managed effectively, and there is sufficient rainfall or irrigation water available. This expected maximum productivity data is sourced from the Irrigation and Drainage Paper Manual Number 33 (FAO 33). The primary aim of this research is to maximize the overall expected yield from the crops (Eq 13).(13) Where YI is the average expected land productivity for each crop (ton/ha) and TYc=Expected total yield from all crops (ton). The sum of the production costs for each crop should not exceed with actual total production cost as describe in Eq 14.(14) Where; PCi and TPC were crop production cost for crop I (Birr/ha) and total production cost (Birr) respectively. Non-negativity constraints of optimization were had two scenarios such as allocated land for each crop was non-negativity (scenario 1) and the remaining non-negativity constraints also depend on small-hold farmer practices (scenario 2). The remaining constraints (such as land, water, expected yield, and production cost) were common in both scenarios. The allocated area for each crop were considered as non-negativity during the GAMS code optimization (scenario 1).X1>=0, X2>=0, X3>=0, X4>=0, X5>=………………………………………………………………………….X8>=0 Based on the household interview, the minimum area that covered by the maize crop under scenario 2 optimization was 564.9ha (48% of total area). Because only fruit and vegetable production will not cover food consumption in Lower Kulfo Catchment due to that smallhold farmers allocate more land for maize. The other optimization also evaluated under 48% area covered by maize crop in ordered to satisfy household food security in the area (scenario 2).X1>=0, X2>=564.9ha, X3>=0, X4>=0, X5>=………………………………………..……X8>=0 Average soil texture was clay and the mean value of soil bulk density, soil organic matter, electric conductivity, field capacity, permanent wilting point, and total available water were 1.32 gm/cm3, 0.87%, 0.16ds/m, 38.3%, 25.9%, and 124mm/m, respectively (Table 3). Based on 27R. G. Allen, L. S. Pereira, D. Raes, and M. Smith, "FAO Irrigation and Drainage Paper No. 56 - Crop Evapotranspiration," no. November 2017, 1998.Google Scholar, the maximum total available water of the clay soil varied from 110 to 160mm/m, and the value of the current study was also found with the recommended value that was 127mm/m. The soil was suitable for agricultural practices to be uncompacted with a bulk density of less or equal to 1.63gm/cm3 [31Twum E.K.A. Nii-Annang S. Impact of Soil Compaction on Bulk Density and Root Biomass of Quercus petraea L. at Reclaimed Post-Lignite Mining Site in Lusatia, Germany.Appl. Environ. Soil Sci. 2015; 2015https://doi.org/10.1155/2015/504603Cross
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