Irrigation Scheduling Techniques Research Articles

Predictive Models for Optimal Irrigation Scheduling and Water Management: A Review of AI and ML Approaches

Purpose: Maintaining agricultural output, protecting water supplies, and lessening environmental effects all depend on effective water management. Through a comprehensive review of the literature and an in-depth analysis of various AI and ML techniques, this paper aims to put light on the cutting-edge approaches used in irrigation scheduling predictive modeling. The goal of the research is to determine the advantages, disadvantages, and future directions of AI and ML-based irrigation management systems by means of a methodical analysis of various algorithms, data sources, and applications. Additionally, the study seeks to demonstrate how data-driven methods can enhance irrigation systems' sustainability, accuracy, and precision. Stakeholders in agriculture, water resource management, and environmental conservation can make well-informed decisions to maximize irrigation scheduling techniques by having a thorough understanding of the theoretical underpinnings and practical applications of predictive models. The study also attempts to tackle issues like scalability, model interpretability, and lack of data when implementing AI and ML solutions for practical irrigation management. In final form, this review's conclusions advance our understanding of how to use AI and ML to improve agricultural systems' resilience and water use efficiency, supporting adaptive and sustainable water management strategies in the face of rising water scarcity concerns and climate change. Design/Methodology/Approach: In order to gather information for this review study, several research articles from reliable sources were analyzed and compared. Objective: To provide the current research gaps in prediction models for the best irrigation scheduling and water management, and suggest using AI and ML techniques to fill in these gaps. Results/ Findings: In response to the growing challenges of water scarcity and climate change, the paper's findings highlight the transformative potential of AI and ML techniques in optimizing irrigation scheduling, enhancing agricultural resilience, increasing water use efficiency, and supporting adaptive and sustainable water management strategies. Originality/Value: This paper's uniqueness and significance come from its thorough analysis of AI and ML approaches in predictive modeling for ideal water management and irrigation scheduling. It also provides insights into new methods and their possible effects on resource optimization and agricultural sustainability. Type of Paper: Literature Review.

Farmland mulching and optimized irrigation increase water productivity and seed yield by regulating functional parameters of soybean (Glycine max L.) leaves

In both arid and semi-arid regions, adopting field mulching can effectively optimize soil moisture distribution, enhance crop yields, and improve water productivity. While acknowledging its advantages, field mulching seems insufficient for maintaining high crop productivity due to the increasing frequency of extreme weather. Furthermore, drought often coincides with critical crop growth stages, necessitating the implementation of agricultural irrigation to ensure normal crop growth. Accordingly, we conducted a three-year field experiment from 2021 to 2023 including three typical field mulching methods (no mulching, NM; straw mulching, SM; plastic film mulching, FM) and three supplementary irrigation strategies (irrigated at the branching stage (V4), W1; irrigated at the pod-filling stage (R2), W2; irrigated at both the V4 and R2 stage, W3). Throughout the entire growth period, we monitored soil moisture conditions for each treatment, measured leaf physiological parameters at crucial growth stages, and assessed soybean yields and water productivity (WP). Our findings indicated that, relative to SM and NM, FM maintains optimal soil moisture balance, augments chlorophyll content, and enhances photosynthesis, resulting in an average yield increase of 17.0% and 38.3% over three growing seasons. Additionally, supplementary irrigation also significantly affects the growth and seed yield of soybean. FMW2 achieved the higher seed yield (4307.5 kg ha−1, 3-year averaged), had insignificant difference with the highest seed yield of 4568.6 kg ha−1, both significantly higher than other treatments. Similarly, the leaf area index, chlorophyll content, net photosynthetic rate (Pn) and transpiration rate (Tr) also presented insignificant difference between FMW2 and FMW3, while WUEleaf (Pn/Tr) of FMW2 obviously higher than that of FMW3. As a result, FMW2 achieved the highest WP of 12.2 kg ha−1 mm−1 over the three growing seasons, compared to the three-year average of the other treatments, the increase ranges from 5.6% to 46.7%. In summary, the FMW2 treatment optimized water distribution to meet the water demands of soybeans during the reproductive growth stages, achieving a beneficial balance between soybean seed production and WP by regulating leaf functional parameters. Future research will explore more specific irrigation scheduling techniques (e.g., precision irrigation, deficit irrigation, and sensor-based irrigation management systems) while integrating innovative agricultural film materials (e.g., biodegradable films) to further enhance crop resilience and productivity under evolving climatic conditions.

