Minimum Irrigation Research Articles

Evaluating the tradeoffs between water conservation, aesthetic value, evaporative cooling and CO2 emissions in St. augustinegrass and buffalograss

Urban lawns comprise a significant portion of urban greenery and provide several ecosystem services. Nevertheless, maintaining lawns comes with significant water costs in semi-arid inland southern California, as they require consistent irrigation to stay healthy and productive. The main objective of this study was to evaluate the effect of a wide range of irrigation rates and frequencies applied autonomously by a smart ET-based controller on the aesthetic value, cooling potential, and CO2 efflux of two warm-season turfgrass species. For three years, we studied the responses of Buffalograss and St. Augustinegrass to six irrigation rates and two irrigation frequencies in Riverside, California. Under historical average climate conditions, the minimum irrigation rate for Buffalograss was 93 % ETo, and for St. Augustinegrass, it was 74 % ETo. Under projected future climate conditions, the estimated minimum irrigation rate for Buffalograss did not change, but for St. Augustinegrass, it increased by 4 % and 7 % in 2100 under low emission (RCP 4.5) and high emission (RCP 8.5) scenarios, respectively. On average, canopy minus air temperature in Buffalograss was 6.2 ℃, and in St. Augustinegrass, it was 1.1 ℃. The average CO2 efflux in Buffalograss was 122.3 µg CO2-C m−2 s−1, and in St. Augustinegrass, it was 182.8 µg CO2-C m−2 s−1. Our results showed that turfgrass aesthetic values, cooling potential, and CO2 efflux diminished as the irrigation rate decreased, but at different rates for each turfgrass species.

A Comprehensive IoT edge based smart irrigation system for tomato cultivation

Agriculture industry is the primary engine for a country's economic development. Growing crops using minimum irrigation water is a major challenge for farmers. In conventional farming, crops may be affected by various diseases due to inadequate irrigation scheduling. Recent proposals have suggested using Edge-IoT, AI, and distributed computing to accelerate the inference procedure utilized in smart irrigation applications. The use of resource-constrained edge servers and edge devices used to deliver smart agriculture applications can cause latency-sensitive workloads to interfere with one another. To address this issue, we design a long-range (LoRa) edge IoT computing-based sustainable and customized smart irrigation framework to capture the real-time data of tomato plants. This helps in automatic underground drip irrigation scheduling. This also predicts total water demand and usage, and measure plant growth status. The edge-IoT cloud data transmission control and optimization has been enforced using Smart irrigation data optimization and robust transmission (SIDORT) Message Queuing Telemetry Transport (MQTT) system. We develop a hybrid algorithm named Linked least traversal (LLT) for machine-to-machine communication (M2M). Also, a Reinforcement learning (RL) based Optimal Soil Wetness Closeness Policy (OSWCP) for irrigation scheduling has been proposed. The performance of the proposed smart irrigation models has been validated through extensive experiments using real-time data in which OSWCP performance has been measured at a 97.88 % accuracy rate. Additionally, a comparison of our proposed architecture has been accomplished by resolving the existing smart irrigation system challenges.

Effect of Irrigation Levels and Straw Mulching on Yield and Water Use Efficiency of Papaya under Drip Irrigation System

Papaya (Carica papaya L.) is a significant fruit crop cultivated worldwide in tropical and subtropical regions. However, its cultivation is often constrained by limited water availability and soil moisture stress, which may significantly affect its yield and quality. This study aimed to explore the effects of irrigation levels and straw mulch on the growth and yield of papaya under a drip irrigation system. The experiment encompassed six treatments: three different irrigation levels (100%, 75%, and 50% of crop water requirement) and two mulch levels (straw mulching and no mulch). The effect of mulch levels on plant growth, yield, and water use efficiency was statistically significant. The results revealed that applying 100% of crop water requirement along with straw mulch resulted in the maximum plant height, stem girth, crown diameter, and yield compared to the no mulch treatment. The study also evaluated irrigation water use efficiency, an essential aspect of sustainable farming practices. The results indicated that the maximum irrigation water use efficiency (58.47 kg m-3) was recorded when 50% of the crop water requirement was met alongside straw mulch. Conversely, the minimum irrigation water use efficiency was observed when 100% of the crop water requirement was completed without any mulch under the drip irrigation system.

