The effects of deficit irrigation on nitrogen consumption,yield,and quality in drip irrigated grafted and ungrafted watermelon

The aim of this study is to determine the effects of deficit irrigation on nitrogen consumption,yield,and quality in grafted and ungrafted watermelon.The study was conducted in Cukurova region,Eastern Mediterranean,Turkey,between 2006 and 2008,and employed 3 irrigation rates（full irrigation（l_（100）） with no stress,moderate irrigation（Dl_（70））,and low irrigation（Dl_（50））;Dl_（70） and Dl_（50） were considered deficit irrigation） on grafted（CTJ,Crimson Tide＋Jumbo） and the ungrafted（CT,Crimson Tide） watermelon.The amount of irrigation water（IR） applied to the study plots were calculated based on cumulative pan evaporation that occurred during the irrigation intervals.Nitrogen consumption was 16%lower in CTJ plants than in CT plants.On the other hand,consumption of nitrogen was 28%higher in Dl_（50） plants than in Dl_（70） plants while it was 23%higher in Dl_（50） plants than in l_（100） plants.By grafting,the average amount of nitrogen content in seeds,pulps and peels for CTJ was 30,43 and 56%more than those of CT,respectively.The yield and the quality were not significantly affected by the deficit irrigation.In this respect,grafting of watermelon gave higher yield,but,it had a slight effect on fruit quality.The highest yield values of 16.90 and 19.32 kg plant^（-1） in 2008 were obtained with l_（100）and in CTJ plants,respectively.However,Dl_（50） treatment could be taken into account for the development of reduced irrigation strategies in semiarid regions where irrigation water supplies are limited.Additionally,the yield increased by applying CTJ treatment to the watermelon production.

Estimation of the van Genuchten Soil Water Retention Properties from Soil Textural Data

The van Genuchten (vG) function is often used to describe the soil water retention curve (SWRC) of unsaturated soils and fractured rock. The objective of this study was to develop a method to determine the vG model parameter m from the fractal dimension. We compared two approaches previously proposed by van Genuchten and Lenhard et al. for estimating m from the pore size distribution index of the Brooks and Corey (BC) model. In both approaches we used a relationship between the pore size distribution index of the BC model and the fractal dimension of the SWRC. A dataset containing 75 samples from the UNSODA unsaturated soil hydraulic database was used to evaluate the two approaches. The statistical parameters showed that the approach by Lenhard et al. provided better estimates of the parameter m. Another dataset containing 72 samples from the literature was used to validate Lenhard's approach in which the SWRC fractal dimension was estimated from the clay content. The estimated SWRC of the second dataset was compared with those obtained with the Rosetta model using sand, silt, and clay contents. Root mean square error values of the proposed fractal approach and Rosetta were 0.081 and 0.136, respectively, indicating that the proposed fractal approach performed better than the Rosetta model.

Simulating long-term effect of Hyrcanian forest loss on phosphorus loading at the sub-watershed level

Conversion of forest land to farmland in the Hyrcanian forest of northern Iran increases the nutrient input, especially the phosphorus（P） nutrient, thus impacting the water quality. Modeling the effect of forest loss on surface water quality provides valuable information for forest management. This study predicts the future impacts of forest loss between 2010 and 2040 on P loading in the Tajan River watershed at the sub-watershed level. To understand drivers of the land cover, we used Land Change Modeler（LCM） combining with the Soil Water Assessment Tool（SWAT） model to simulate the impacts of land use change on P loading. We characterized priority management areas for locating comprehensive and cost-effective management practices at the sub-watershed level. Results show that agricultural expansion has led to an intense deforestation. During the future period 2010–2040, forest area is expected to decrease by 34,739 hm^2. And the areas of pasture and agriculture are expected to increase by 7668 and 27,071 hm^2, respectively. In most sub-watersheds, P pollution will be intensified with the increase in deforestation by the year 2040. And the P concentration is expected to increase from 0.08 to 2.30 mg/L in all of sub-watersheds by the year 2040. It should be noted that the phosphorous concentration exceeds the American Public Health Association′s water quality standard of 0.2 mg/L for P in drinking water in both current and future scenarios in the Tajan River watershed. Only 30% of sub-watersheds will comply with the water quality standards by the year 2040. The finding of the present study highlights the importance of conserving forest area to maintain a stable water quality.