Assessing the value of deep reinforcement learning for irrigation scheduling

Due to increasing global water scarcity pressure, researchers, policy makers and industry are looking for innovative solutions to increasing agricultural water productivity. Motivated by recent success within complex decision-making environments, Deep Reinforcement Learning (DRL) is being proposed as a method for optimizing irrigation strategies. Early research has hinted towards increased profits with DRL compared to heuristic approaches such as soil-moisture thresholds or fixed schedules. However, an assessment of the value of DRL for irrigation scheduling that incorporates local climate variability and water-use restrictions has yet to be performed. To address this gap in the literature, we created aquacrop-gym, an open-source Python framework for researchers to train and evaluate customized irrigation strategies within the crop-water model AquaCrop-OSPy. In this analysis, aquacrop-gym was used to quantify the value of DRL in comparison to conventional irrigation scheduling techniques (e.g., optimized soil-moisture heuristic) for maize production in an intensively irrigated region of the central United States. The DRL and heuristic approaches were both trained on 70 years of weather data produced from the weather generator LARS-WG, and evaluated on 30 unseen validation years of generated weather data. Findings from this analysis show that in the presence of high rainfall variability, DRL does not outperform conventional optimized heuristics. However, in the scenario where rainfall is set to zero, DRL approaches achieve higher profits on the unseen validation years. Similarly, DRL approaches also outperform optimized heuristics when severe water-use restrictions are introduced. Our analysis demonstrates that DRL approaches are a promising method of irrigation scheduling, notably in regions where farmers are faced with significant physical or regulatory water scarcity.

Determination of the Most Efficient Forage Sorghum Irrigation Scheduling Strategies in the U.S. Central High Plains Using the AquaCrop Model and Field Experiments

The current status of water resources in the U.S. Central High Plains necessitates adopting water conservation practices to move toward a sustainable agricultural economy. Identifying proper irrigation scheduling techniques is a conservative practice to maintain the sustainability of the agricultural systems. However, conducting field experiments is time and money consuming. Thus, the utilization of crop models, such as AquaCrop, could be a convenient alternative to field experiments. The FAO AquaCrop model was calibrated and validated for simulating forage sorghum yield response to various deficit irrigation conditions in a semi-arid region. Afterwards, the model was used to investigate the efficiency of the pre-season and in-season irrigation scheduling scenarios. In this study, the soil water status at the planting time was considered as the indicator of the pre-season irrigation level. Therefore, the pre-season irrigation scenarios were arranged as the replenishment of soil water deficiency at the time of planting at up to 30, 50, and 100% of the soil’s total available water for the first 60 cm of soil depth and the same replenishment levels for the entire crop root zone (150 cm soil depth). Then, AquaCrop long-term (37 years) simulations of forage sorghum biomass and irrigation water use efficiency reactions to three levels of maximum allowable depletion (MAD) (40, 55, and 70%) were compared to three fixed irrigation interval (4, 6, and 10 days) scenarios by considering six pre-season irrigation conditions (36 scenarios). The scenarios analysis found the 10-day irrigation interval and the MAD levels of 55% and 70% to be the most efficient irrigation scheduling strategies if combined with pre-season irrigation that brought the crop root zone (0–150 cm soil depth) to field capacity. Moreover, the 40% MAD application was the least efficient strategy. This study’s outputs can be a baseline for establishing forage sorghum irrigation scheduling in the U.S. Central High Plains. However, exploring the interactions of irrigation scheduling strategies with other irrigation and agronomic practices, such as salinity management and fertilizer application, is highly recommended.

Irrigation Scheduling Effect on Water Requirements

Irrigation scheduling techniques is one of the suggested solutions for water scarcity problem. The study aims to show the possibility of using practical and applicable irrigation scheduling program which was designed by Water Resources Department at the University of Baghdad by using Spreadsheet Formulas for Microsoft Excel program, version 2007, with some modification to generalize it and made it applicable to various climatic zone and different soil types, as a salvation for the shortage of irrigation water inside the irrigation projects. Irrigation projects which incidence of Tigris River basin will be taken as an applicable example. This program was based on water budgeting and programmed depending on scientific concepts which facilitate irrigation structures operation and ease the use by farmers. By using the abilities of this program, the monthly and annually water requirements and drainage water were estimated. Finally a comparison is made between the calculated discharges with the designers suggested ones. This comparisons showed that the use of this type of irrigation scheduling (i.e. predicted irrigation scheduling) with itsapplicable constrains require high attention when choosing the cropping pattern for each climate zone. Also it found that this irrigation program is a useful tool for saving water if cropping pattern has been chosen carefully.