Diverse water management in a preceding wheat crop does not affect maize yield but increases inter-annual variability: A six-year field study

As maize-based crop rotation systems are essential to ensure global food security, understanding the effect of the preceding crops and the associated management on the subsequent maize is crucial. Here we address this view with a 6-year field experiment in the North China Plain's annual winter wheat and summer maize rotation system. Effects of preceding wheat with three water management (conventional irrigation, Con.W/M; optimized irrigation, Opt.W/M; minimum irrigation, Min.W/M) on maize yield and water consumption are investigated. We find that water management in the preceding wheat crop does not affect the subsequent maize yield but increases the interannual yield variability. Over the six years, an average yield of > 10.7 Mg ha-1 is achieved for maize in the three treatments. The highest coefficient of variation (CV) of yield is observed in Min.W/M (12.0%), while there is no significant difference in CV between Con.W/M and Opt.W/M. The annual water consumption follows a decreasing trend in the order of Con.W/M (357 mm), Opt.W/M (332 mm), and Min.W/M (290 mm), which primarily attribute to variations in initial water storage in the deeper soil profile (below 80 cm). The variation in water storage would be eliminated during the following maize growing stages due to ample rainfall in the temperate continental monsoon climate. The findings show that kernel weight (KW) is the primary determinant (accounting for 55–86%) to the observed variations in maize yield. In the Min.W/M treatment, the interactive effect of killing degree days, growing degree days, maximum temperature, and solar radiation accounts for 85% of the variation observed in KW. This study provides insights into the effect of the preceding wheat on the subsequent maize for grain yield and water consumption in the rotation system.

Kebutuhan Air Irigasi pada D.I Krueng Pasee Aceh Utara

Water demand is the amount of water volume that must be met for irrigation, in the Krueng Pasee irrigation area of North Aceh has an irrigation network area of 5579 Ha. The purpose ofthis research is to obtain the maximum and minimum irrigation water demand discharge in the study area. This research can be an additional source of knowledge for writers in particular and for readers in general. As well as the results of this study can be known the value of rice planting water needs. The data used in the research is rainfall data sourced from BMKG (Meteorology, Climatology, and Geophysics Agency) Malikussaleh, used to calculate effective rainfall and climate data sourced from BMKGMalikussaleh in the form of temperature data, solar irradiation data, wind speed data and humidity or temperature data used to calculate evapotranspiration (ETo). ETo is a combination of evaporation and transpiration which is defined as the event of water loss from plant tissue and the surface of the soil usedas a place to grow plants. In analyzing ETo using the Penman Modification Methodology. The Penman method by adjusting the wind function according to local conditions produces the best approach to the evapotranspiration rate of the lysimeter results. The consumptive use of plants (ETc) during the land preparation period uses the results of the calculation of water requirements for land preparation (IR). Crop consumptive use (ETc) after land preparation is a calculation between evapotranspiration and cropcoefficient. The calculated irrigation water requirement (NFR) results in water requirement in the form of mm/day. Irrigation water requirements are converted to l/dt/ha with a conversion rate of 1/8.64. The smallest water demand calculation discharge was obtained in November-1 at Q = 4.32 m3/det and thelargest water demand in February -1 at Q = 13.40 m3/det. At the research location, rice planting begins in January-I, with a rice-soybean planting pattern. From the calculation of water demand, Krueng Pasee Irrigation Area has the smallest water demand in November-1 and the largest water demand in February-1.

