Crop Water Need Research Articles

Innovation in Agriculture Advantages of a Remote Irrigation Monitoring and Control System

Globally, the irrigation of crops is the largest consumptive user of water. Water scarcity is increasing worldwide, resulting in tighter regulation of its use for agriculture. This necessitates the development of irrigation practices that are more efficient in the use of water but do not compromise crop quality and yield. Precision irrigation already achieves this goal, in part. The goal of precision irrigation is to accurately supply the crop water need in a timely manner and as spatially uniformly as possible. However, to maximize the benefits of precision irrigation, additional technologies need to be enabled and incorporated into agriculture. This Search discusses how Advantages of a remote irrigation monitoring and control system irrigation management will enable significant advances in increasing the efficiency of current irrigation approaches. From the literature review, it is found that precision irrigation can be applied in achieving the environmental goals related to sustainability. The demonstrated economic benefits of precision irrigation in field-scale crop production is however minimal. It is argued that a proper combination of soil, plant and weather sensors providing real-time data to an adaptive decision support system provides an innovative platform for improving sustainability in irrigated agriculture. The review also shows that adaptive decision support systems based on model predictive control are able to adequately account for the time-varying nature of the soil–plant–atmosphere system while considering operational limitations and agronomic objectives in arriving at optimal irrigation decisions. It is concluded that significant improvements in crop yield and water savings can be achieved by incorporating model predictive control into precision irrigation decision support tools. Further improvements in water savings can also be realized by including deficit irrigation as part of the overall irrigation management strategy. Nevertheless, future research is needed for identifying crop response to regulated water deficits, developing improved soil moisture and plant sensors, and developing self-learning crop simulation frameworks that can be applied to evaluate adaptive decision support strategies related to irrigation.

Robustness of climate indices relevant for agriculture in Africa deduced from GCMs and RCMs against reanalysis and gridded observations

This study assesses the ability of climate models to represent rainy season (RS) dependent climate indices relevant for agriculture and crop-specific agricultural indices in eleven African subregions. For this, we analyze model ensembles build from Regional Climate Models (RCMs) from CORDEX-CORE (RCM_hist) and their respective driving General Circulation Models (GCMs) from CMIP5 (GCM_hist). Those are compared with gridded reference data including reanalyses at high spatio-temporal resolution (≤ 0.25°, daily) over the climatological period 1981–2010. Furthermore, the ensemble of RCM-evaluation runs forced by ERA-Interim (RCM_eval) is considered. Beside precipitation indices like the precipitation sum or number of rainy days annually and during the RS, we examine three agricultural indices (crop water need (CWN), irrigation requirement, water availability), depending on the RS’ onset. The agricultural-relevant indices as simulated by climate models, including CORDEX-CORE, are assessed for the first time over several African subregions. All model ensembles simulate the general precipitation characteristics well. However, their performance strongly depends on the subregion. We show that the models can represent the RS in subregions with one RS adequately yet struggle in reproducing characteristics of two RSs. Precipitation indices based on the RS also show variable errors among the models and subregions. The representation of CWN is affected by the model family (GCM, RCM) and the forcing data (GCM, ERA-Interim). Nevertheless, the too coarse resolution of the GCMs hinders the representation of such specific indices as they are not able to consider land surface features and related processes of smaller scale. Additionally, the daily scale and the usage of complex variables (e.g., surface latent heat flux for CWN) and related preconditions (e.g., RS-onset and its spatial representation) add uncertainty to the index calculation. Mostly, the RCMs show a higher skill in representing the indices and add value to their forcing models.