Effects of Irrigation Methods and Scheduling on Water and Onion Productivity in Semi-Arid Areas of Ethiopia

Highlights Irrigation experiments on onion productivity were conducted at two sites for two consecutive years in Ethiopia. Factors from irrigation interval and irrigation method analyzed independently and factorially. Basin irrigation and fixed irrigation produced higher bulb yields and water productivity. Farmers’ income can be enhanced using improved irrigation practices for increased onion productivity. Abstract. How to meet the crop water demand and improve crop productivity is a particular concern for small-scale farmers, where the availability of water resources is limited. This study evaluated three different irrigation methods (furrow, basin and border) and two types of irrigation scheduling (CROPWAT schedule and farmers’ practices) with three replications for two consecutive years (2016-2017) at two sites using onion crops. The results showed that the CROPWAT schedule, basin irrigation method, and their interactions showed better performances and produced higher yield and water productivity. An average of 26 and 27 metric tons/hectare of onion were obtained under the basin irrigation method and basin irrigation with CROPWAT schedule combined, respectively. The water productivity (WP) and irrigation water productivity (IWP) also showed higher results under the basin irrigation method compared to other treatment combinations. The basin irrigation method produced higher marketable onion bulbs with firm medium bulb sizes that are essential for onion producers to earn maximum profit. The findings of this study also indicate that focusing on enhanced irrigation scheduling techniques and irrigation methods is paramount for better onion productivity in irrigation water-limited areas. Keywords: Irrigation interval, Irrigation methods, Irrigation practices, Onion productivity, Water management.

The Complexity of Modulating Anthocyanin Biosynthesis Pathway by Deficit Irrigation in Table Grapes.

Deficit irrigation (DI) is an irrigation scheduling technique that is used in grapes to improve red color development; however, results are not always satisfactory in table grapes. The red color in grapes is mainly due to the plant pigment anthocyanin. In the present study, the anthocyanin biosynthesis in Scarlet Royal grapes (Vitis vinifera L.) grown in the San Joaquin and Coachella Valleys, and subjected to two different DI strategies was investigated. The objective of this study was to identify potential regulatory factors that may lead to potential treatments to improve red color in table grapes, especially under warm climate conditions. In both locations, DI induced the expression of several genes involved in three major pathways that control the red color in table grapes: anthocyanin biosynthesis, hormone biosynthesis, and antioxidant system. DI at veraison induced anthocyanin accumulation and enhanced red color in berries at harvest time. However, anthocyanin accumulation was lower at the Coachella Valley compared to the San Joaquin Valley. The lower level of anthocyanin was associated with lower expression of critical genes involved in anthocyanin biosynthesis, such as flavonoid-3-O-glucosyltransferase (UFGT), myb-related regulatory gene (R2R3-MYB) (MYBA1), basic helix-loop-helix (bHLH) (MYCA1) and the tryptophan-aspartic acid repeat (WDR or WD40) proteins (WDR1). Further, gene expression analysis revealed the association of ABA biosynthesis gene 9-cis-epoxycarotenoid dioxygenase (NCED1), 1-aminocyclopropane-1-carboxylic acid oxidase (ACO3), and the gibberellic acid (GA) catabolic gene GA2 oxidase (GA2ox1) in the induction of anthocyanin biosynthesis. An increase in the chalcone synthase gene (CHS2) was observed in response to DI treatments in both sites. However, CHS2 expression was higher in Coachella Valley after ending the DI treatment, suggesting the involvement of environmental stress in elevating its transcripts. This data was also supported by the lower level of antioxidant gene expression and enzyme activities in the Coachella Valley compared to the San Joaquin Valley. The present data suggested that the lack of grape red coloration could partially be due to the lower level of antioxidant activities resulting in accelerated anthocyanin degradation and impaired anthocyanin biosynthesis. It seems that under challenging warmer conditions, several factors are required to optimize anthocyanin accumulation via DI, including an active antioxidant system, proper light perception, and hormonal balance.