Optimizing the Maize Irrigation Strategy and Yield Prediction under Future Climate Scenarios in the Yellow River Delta

The contradiction between water demand and water supply in the Yellow River Delta restricts the corn yield in the region. It is of great significance to formulate reasonable irrigation strategies to alleviate regional water use and improve corn yield. Based on typical hydrological years (wet year, normal year, and dry year), this study used the coupling model of AquaCrop, the multi-objective genetic algorithm (NSGA-III), and TOPSIS-Entropy established using the Python language to solve the problem, with the objectives of achieving the minimum irrigation water (IW), maximum yield (Y), maximum irrigation water production rate (IWP), and maximum water use efficiency (WUE). TOPSIS-Entropy was then used to make decisions on the Pareto fronts, seeking the best irrigation decision under the multiple objectives. The results show the following: (1) The AquaCrop-OSPy model accurately simulated the maize growth process in the experimental area. The R2 values for canopy coverage (CC) in 2019, 2020, and 2021 were 0.87, 0.90, and 0.92, respectively, and the R2 values for the aboveground biomass (BIO) were 0.97, 0.96, and 0.96. (2) Compared with other irrigation treatments, the rainfall in the test area can meet the water demand of the maize growth period in wet years, and net irrigation can significantly reduce IW and increase Y, IWP, and WUE in normal and dry years. (3) Using LARS-WG (a widely employed stochastic weather generator in agricultural climate impact assessment) to generate future climate scenarios externally resulted in a higher CO2 concentration with increased production and slightly reduced IW demand. (4) Optimizing irrigation strategies is important for allowing decision makers to promote the sustainable utilization of water resources in the study region and increase maize crop yields.

Alleviating groundwater depletion while realizing food security for sustainable development

Groundwater depletion was one of humanity's greatest challenges for both food and environmental security. Here we tried to address these challenges through a meta-analysis in three major global groundwater-depleted agricultural regions and a case study with the long-term field experiment with factorable and systematic solutions. We showed groundwater depletion was particularly evident in the important regions for global food production, such as the High Plains of the United States, the Ganges Plains of India, and North China Plain, resulting from a 262.5 mm gap between crop water demand and rainfall. In order to solve this problem, the long-term field study in North China Plain indicated that the factor adjustment (minimum irrigation) within the traditional system and systematic change from traditional wheat-maize rotation to double-cropped maize could achieve both sustainable groundwater use and high calorie yield, accompanying by low integrated environmental impacts. Our results provided essential insights in similar areas for the United Nations Sustainable Development Goals for food and environmental security.

TIMELY IRRIGATION ENHANCES THE YIELD AND WATER PRODUCTIVITY OF CHICKPEAS IN BANGLADESH

Groundwater level declination is a significant issue in various regions of Bangladesh, primarily due to intensive irrigation in agriculture. To address this growing problem, a potential solution is to shift from cultivating water-intensive crops to less water-demanding crops like chickpeas in water-scarce areas, which could prove to be an effective strategy. With this view, a 3-year long (from 2015 to 2018) experience was done in two different locations (Gazipur and Rajshahi) to investigate the response of irrigation to the growth, yield, and water use of chickpeas. There were four treatments (each has three replications), namely, T1 = rainfed, T2 = one irrigation at post-sowing (20-22 days after sowing (DAS)), T3 = one irrigation at pod development (80-85 DAS) stage, and T4 = two irrigations each at post-sowing and pod development stages. The results showed that relying solely on rainfall was insufficient for satisfactory chickpea yield in Gazipur, emphasizing the need for irrigation. Conversely, if Rajshahi receives ample rainfall, there might not be a requirement for irrigation. Applying irrigation twice (at the post-sowing and pod-filling stage) in Gazipur and once (at the pod-filling stage) in Rajshahi, along with approximately 82.45 mm of irrigation in Gazipur and 75.94 mm in Rajshahi, resulted in enhanced growth and yield of chickpeas. The study findings indicate that chickpeas can be effectively cultivated with no or minimum irrigation, thereby reducing the risks of water-table mining in drier parts of the country.