Modeling Daily Reference Evapotranspiration from Climate Variables: Assessment of Bagging and Boosting Regression Approaches

The increasing frequency of droughts and floods due to climate change has severely affected water resources across the globe in recent years. An optimal design for the scheduling and management of irrigation is thus urgently needed to adapt agricultural activities to the changing climate. The accurate estimation of reference crop evapotranspiration (ET0), a vital hydrological component of the water balance and crop water need, is a tiresome task if all the relevant climatic variables are unavailable. This study investigates the potential of four ensemble techniques for estimating precise values of the daily ET0 at representative stations in 10 agro-climatic zones in the state of Karnataka, India, from 1979 to 2014. The performance of these models was evaluated by using several combinations of climatic variables as inputs by using tenfold cross-validation. The outcomes indicated that predictions of ET0 by all four ensemble models based on all climatic variables were the most accurate in comparison with other input combinations. The random forest regressor was found to deliver the best performance among the four models on all measures considered (Nash–Sutcliffe efficiency, 1.0, root-mean-squared error, 0.016 mm/day, and mean absolute error, 0.011 mm/day). However, it incurred the highest computational cost, whereas the computational cost of the bagging model for linear regression was the lowest. The extreme gradient-boosting model delivered the most stable performance with a modified training dataset. The work here shows that these models can be recommended for daily ET0 estimation based on the users’ interests.

Determining Length of Growing Period for Crop Planning in East and South Eastern Coastal Plain Agroclimatic Zone of Odisha

The length of the growing period (LGP) refers to a number of days in a year during which the moisture at the root zone of crop plants is adequate to meet the crop water need. Information on LGP provides a basis for the selection of crops, cultivars (short / medium / long duration) and cropping/farming systems of a region.
 Aim: The study aims to estimate the length of crop growing season of the East and South Eastern Coastal Plain Agroclimatic Zone of Odisha in order to provide a basis for suitable crop planning for the region. 
 Methodology: The LGP for four districts (Jagatsinghpur, Kendrapara, Khordha and Puri) under East and South Eastern Coastal Plain Agroclimatic Zone of Odisha was estimated using rainfall data of 30 years (1991 to 2020). The growing season begins when the sum of daily rainfall (forward accumulation) reaches 75 mm (28 May, 29 May…15 Oct) and ends when backward accumulation (15 Oct, 14 Oct,……, 28 May) of 20 mm reach. The LGP was worked out by adding the duration of the rainy season with the sum of post-monsoon rainfall and the average water-holding capacity of soil divided by the post-monsoon PET of the district.
 Results: LGPs obtained by the above method varied from 173 to 192 days in this zone with Kendrapara district having the highest LGP (192 days) and Khordha the shortest (173 days). The date of onset and cessation of south west (SW) monsoon was 15-17 June and 13 October, respectively.
 Conclusion: Double cropping can be followed in Jagatsinghpur, Kendrapara and Puri districts where the length of the growing period >180 days except Khordha district where paira cropping can be followed as the length of the growing period <180 days.

OPTIMIZED HYBRID WIND-SOLAR ENERGY SYSTEM STRUCTURES FOR IRRIGATION PUMP LOAD SITES IN KENYA

The world’s Sustainable Development Goals as clearly presented by the United Nations Department of economic and social affairs has zero hunger, and affordable clean energy. In the global struggle to meet both energy and sustainable agriculture, emerges the complex Energy-Water-Food (EWF) nexus. Kenya has initiated large-scale irrigation projects to ensure zero hunger. However, the energy required to run the pumps is very high, since diesel pumps and diesel generators are extensively used. This translates to high food production costs. In this research, optimized affordable clean energy systems required for irrigation were modeled and studied. Optimized hybrid wind and solar energy solutions for irrigation projects were presented. Five potential large-scale irrigation sites in Kenya were considered namely: Galana-Kulalu, Lotikipi, Rahole, Wei Wei, and Perkerra. The results showed that the energy requirement scenarios depended majorly on on-site conditions; crop water need cycles and rainfall patterns. Average Wind Speed to Global Horizontal Irradiance ratio influenced the structure of hybrid energy systems. Based on this ratio, longer cycle crops like sugarcane, maize, and cotton required systems with higher wind energy penetration compared to shorter cycle crops like radish and spinach. When this ratio changed in selected other irrigation sites, wind to solar power production was greater than one, and up to 500 times greater than one in sites with relatively stable sufficient wind like Lotikipi. Wind energy systems for areas with higher ratios contributed to smaller system structures as a result of a lower number of solar photovoltaic panels installed.