ET-Based Irrigation Scheduling for Papaya (Carica papaya) in Florida

Three irrigation scheduling methods (set schedule, ET-based, and tensiometer-based) were tested for papaya production in south Florida. ET-based irrigation scheduling was found to conserve water effectively. This 6-page document primarily focuses on the ET-based irrigation scheduling techniques for papaya under Florida conditions. Written by Haimanote K. Bayabil, Jonathan H. Crane, Kati W. Migliaccio, Yuncong Li, and Fredy Ballen, and published by the UF/IFAS Department of Agricultural and Biological Engineering, March 2020. https://edis.ifas.ufl.edu/ae540

Performance Evaluation of Sesame under Regulated Deficit Irrigation Application in the Low Land of Western Gondar, Ethiopia

Sesame (Sesamum indicum L.) is the leading oil seed crop produced in Ethiopia. It is the second most important agricultural commodity for export market in the country. It is well suited as an alternative crop production system, and it has low crop water requirement with moderate resistance to soil moisture deficit. The low land of North Western Ethiopia is the major sesame producer in the country, and the entire production is from rainfed. The rainfall distribution in North Western Ethiopia is significantly varied. This significant rainfall variability hampers the productivity of sesame. Irrigation agriculture has the potential to stabilize crop production and mitigate the negative impacts of variable rainfall. This study was proposed to identify critical growth stages during which sesame is most vulnerable to soil moisture deficit and to evaluate the crop water productivity of sesame under deficit irrigation. The performance of sesame to stage-wise and uniform deficit irrigation scheduling technique was tested at Gondar Agricultural Research Center (Metema Station), Northern Western Ethiopia. Eight treatments, four stage-wise deficit, two uniform deficit, one above optimal, and one optimal irrigation applications, were evaluated during the 2017 irrigation season. The experiment was designed as a randomized complete block design with three replications. Plant phenological variables, grain yield and crop water productivity, were used for performance evaluation. The result showed that deficit irrigation can be applied both throughout and at selected growth stages except the midseason stage. Imposing deficit during the midseason gave the lowest yield indicating the severe effect of water deficit during flowering and capsule initiation stages. When deficit irrigation is induced throughout, a 25% uniform deficit irrigation can give the highest crop water productivity with no or little yield reduction as compared with optimal irrigation. Implementing deficit irrigation scheduling technique will be beneficial for sesame production. Imposing 75% deficit at the initial, development, late season growth stages or 25% deficit irrigation throughout whole seasons will improve sesame crop water productivity.

Irrigation scheduling techniques and irrigation frequency on capsicum growth and yield

The present study aimed to evaluate different irrigation scheduling strategies on capsicum growth and yield inprotected environment. The experiment was carried out at the Northeastern of Brazil. Five irrigation scheduling techniques to define water depth (weighing lysimeter, Hargreaves-Samani equation, Piché evaporimeter, tensiometer and soil moisture sensor) andtwo application frequencies (F1-once a day and F2-alternating frequency) were tested. A completely randomized factorial design experiment was installed in a 5 x 2 factorial scheme, with eight replicates. It was observed that the variables stem diameter and leaf area index were influenced by the irrigation scheduling techniques, and treatments based on Hargreaves-Samani and lysimeter scheduling methods led to the lowest values. Fruit biometric parameters were significantly affected only by the Hargreaves-Samani treatment. It can be concluded that both irrigation scheduling techniques and frequencies influenced capsicum growth and yield. Furthermore, irrigation management techniques based on soil sensors caused the highest yields.

Analysis of the competitiveness of wheat and orange in Tunisia under water shortage scenarios

Increasing water shortage results in higher food prices which may affect the competitiveness of small irrigated farming systems. The purpose of this paper is to evaluate the effects of water shortage on the competitiveness of orange and wheat commodities in Tunisia. Stochastic production functions were used to estimate changes in the opportunity cost of water which will be utilized in the calculation of domestic resource cost (DRC). Primary data used for the empirical analysis were collected from 170 wheat and 51 orange farms located in the major irrigated areas of the country. Empirical results indicate that, in the current baseline situation, Tunisia has a comparative advantage in the production of orange but not for wheat production. Results of sensitivity analysis show that the competitiveness in wheat and orange production has been deteriorated due to a decrease in water availability at the farm level. DRC ratio increases from 0.91 to 1.13 for orange, and from 1.24 to 8.27 for wheat with 50% decreases in water availability. However, improving irrigation water use efficiency led to a significant decrease in DRC for both commodities. This finding urges decision-makers to put more efforts to optimize irrigation management at field and farm levels through dissemination of better irrigation scheduling techniques and implementing deficit irrigation strategies especially that the country is under a critical water shortage.