Seed Quality of Transplanted Aman Rice as Impacted by Rainfed in the Ripening Phase

Rainfall is the main contributor to provide soil moisture for rainfed rice (Transplanted Aman) cultivation. The aim of the study was to investigate the seed quality of different transplanted Aman rice cultivars that are influenced by rainfed during ripening phases. For this, several experiments have been performed during the transplanted Aman season of 2014, 2015, and 2016 at the Bangladesh Rice Research Institute, Gazipur. Two planting dates and three rice varieties were used in the treatment, and the treatments were organized in a randomized complete block design with three replications. T. Aman rice received huge rainfall up to vegetative phage resulting no irrigation demand in all three tested years. In all the parameters of yield and yield components, the association between planting dates and variety was not significant. Also the relationship between planting dates and variety did not greatly affect the germination percentage, seedling vigor index, high density grain shoot dry weight, and root dry weight. Sixteen august planting produced a greater number of tillers m-2, panicles m-2, grain panicle-1, 1000-grain weight, grain and straw yield in the case of yield and yield components. While, the 1000-grain weight of panicles m-2, grain panicle-1, was greatly influenced by the variety. The highest 1000 grain weight and grain yield were produced by BRRI dhan46. The SVI, HDG percent, SDW and RDW were greatly influenced by planting dates in terms of seed quality. All of these criteria were higher for planting on 16 August than for planting on 12 September. Early transplanting received more rainfall in reproductive phase than late planting. Varieties were greatly influenced by all the seed quality parameters. BRRI dhan46 received the highest GM percent, SVI, HDG percent, SDW and RDW. Specific suitable transplanting dates were selected considering minimum irrigation at reproductive phage and maximun seed quality and grain yield. Delay in transplanting demanded more irrigation and reduced seed quality and grain yield.

Assessing water productivity and energy use for irrigating rice in Pakistan

AbstractOverirrigation of rice crops from groundwater negatively impacts water productivity, energy use and the environment in Pakistan. Therefore, four decades (1981–2020) of data on rice crop area and production were assessed for water productivity, and alternative options for irrigation application and energy use were explored. The results show a maximum average yield of 2.42 t ha‾¹ and water productivity of 0.16 kg m‾3 (6211 L kg‾¹) for the last decade (2011–2020), with an average decadal yield increase of 14%. The last year's rice crop during 2020 consumed 14.43 million acre‐feet (MAF) of groundwater, assuming a minimum irrigation application of 1500 mm per season. The maximum equivalent energy required for pumping is approximately 386 MW for electrical and 4966 MW for diesel‐driven tube wells. Diesel combustion for pumping caused 1.26 million t (467 kg ha‾¹) of CO2 emissions per season. The capital cost for converting all installed capacities of electrical (2311 MW, PKR 481 billion) and diesel (16 517 MW, PKR 1909 billion) tube wells to solar energy can be recovered in less than 4 years. Improved governance and optimized site‐specific solar system designs can avoid undergoing groundwater. Improving water productivity and using solar energy for irrigation indicates the prospects of improved sustainability of rice crops in Pakistan.

Sustainable water and nitrogen optimization to adapt to different temperature variations and rainfall patterns for a trade-off between winter wheat yield and N2O emissions