Design of decision support system to identify crop water need

Design of decision support system to identify crop water need

Negative pressure irrigation for greenhouse crops in China: A review

Negative pressure irrigation (NPI) has potential to reduce evaporative loss of water on –the soil surface, reduce runoff and maintain a stable soil water content, as compared to other irrigation methods. However, there is a lack of synthesized information in literature with regard to its effects on crop yield and water use efficiency (WUE), which hinders its wide adoption in China. We introduce the principles and device of NPI, analyze the NPI research hotspots and review its effects on yield and WUE of typical greenhouse crops, based on the literature available in the China National Knowledge Infrastructure (CNKI) and Web of Science (WOS). Review shows that a NPI device typically consists of emitter, water pipe, water storage tank and negative pressure generator. The material of emitters is important in affecting irrigation efficiency, and polyvinyl formal materials have higher cumulative infiltration per unit area and faster soil water movement than ceramic head under the same water pressure, as the polyvinyl formal material can control the larger soil water space than a ceramic head. Current research hotspots include the development of NPI devices, crop response and water use efficiency of NPI, and influences of environmental factors on irrigation efficiency. Compared with conventional irrigation, NPI is more energy-saving and water-saving, and significantly improves WUE and crop yield. Soil texture is the main factor contributing to the maximum vertical wetting distance and maximum horizontal wetting distance and cumulative infiltration, and it affects the irrigation efficiency of NPI and the arrangement of NPI for meeting crop water need. Results suggest that when the negative pressure level of −10 kpa to −5 kpa and the soil water content of 60–80% field capacity are most conducive to the growth of most greenhouse crops, with better agronomic effects than conventional irrigation.

Mathematical Models and Comparative Analysis for Rice and Soya Bean Irrigation Crop Water Needs: A Case Study of Bida Basin Niger State, Nigeria

In this manuscript, mathematical models for cropping water need (C.W.N) and the size of land for irrigation (S.L.I) were formulated. The solutions of the models for Crop water need for Soya beans and Rice, and the size of land for irrigation (S.L.I) of the two crops was obtained. We fill the gap by considering the size of the irrigation land which is not considered by the Food and Agriculture Organization (F.A.O). The computational Method of solutions is carried out to get effective results. The climatic data of the study area (Bida Basin) under which our research is based includes: Rainfall, Humidity, Sunshine hours, minimum and maximum temperature, evapotranspiration were secondary data collected from Nigeria Metrological Society (NIMET). We compared the results of CROPWAT 8.0 software developed by the Food and Agriculture Organization (F.A. O) and our computational method so that we can arrive at a new finding and better results. The results for the computational method with the size of Land for irrigation shows that there is an increase in crop water need for the crops than the results of CROPWAT 8.0 software developed by the Food and Agriculture Organization (F.A. O) in which the size for the land is not considered. We therefore, recommended that the integral calculus can be used to estimate the irregular shape of the size of the land if the land shape is not in rectangular form before solutions are given for accuracy and effective results.

Modeling climate change impacts on crop water demand, middle Awash River basin, case study of Berehet woreda