Modeling the water and nitrogen productivity of sunflower using OILCROP-SUN model in Pakistan

Water and nitrogen (N) limitations in soil usually restrict plant growth hence reducing water and nitrogen productivity. Precise irrigation application is vital in water scarce areas of the world, such as Pakistan. Water and N are generally the most important factors in high yielding of crops. This interaction between irrigation levels and N can be analyzed through field experiments in concert with crop simulation models and decision support systems. Recently the use of crop growth models in crop husbandry has become more common to provide the decision support for farmers. The proposed study was conducted at Agronomic Research Area, University of Agriculture Faisalabad, Pakistan during the years 2012 and 2013 to evaluate OILCROP-SUN model under decision support system for agro-technology transfer (DSSAT) for simulating growth, development and achene yield of sunflower hybrid Hysun-33. The experiment was laid out using split plot design with four irrigation levels (control, 45, 60 and 75mm potential soil moisture deficit) in main plots and three N rates (90, 120 and 150kgha−1) in sub plots. The model was first calibrated with control irrigation plus 150kgNha−1 treatment (I1N3), then evaluated (2012) and validated (2013) by utilizing experimental data. The evaluation and validation exhibited that the model simulated anthesis date, maturity date, maximum leaf area index (LAI), total dry matter (TDM), achene yield and oil contents very well with an error of 0–6.15%, 0–3.96%, −25.14 to 6%, −0.37 to 17.75%, −4.58 to 17.12% and −15.04 to 12.15%, respectively. Similarly, root mean square error (RMSE) values for soil water and leaf N contents were ranged from 0.03 to 0.05cm3cm−3 and 0.16% to 0.71%, respectively. While RMSE for crop evapotranspiration (ET) was 29.87mm in 2012 and 32.56mm in 2013. The highest achene yield and oil contents were achieved with the control irrigation in combination of 150kgNha−1 application. However saving of water (110mm in 2012 and 120mm in 2013) with 45mm potential soil moisture deficit (PSMD) treatment (without significant reduction in sunflower productivity) recognized it as most suitable irrigation scheduling technique under water scarce areas of the world. The results disclosed that OILCROP-SUN model can be efficaciously used for simulation of spring sown sunflower under semi-arid conditions of Pakistan.

Yield and water use efficiency of furrow irrigated potato under regulated deficit irrigation, Atsibi-Wemberta, North Ethiopia

The pressure on availability of water in Tigray regional state is likely to increase as the requirement for food production in couple with rapidly growing of the population is at increasing rate. Hence, improving water productivity using regulated deficit irrigation is important to reduce the water consumption while minimizing adverse effects on the crop yield. This study was conducted in the eastern zone of Tigray regional state, Atsibi Womberta district. The aim of this study was to determine the yield and water use efficiency of potato under deficit irrigation practice in the 2012 growing period. The effect of water deficit or water stress on crop yield and water use efficiencies were evaluated. Guasa variety potato was grown under eight (T1–T8) irrigation treatments. The treatments were replicated three times under completely randomized blocks experimental design. Water was applied to every furrow using watering can with fixed interval and variable depth irrigation scheduling technique was selected. Yield of potato was significantly (p<0.05) affected by water stress (deficit irrigation). The highest yield was found in T1 (18770kg/ha) which was not subjected to water stress (full water requirement) whereas minimum yield of potato was obtained under the fully stressed treatment T8 (7037kg/ha). There was no significant different between the yield of T1 (18770kg/ha) and T6 (14440kg/ha) which was 25% deficit throughout the growing season. According to the result obtained, stressed at the middle stage was affected more the yield of potato as compared to other treatments. This showed that stressing the crop at flowering/middle stage is sensitive to deficit irrigation. Giving 65% of crop water requirement throughout the growing season is better than stressing the crop only at the middle stage. The second sensitive period for water stress is the late crop stage. Crop water use efficiency was not statistically significant. Though it was not significantly different, T6 (2.86kg/m3) and T4 (1.60kg/m3) had the highest and the lowest water use efficiency respectively. This elaborated that applying 75% of crop water requirement has better water use efficiency than optimal or “no stress” irrigation. It can be conclude that using deficit irrigation is a good water management technique to save irrigation water without reducing the yield of potato. For dry land areas like The Tigray regional state of Ethiopia and other similar agro-ecology elsewhere in the world with scarce water and agricultural water management is very poor. The authors of this study would like to recommend farmers, water managers, water use associations and decision makers to use water efficiently using deficit irrigation and increase their agricultural production by expanding irrigable land with the same amount of water in a given irrigation scheme