Clean Energy Consumption of Power Systems Towards Smart Agriculture: Roadmap, Bottlenecks and Technologies

Over the past decades,both agriculture and power systems have faced serious problems,such as the power supply shortage in agriculture,and difficulties of clean energy consump-tion in the power system.To address and overcome these issues,this paper proposes an idea to combine smart agriculture and clean energy consumption,use surplus clean energy to supply agriculture production,and utilize smart agriculture to support power system with clean energy penetration.A comprehensive review has been conducted to first depict the roadmap of coupling a agriculture-clean energy system,analyze their feasibilities and advantages.The recent technologies and bottlenecks are summa-rized and evaluated for the development of a combined system consisting of smart agriculture production and clean energy consumption.Several case studies are introduced to explore the mutual benefits of agriculture-clean energy systems in both the energy and food industries.

Effects of rates and time of zeolite application on controlling runoff generation and soil loss from a soil subjected to a freeze-thaw cycle

Many factors such as freeze-thaw(FT)cycle influence soil behavior.Application of soil amendments can play an important role on runoff time commencement(RT),volume(RV)and soil loss(SL)on soils subjected to FT cycles.However,limited studies have been documented on this subject.The present study was therefore carried out under rainfall simulation circumstances to investigate the effect of different rates of zeolite ap-plication to control the effects of FT on basic hydrological variables such as runoff production and soil loss.Towards this attempt,the effect of application of different rates of 250,500 and 750 gm^(-2) of zeolite applied before,during and after the occurrence of FT cycle on RT,RV and SL was assessed in a completely randomized design.Treatments were set up in two categories viz.control(without zeolite application),and three rates and times of zeolite application in small 0.25 m-experimental plots in three replications.The results showed that application of zeolite had significant effects on hydrological behavior of soil induced by FT cycles.Ap-plication rate of 750 gm^(-2) prior to FT cycle increased RT and reduced RV and SL at rates of 644%,68% and 91%,respectively.The results also verified that zeolite could successfully mitigate the impacts of FT cycle on the main soil hydrological variables of soil profile induced by FT cycle.It is accordingly recommended to employ zeolite as an effective amendment to control soil erosion in steep and degraded rangelands where surface soil is exposed to rainfall and runoff.

Estimating Water Retention with Pedotransfer Functions Using Multi-Objective Group Method of Data Handling and ANNs

Pedotransfer functions （PTFs） have been developed to estimate soil water retention curves （SWRC） by various techniques. In this study PTFs were developed to estimate the parameters （θs, θr, α and λ） of the Brooks and Corey model from a data set of 148 samples. Particle and aggregate size distribution fractal parameters （PSDFPs and ASDFPs, respectively） were computed from three fractal models for either particle or aggregate size distribution. The most effective model in each group was determined by sensitivity analysis. Along with the other variables, the selected fractal parameters were employed to estimate SWRC using multi-objective group method of data handling （mGMDH） and different topologies of artificial neural networks （ANNs）. The architecture of ANNs for parametric PTFs was different regarding the type of ANN, output layer transfer functions and the number of hidden neurons. Each parameter was estimated using four PTFs by the hierarchical entering of input variables in the PTFs. The inclusion of PSDFPs in the list of inputs improved the accuracy and reliability of parametric PTFs with the exception of ~s- The textural fraction variables in PTF1 for the estimation of a were replaced with PSDFPs in PTF3. The use of ASDFPs as inputs significantly improved a estimates in the model. This result highlights the importance of ASDFPs in developing parametric PTFs. The mCMDH technique performed significantly better than ANNs in most PTFs.

Modelling reference evapotranspiration using gene expression programming and artificial neural network at Pantnagar,India

Evapotranspiration is an essential component of the hydrological cycle that is of particular interest for water resource planning.Its quantification is helpful in irrigation scheduling,water balance studies,water allocation,etc.Modelling of reference evapotranspiration(ET0)using both gene expression programming(GEP)and artificial neural network(ANN)techniques was done using the daily meteorological data of the Pantnagar region,India,from 2010 to 2019.A total of 15 combinations of inputs were used in developing the ET0 models.The model with the least number of inputs consisted of maximum and minimum air temperatures,whereas the model with the highest number of inputs consisted of maximum air temperature,minimum air temperature,mean relative humidity,number of sunshine hours,wind speed at 2mheight and extra-terrestrial radiation as inputs and with ET0 as the output for all the models.All the GEP models were developed for a single functional set and pre-defined genetic operator values,while the best structure in each ANN model was found based on the performance during the testing phase.It was found that ANN models were superior to GEP models for the estimation purpose.It was evident from the reduction in RMSE values ranging from 2%to 56%during training and testing phases in all the ANN models compared with GEP models.The ANN models showed an increase of about 0.96%to 9.72%of R2 value compared to the respective GEP models.The comparative study of these models with multiple linear regression(MLR)depicted that the ANN and GEP models were superior to MLR models.