Abstract Climate change mainly affects crops via impacting evapotranspiration. This study quantifies climate change impacts on evapotranspiration, crop water requirement, and irrigation water demand. Seventeen GCMs from the MarkSim-GCM were used for RCP 4.5 and 8.5 scenarios for future projection. A soil sample was collected from 15 points from the maize production area. Based on USDA soil textural classification, the soil is classified as silt loam (higher class), clay loam (middle class), and clay loam (lower class). The crop growing season onset and offset were determined using the Markov chain model and compared with the farmer's indigenous experience. The main rainy season (Kiremt) starts during the 1st meteorological decade of June for baseline period and 2nd decade to 3rd decade of June for both RCP 4.5 and RCP 8.5 of near (2020s) and mid (2050s) future period. The offset date is in the range of 270 (base period), RCP 4.5 (278, 284), and RCP 8.5 (281, 274) DOY for baseline, near, and mid future. The rainfall and temperature change show an increasing pattern from the base period under both scenarios. Furthermore, the reference evapotranspiration (ETo) estimating model was developed using multiple variable regression and used for a future period in this study. In the base period, ETo increases from 33.4 mm/dec in the 1st decade of July to the peak value of 52.1 mm/dec in the 3rd decade of May. Under RCP8 .5, the 2nd decade of August ETO is minimal (44.3 mm/dec), while in 1st decade of April ETO was maximum (75.3 mm/dec) and raise from 44.3 mm/dec in the 2nd decade of August to the peak value of 75.3 mm/dec in the 1st decade of April. Under RCP 4.5, ETO raises from 33 mm/dec in the 1st decade of Dec to the peak value of 48 mm/dec in the 3rd decade of May. ETo shows an increasing trend from the base period under both scenarios. During the base period, a maize variety with a growing period of 110 days required 403.2 mm depth of water, while 67 mm is required as supplementary irrigation. Crop water and irrigation requirements of the maize variety with a growing period of 110 days are predicted to be 436.1 and 445.1 mm water during the 2020 and 2050 s for RCP 4.5, while 101.8 to 63.7 mm depth of water as supplementary irrigation respectively and 441.3 and 447.3 mm of water during 2020 and 2050 s of the future period for RCP 8.5, while 142.9 to 134.0 mm required as supplementary irrigation for both periods of RCP 8.5 scenarios. Crop water need will increase by 8.16 and 10.39% for RCP 4.5 and by 9.45 and 10.94% for RCP 8.5 of the 2020 and 2050 s respectively. In this study, a new ETO model is developed using a multiple variable linear regression model and its degree of the fitting is statistically tested and Kc is adjusted for the local climate, and hence, can be used in future irrigation and related studies. Generally, decision-makers, farmers, Irrigation engineers, and other stakeholders can use the results of this study in irrigation design, monitoring, scheduling, and other related activities.

Designing an irrigation system using photovoltaic energy by considering crop type in Fergana Valley

Today, the importance of energy cost and efficiency is gradually increase. The decrease in drinking water and agricultural water resources, increases the interest in drip irrigation systems in agricultural irrigation. Environmentally friendly photovoltaic drip irrigation systems (PVDIS) are the appropriate solution in regions where there is no electricity distribution network, where it is far away, or where power cuts are frequently. This study is carried out in the Fergana Valley of Uzbekistan. Regional climate data obtained from Climwat 2.0 software are processed in Cropwat 8.0 software. Crops that are both the source of livelihood of the people of the region and that can be used in this study have been determined. Annual and daily water needs are analyzed so that these crops are irrigated every seven days. A system is designed by taking the data of the crop with the highest water requirement as a reference. The drip irrigation system is set up in a PVsyst 7.1.7 simulation environment to pump 114.24 m3 of water daily from a 5-meter-deep river with a 1.8 kW photovoltaic system. The efficiency of the system is 58.7% and the efficiency of the pump is 34.5%. Crop water need is met at the rate of 98.87%. It is predicted that the designed and analyzed PVDIS will provide efficiency in energy and water resources.

Estimation of Arecanut Crop Evapotranspiration Rate using Remote Sensing

Arecanut is a plantation crop sustains for decades and its crop water demand varies with the age. For scheduling and management of irrigation water, crop water requirement information is important. To calculate the crop water requirement, estimation of evapotranspiration is crucial. The term Evapotranspiration (ET) refers to transport of water molecules into the atmosphere from soil (soil evaporation) and vegetation (transpiration) surfaces. It is a most important component of hydrological cycle and also the most difficult factor to quantify. Crop water need is the amount of water required for balancing loss due to evapotranspiration. There are different methods proposed by researchers for the estimation of evapotranspiration. The conventional methods of evapotranspiration estimation from ground data are tedious. The advancement in remote sensing data provides estimation of evapotranspiration in a global scale. The invention of thermal remote sensing has benefitted greatly since it reduces the field data requirement for estimation of ET. It also helps to understand spatial distribution of landmass and different estimates also in estimation of evapotranspiration over a larger extent timely and periodically. In this study to estimate Arecanut crop evapotranspiration Hargreaves Samani, Penman Monteith and Priestly Taylor methods were used and compared. Arecanut crop evapotranspiration rate estimated form Landsat 8 and MODIS data are showed similar range of values between 3 to 4.45 mm/day. The study area covers an area of 835.3 hectares of Arecanut crop and the gross crop water need is found to be 23059 m3.