Performance of simple irrigation scheduling calendars based on average weather data for annual ryegrass

Poor irrigation management in pastures can lead to yield and quality reduction as well as loss of income through extra pumping and leaching of nitrate fertiliser. A number of irrigation scheduling techniques of varying levels of sophistication have been developed over the years to address limited irrigation water availability and maximise productivity. Despite this, the adoption of irrigation scheduling tools by farmers remains low. The objective of this study was to assess the use of simple irrigation scheduling calendars based on average weather data to improve irrigation management in ryegrass. The calibrated Soil Water Balance (SWB) model was used to generate simple irrigation calendars and assess effectiveness for different scenarios by mechanistically simulating water dynamics and pasture growth. Scheduling irrigation using the calendars gave similar irrigation applications, water losses and yields compared to a more scientific real-time scheduling (in response to soil water depletion by the crop). While site-specific irrigation scheduling calendars can easily be generated by consultants and irrigators, even simpler monthly estimates of average daily water use can also be useful. Application of calendars by farmers is encouraged to improve water and nutrient use efficiency of irrigated pastures, if real-time irrigation scheduling is not employed.

Irrigation Scheduling Using Remote Sensing Data Assimilation Approach

Remote sensing and crop growth models have enhanced our ability to understand soil water balance in irrigated agriculture. However, limited efforts have been made to adopt data assimilation methodologies in these linked models that use stochastic parameter estimation with genetic algorithm (GA) to improve irrigation scheduling. In this study, an innovative irrigation scheduling technique, based on soil moisture and crop water productivity, was evaluated with data from Sirsa Irrigation Circle of Haryana State, India. This was done by integrating SEBAL (Surface Energy Balance Algorithm for Land)-based evapotranspiration (ET) rates with the SWAP (Soil-Water-Atmosphere-Plant), a process-based crop growth model, using a GA. Remotely sensed ET and ground measurements from an experiment field were combined to estimate SWAP model parameters such as sowing and harvesting dates, irrigation scheduling, and groundwater levels to estimate soil moisture. Modeling results showed that estimated sowing, harvesting, and irrigation application dates were within ±10 days of observations and produced good estimates of ET and soil moisture fluxes. The SWAP-GA model driven by the remotely sensed ET moderately improved surface soil moisture estimates suggesting that it has the potential to serve as an operational tool for irrigation scheduling purposes.

English

This study evaluated the wetting patterns around drip and subsurface irrigation systems (DI and SI), respectively with three irrigation scheduling techniques. The drip and subsurface irrigation systems were used to irrigate a tomato crop. The wetting patterns for each irrigation system and each irrigation scheduling technique was evaluated below the soil surface at different distances and depths from the emitter 24 and 48 h after irrigation. The soil moisture distribution patterns showed that the vertical movement of soil moisture was higher than the horizontal movement under both DI and SI systems. The overall wetted area, delimited by the wetting front was largest for the manually controlled irrigation scheduling with both DI and SI, and smallest for the Smart controller irrigation scheduling under both DI and SI systems. The effect of irrigation scheduling techniques on the water distribution pattern varied at different soil depths. Average coefficients of uniformity values for the DI and SI systems were 84.32 and 88.72%, respectively. The coefficients of uniformity for SI were higher by approximately 4.40% than for DI for all irrigation scheduling techniques, although there was variation in Cus values between the DI and SI systems with all three techniques. It can be concluded that a parameter related to the soil water content is an important consideration in estimation of soil wetting patterns with both DI and SI systems. The results of this research can be used in the design, operation, and management of DI and SI systems. Key words: Drip irrigation; subsurface irrigation; Soil water; Wetting pattern; coefficient of uniformity, Irrigation scheduling techniques.