Optimizing irrigation and nitrogen (N) fertilizer applications is essential to ensure crop yields and lower environmental risks under climate change. The DeNitrification-DeComposition (DNDC) model was employed to investigate the impacts of irrigation regime (RF, rainfed; MI, minimum irrigation; CI, critical irrigation; FI, full irrigation) and N fertilizer rate (N60, N90, N120, N150, N180, N210, N240, N270, and N300 kg ha−1) on yield and nitrous oxide (N2O) emissions from winter wheat growing season under different temperature rise levels (+0, +0.5, +1.0, +1.5, and +2.0 °C scenarios) and precipitation year types (wet, normal, and dry seasons) in the North China Plain. Model evaluations demonstrated that simulated soil temperature, soil moisture, daily N2O flux, yield, and cumulative N2O emissions were generally in close agreement with measurements from field experiment over three growing seasons. By adopting simulation scenarios analysis, the model was then used to explore the effects of irrigation and N fertilizer inputs to balance yield and N2O emissions from winter wheat growing season. Based on reduced water and fertilizer inputs and N2O emissions with little yield penalty, recommended management practices included application of MI-N150 in wet season, CI-N120 in both normal and dry seasons, and CI-N150 for +0 to +2.0 °C scenarios. Recommended practices in different precipitation year types reduced irrigation amount by 75–150 mm, N rate by 75–105 kg N ha−1, yield by 0.16–0.86 t ha−1, cumulative N2O emissions by 0.13–0.64 kg ha−1, and yield-scaled N2O emissions by 15.5–85.0 mg kg−1 compared with current practices. The corresponding metrics for different elevated temperature levels decreased by 75 mm, 75 kg N ha−1, 0.09–0.50 t ha−1, 0.12–0.52 kg ha−1, and 13.7–72.3 mg kg−1, respectively. The proposed management practices can help to maintain high agronomic productivity and alleviate environmental pollution from agricultural ecosystems, thereby providing an important basis for mitigation strategies to adapt to climate change.

Optimizing planting density and irrigation depth of hybrid maize seed production under limited water availability

Improvement in seed vigor and yield of hybrid maize is required to ensure food security. Optimal planting density and irrigation depth are critical for hybrid maize seed production in arid areas. Using data from 2012 to 2017, a new integrated model optimized planting density and border irrigation depth for achieving high yield and seed vigor of hybrid maize under limited water availability. For field experiments, a planting density of 9.75 plants m−2 under full irrigation was in the control treatment. The results showed that water deficit decreased yield, evapotranspiration, and aboveground biomass per plant. The highest yield was obtained for the planting density of 12.75 plants m−2 under full irrigation. The single crop coefficient and crop water production function models were modified to simulate evapotranspiration and yield, respectively. Using kernel number per plant and plant growth rate during the flowering stage, a kernel weight model was established. The maximum yield and minimum irrigation depth were weighted and kernel weight was constrained. Three minimum relative kernel weight (RKWmin) options were considered, option 1 with RKWmin= 1.00 (control), option 2 with RKWmin= 0 (unconstrained), and option 3 with RKWmin ranged from 0.60 to 0.90. In option 1, the optimal irrigation depth during the growing season under control planting density decreased by 24.3–39.4% compared to the conventional practices. In option 2, yield increased but average kernel weight decreased by 25% than control. In the option 3, average yield and water use efficiency increased by 9.2% and 6.5% compared to option 1, respectively. The average planting density decreased by 10.2%, yield reduced by 2.0%, and water use efficiency reduced by 1.7%, but kernel weight increased by 9% compared to option 2. Thus option 3 can be recommended for hybrid maize seed production with limited water availability in arid regions of Northwest China. Our research provided a new theoretical method to improve yield, and ensure seed vigor of hybrid maize in arid regions.

Optimization of Water and Energy Spatial Patterns in the Cascade Pump Station Irrigation District

Cascade pump station irrigation districts (CPSIDs) consume large quantities of water and energy. Water- and energy-saving results and income increases are guaranteed under the sustainable development of the CPSID. The CPSID is divided into several sub-districts based on the elevation difference of topography and pump station distributions. The spatial patterns of crops and irrigation technologies can be changed by adjusting crop planting structures and developing drip irrigation in each sub-district. Its optimization will change the spatial patterns of irrigation water and energy consumption to achieve water- and energy-saving results, increase income, and provide an ecological advantage. To obtain the optimal spatial patterns of water and energy in the CPSID, a multi-objective linear programming model of minimum irrigation water consumption, minimum energy consumption, and highest crop output value was established. This model was applied to the Jingdian Phase I Irrigation District in northwest China, and an optimal scheme of water and energy spatial patterns was obtained. Compared with the present situation, the optimal scheme could save water by 26.18%, save energy by 29.38%, and increase income by 29.55%. The increased investment in the drip irrigation project would lead to reduced irrigation water and energy consumption and increased crop output value. The research results provide a scientific basis for the sustainable development of agriculture and ecological environment protection in the CPSID.