Estimation of Arecanut Crop Evapotranspiration Rate using Remote Sensing

Arecanut is a plantation crop sustains for decades and its crop water demand varies with the age. For scheduling and management of irrigation water, crop water requirement information is important. To calculate the crop water requirement, estimation of evapotranspiration is crucial. The term Evapotranspiration (ET) refers to transport of water molecules into the atmosphere from soil (soil evaporation) and vegetation (transpiration) surfaces. It is a most important component of hydrological cycle and also the most difficult factor to quantify. Crop water need is the amount of water required for balancing loss due to evapotranspiration. There are different methods proposed by researchers for the estimation of evapotranspiration. The conventional methods of evapotranspiration estimation from ground data are tedious. The advancement in remote sensing data provides estimation of evapotranspiration in a global scale. The invention of thermal remote sensing has benefitted greatly since it reduces the field data requirement for estimation of ET. It also helps to understand spatial distribution of landmass and different estimates also in estimation of evapotranspiration over a larger extent timely and periodically. In this study to estimate Arecanut crop evapotranspiration Hargreaves Samani, Penman Monteith and Priestly Taylor methods were used and compared. Arecanut crop evapotranspiration rate estimated form Landsat 8 and MODIS data are showed similar range of values between 3 to 4.45 mm/day. The study area covers an area of 835.3 hectares of Arecanut crop and the gross crop water need is found to be 23059 m 3 .

Theory and Development of a VRI Decision Support System: The USDA-ARS ISSCADA Approach

HighlightsMulti-faceted research efforts converged to an automated irrigation decision support system (DSS).Low-cost, solar-powered, wireless plant abiotic and biotic stress sensors were developed to aid the DSS.Low-cost, accurate TDR soil water sensors and a wireless node and gateway system were developed for the DSS.Sensor systems and research-based algorithms were integrated into an automated irrigation DSS and control system.Abstract. Variable-rate irrigation (VRI) is now possible with every new center pivot irrigation system sold, either using sector (speed) control or both sector and zone (radial along the pipeline) control. However, decision support systems able to generate a prescription for spatially varying irrigation based on crop water need have lagged far behind VRI equipment. Irrigation based on crop water need has been shown to increase both crop water productivity and nutrient use efficiency, meaning that an effective VRI decision support system (DSS) could improve profitability while conserving resources. In this article, we report separately on a VRI DSS using sensor-based plant and soil water feedback as implemented in four U.S. states. This article describes the genesis and development of the Irrigation Scheduling Supervisory Control and Data Acquisition (ISSCADA) system, of the integral plant and soil sensors, and of its wireless sensor network subsystems, as well as the role of multi-location research efforts and cooperative research and development agreements in the development of the needed plant and soil sensors and the ISSCADA and wireless sensor network systems. Keywords: Crop water productivity, Decision support system, Product development, Sensors, Variable-rate irrigation, VRI.

Projection of near-future climate change and agricultural drought in Mainland Southeast Asia under RCP8.5

In order to plan for agricultural irrigation, the drought risk and amount of water needed for crops must be well studied. In this work, we apply the Standard Precipitation Index (SPI) and Crop Water Need (CWN) using input data from a fine-resolution Nested Regional Climate Model (NRCM) to assess the risk of future agricultural drought in Mainland Southeast Asia from 2020 to 2029. The NRCM was performed with resolutions of 60 and 10-km grid spacing for the present (1990–1999) and the future (2020–2029). The model employs initial and boundary conditions from the Community Climate System Model Version 4 (CCSM4) for meteorological variables. Two simulations, present-day (1990–1999) and future (2020–2029), were conducted under the Representative Concentration Pathway (RCP) 8.5 climate scenario. In general, the comparison between the NRCM predictions and observed data shows that the NRCM reasonably predicts precipitation and 2-m temperature with a high correlation of 0.89–0.98 and index of agreement (IOA) values ranging from 0.76 to 0.95. The future precipitation tends to decrease by (−1)–(1) mm/day, while the temperature will increase by up to 2–3 °C, which are favorable conditions for drought risk. Additionally, the SPI values between (− 1.5) and 0 for both the dry and rainy seasons indicate a high possibility of drought events in the future. There seemed to be some evidence of drought risk in this region, but the calculation of CWN indicates that the region will remain relatively water rich for agriculture.