Sensitivity of root pruned ‘Conference’ pear to water deficit in a temperate climate

Abstract The present study examines the need for irrigation in pear trees (Pyrus Communis, cv. ‘Conference’) under low evaporative demand conditions, like in Belgium, in order to maintain a consistent fruit yield and high fruit size. To determine the sensitivity of the pear yield under low evaporative demand conditions three different orchards were monitored. The study shows that a Ψsoil of −60 kPa during shoot growth has no effect on fruit yield but lower Ψsoil values induced a decline in both fruit size and total yield. Just as for arid environments a Ψstem of −1.5 MPa is related to negative yield responses. In dry conditions lower Ψsoil and Ψstem values were observed in root pruned trees compared to not root pruned trees in the same irrigation treatment, however without yield decline. In one orchard a biannual bearing tendency was observed after root pruning. Furthermore intensive Ψsoil measurements show a high variation in Ψsoil between orchards, and within an orchard. This underlines the need for irrigation management on a parcel level and the need for new irrigation scheduling techniques which take the spatial variation in the orchard into account.

Regulated deficit irrigation effects on growth, yield, grape quality and individual anthocyanin composition in Vitis vinifera L. cv. ‘Tempranillo’

Regulated Deficit Irrigation (RDI) is an irrigation scheduling technique, originally developed for fruit orchards, that has been successfully adapted for winegrape production. The aim of this work is to evaluate the effect of RDI in vegetative growth, yield and harvest quality in ‘Tempranillo’ vineyards grown under semiarid conditions typical in Mediterranean areas. Two RDI strategies were compared with conventional irrigation practices (CI). CI that consisted in a progressive increase in water deficit as summer progressed, whereas RDI strategies (RDI1 and RDI2) had in common a deficit period just after fruit set and, in RDI2, vines were subjected to an additional stress period shortly after veraison. The experiment was carried out in four consecutive seasons in a commercial vineyard. Water stress at the beginning of berry development resulted in an important reduction of both vegetative growth and berry weight in RDI strategies. No differences in sugar concentration were found between treatments, and RDI berries tended to have lower acidity. The most relevant effect of RDI strategies on grape quality was an increase in anthocyanin and phenolics concentration. In RDI1 berries, this increase was mainly an indirect consequence of smaller berry size. However, in the RDI2 treatment the higher berry anthocyanin concentration reported was not exclusively due to a reduction in berry size. Since harvest quality has been clearly improved by any of the RDI strategies in both years, it can be concluded that RDI constitutes an interesting technique to be applied in ‘Tempranillo’ vineyards grown in semiarid areas aiming to obtain high quality grape.

EVALUATION OF A CONTROL SYSTEM BASED ON THE IRRIGAS® SENSOR FOR IRRIGATION SCHEDULING OF HYDROPONIC TOMATO IN CEARÁ, BRAZIL

The study aimed to evaluate two irrigation scheduling techniques for hydroponic tomato production in Ceara, northeastern Brazil. The experiment was set up as a split-plot randomized block design, with six replications. As main factor, two irrigation scheduling techniques were tested: (a) Automatic scheduling using matric sensors, a closed loop irrigation control system based on the Irrigas ® sensor, was used to start irrigation whenever matric potential in the substrate reached -4 kPa. (b) Time clock scheduling - irrigation applications were programmed at fixed intervals and rates, according to drainage fraction observed on the previous day and water retention capacity of the substrate. Tomato hybrids ‘Miramar’ and ‘Ellen’ were evaluated in the subplots. The growing medium was coconut fiber in bags of 40 L. The automatic irrigation scheduling based on Irrigas ® sensors did not affect significantly the tomato marketable yield, fruit weight, and number of fruits per plant (p>0.05) as compared to time clock scheduling. The automatic irrigation control reduced water application by 6.3% as compared to time clock scheduling, without affecting pH and EC levels of the drainage solution. The tomato hybrid ‘Miramar’ presented significantly higher (p<0.01) marketable yield and number of fruits per plant than hybrid ‘Ellen’, which presented higher fruit weight.

Climate Change Impacts on UK Top and Soft Fruit Production

The impacts of climate change for UK fruit growing regions requiring winter chill dormancy are discussed and insights are drawn from subtropical regions where chilling requirements for these perennial crops have been overcome. A further challenge facing UK soft fruit growers is water availability for irrigation. To maintain future productivity, more sustainable production systems are needed. The authors discuss recent advances in irrigation scheduling and deficit irrigation techniques, along with their potential to reduce water inputs while maintaining yields of high-quality, healthy berries.