Growth-stage-specific crop coefficient and consumptive use of Capsicum chinense using hydraulic weighing lysimeter

This research was conducted to determine growth stage-specific crop coefficient (Kc) and Consumptive Use (CU), for Capsicum chinense (Ata rodo) grown under irrigation in Omu-Aran, North central Nigeria. Daily CU and growth stage-specific crop coefficient Kc for Capsicum chinense was measured using a developed and calibrated hydraulic weighing lysimeter over 2 consecutive dry seasons (2015/16 and 2016/17). The values of CU and growth stage-specific Kc obtained were compared to that estimated using CROPWAT 8.0 and with existing Food and Agricultural Organization (FAO) Kc values for bell pepper. The Kc values determined over the growing seasons varied from 0.32 to 0.7, 1.02 to 1.45 and 0.76 to 0.9 representing Kc initial, Kc mid-season and Kc late season (Kcini, Kcmid and Kcend), respectively. These Kc varied significantly (P < 0.05) from FAO established Kc values. The observed Kc was validated using relevant statistical methods. The seasonal CU of Capsicum chinense was 580.01 ± 54 mm, while the reference evapotranspiration increased from 3.26 mm day−1 in October to 4.19 mm day−1 in February. The crop factor increased from 0.7 in October to attain its peak of 1.45 in January, but decreased to 0.9 during crop senescence in February. Likewise, the daily crop water need was 2.28 mm day−1 in October but reached its peak of 5.82 mm day−1 in January and thereafter decreased to 3.77 mm day−1 in February. Mean monthly water need of Capsicum chinense pepper was 68.36 mm month−1 in October. The water need of crop rose to its peak of 166.32 mm month−1 in December, and thereafter declined to 113.13 mm month−1 in February It is inferred that the application of growth stage-specific Kc will assist in irrigation management and would serve as useful guide for precise water applications for areas around Omu-Aran as it differs from region to region.

Advanced Monitoring and Management Systems for Improving Sustainability in Precision Irrigation

Globally, the irrigation of crops is the largest consumptive user of fresh water. Water scarcity is increasing worldwide, resulting in tighter regulation of its use for agriculture. This necessitates the development of irrigation practices that are more efficient in the use of water but do not compromise crop quality and yield. Precision irrigation already achieves this goal, in part. The goal of precision irrigation is to accurately supply the crop water need in a timely manner and as spatially uniformly as possible. However, to maximize the benefits of precision irrigation, additional technologies need to be enabled and incorporated into agriculture. This paper discusses how incorporating adaptive decision support systems into precision irrigation management will enable significant advances in increasing the efficiency of current irrigation approaches. From the literature review, it is found that precision irrigation can be applied in achieving the environmental goals related to sustainability. The demonstrated economic benefits of precision irrigation in field-scale crop production is however minimal. It is argued that a proper combination of soil, plant and weather sensors providing real-time data to an adaptive decision support system provides an innovative platform for improving sustainability in irrigated agriculture. The review also shows that adaptive decision support systems based on model predictive control are able to adequately account for the time-varying nature of the soil–plant–atmosphere system while considering operational limitations and agronomic objectives in arriving at optimal irrigation decisions. It is concluded that significant improvements in crop yield and water savings can be achieved by incorporating model predictive control into precision irrigation decision support tools. Further improvements in water savings can also be realized by including deficit irrigation as part of the overall irrigation management strategy. Nevertheless, future research is needed for identifying crop response to regulated water deficits, developing improved soil moisture and plant sensors, and developing self-learning crop simulation frameworks that can be applied to evaluate adaptive decision support strategies related to irrigation.

FAO-CROPWAT Model-Based Estimation of Crop Water Need and Appraisal of Water Resources for Sustainable Water Resource Management:Pilot Study for Kollam District - Humid Tropical Region of Kerala, India

FAO-CROPWAT Model-Based Estimation of Crop Water Need and Appraisal of Water Resources for Sustainable Water Resource Management:Pilot Study for Kollam District - Humid Tropical Region of Kerala, India

Comparison of Water Use Efficiency in Alfalfa Using Water and Waste Water

Growth of cultural and economic level of societies has caused conversion of villages to cities and establishment of wastewater treatment plant in cities. Wastewater treat- ment plants discharge significant amounts of wastewater into natural waterways and their output at the most critical time (the hottest day that crop water need reaches to maximum due to physiological conditions) reaches to the highest amount. Hence, with regard to permanent and accessible wastewater and study on performance and production of forage per water or wastewater consumption unit, the research project was performed in form of statistical plan of totally random blocks with four treatments in three replications during three years. The results indicate that function of forage at the third year of project implementation has increased than the first and second years. Treatments 2 and 4 (the treatments to use wastewater) had greater function than treatments 1 and 3 (treatments that have irrigated with well water), found with significant difference. In average, function of forage (dry and wet) in treatments irrigated with wastewater than the treatments irrigated with well water has been greater to 37 and 32 percent regardless of consumption amount. Water con- sumption efficiency in the treatments to use wells water and wastewater equals to 0.867 and 1.09 kg production of dry hay per cubic meter of water or wastewater.

Yield and Essential Oil Response in Coriander to Water Stress and Phosphorus Fertilizer Application

Coriander (Coriandrum sativum L.) contains essential oil, and various extracts from the seeds possess anti-bacterial, antioxidant, anti-diabetic, anti-cancerous and anti-mutagenic activities. Field experiments were carried out to study the effect of phosphorus (P) fertilization (0, 12 and 24 kg-ha−1 P) on the seed yield, and volatile oil of Egyptian cultivar of coriander under water stress conditions. The oil was obtained by hydrodistillation extraction method using Clevenger apparatus. Chemical analysis of the oil was done by using gas chromatography and mass spectrometry (GC-MS). Increasing water amounts and P fertilization rates increased plant height, number of branches, number of umbels and seed yield. Essential oil yields increased significantly with increasing water regimes from 50 % to 100 % of ET crop. Irrigation applied at 100 % of crop water need, combined with 24 kg-ha−1 P, gave the best seed and oil yields results. Coriander plants have high water use efficiency (WUE) at 50 % ET crop, and 24 kg-ha−1 P whilst irrigation at 100 % ET crop without any phosphorus application gave the lowest WUE. Concerning essential oil constituents, linalool was the major compound in coriander fruits.

An experiment to gauge an ungauged catchment: rapid data assessment and eco-hydrological modelling in a data-scarce rural catchment

We conducted a PUB (predictions in ungauged basins) experiment looking at hydrology and crop dynamics in the semi-arid rural Mod catchment in India. The experiment was motivated by the aims (a) to develop a coupled eco-hydrological model capable of analysing land-use strategies concerning crop water need, erosion protection, crop yield and resistivity against droughts and floods, and (b) to assess the feasibility of a strategy for collecting the necessary data in a data-scarce region. Our experiment combines parsimonious data assessment and eco-hydrological model coupling at the lower mesoscale. Linking bottom-up sampling of functionally representative soil classes and top-down regionalization based on spectral properties of the same resulted in a comprehensive distributed data basis for the model. A clear focus on the dominating processes and the catena as the organizing landscape element in the given environmental setting enabled this. We employed the WASA (Water Availability in Semi-Arid environments) model for uncalibrated process-based water balance modelling and integrated a crop simulation subroutine based on the SWAP (Soil Water Atmosphere Plant) model to account for crop dynamics, feedbacks and yield estimation. While we found the data assessment strategy and the hydrological model application largely feasible, in terms of its accounting for scale, processes and model concepts, the simulation of feedbacks with crops was problematic. Contributing to the PUB issue, more general conclusions are drawn concerning spatially-distributed structural information and uncalibrated modelling.Editor Z.W. Kundzewicz; Associate editor F. Hattermann