Application of water-saving technologies for cotton irrigation in the Lower Volga region

The article presents the results of research in 2019-2020 in the south of Russia. There were 4 options for irrigation and 4 doses of fertilization to obtain a yield of 2 to 5 t/ha of raw cotton with different irrigation methods. It was found that a maintenance of the Soil Pre-Irrigation Moisture (SPIM) 75-75-70 % of Full Moisture Capacity (FMC) together with the doses of fertilizer N140P60K45 made it possible to achieve the minimum Irrigation Water Expenses (IWE) of 463 and 269 m3 and the Total Water Consumption (Water Total Consumption Coefficient – WTCC) of 664 and 456 m3 to obtain 1 ton of raw cotton, achieving the yield of 4.32 and 4.49 t/ha, respectively, with sprinkling and drip irrigation. An increase of the fertilizer doses to N175P75K56 contributed to rise of yield to 5.28 and 5.41 t/ha, simultaneously reducing IWE the to 378 and 224, and WTCC - to 543 and 379 m3/t. Compared to furrow irrigation, the use of sprinkling and drip irrigation made it possible to reduce IWE by 37.0 and 73.7 %, WTCC - by 47.4 and 63.7 %, increasing the yield of raw cotton to 38.0 and 43.5 %.

What Is the Duration of Irrigation? An InVitro Study of the Minimum Exposure Time to Eradicate Bacteria With Irrigation Solutions.

Antiseptic irrigation solutions are commonly used by arthroplasty surgeons to reduce intraoperative bacterial colonization with the goal of reducing postoperative infections in the setting of primary total joint arthroplasty. Currently, the minimum irrigation time to eliminate common microbes implicated in periprosthetic joint infection is unknown. We sought to determine the minimum effective exposure time required to prevent growth of Staphylococcus aureus, Staphylococcus epidermidis, and Cutibacterium acnes with common antiseptic solutions. S aureus, S epidermidis, and C acnes cultures were treated with povidone-iodine (0.35%), chlorhexidine (0.05%), sodium hypochlorite (0.5%), polyhexamethylene biguanide, and an acetic acid-based solution for 15, 30, 60, 90, and 120seconds in triplicate. Bacterial growth was quantified using the drop plate method. Failure to eliminate all bacteria was considered "not effective" at that time point. Povidone-iodine 0.35% (Betadine), sodium hypochlorite 0.5% (HySept), and acetic acid (Bactisure) eradicated all bacterial growth after 90seconds of treatment, and as low as 15seconds in S aureus and C acnes (Betadine) or S epidermidis (Bactisure). Polyhexamethylene biguanide (Prontosan) required 90seconds for elimination of S aureus and S epidermidis, and 120seconds for C acnes. Chlorhexidine 0.05% (Irrisept) did eliminate S epidermidis at 120seconds but did not effectively eradicate S aureus or C acnes. All tested antiseptic solutions demonstrated successful eradication of all bacterial growth in under 2minutes of treatment time except chlorhexidine. Povidone-iodine may require the shortest duration of treatment time to successfully eradicate common bacteria.

Response of sorghum variety growth in the first ratoon with liquid organic fertilizer application

The advantages of ratoon system are short harvesting time, minimum irrigation needed, and lower farming cost compared with the first plant of seed. Liquid organic fertilizer (LOF) application is selected to increase nutrition absorption and as an attempt of supporting sustainable agriculture program. The objective of research is to study the effect of first-ratoon sorghum variety and LOF dose on sorghum growth. This research used completely randomized block design factorial. The first factor is sorghum variety consisting of Numbu, Kawali, and Suri 3. The second treatment of LOF dose consisted of 0 L.ha-1, 3 L.ha-1, 6 L.ha-1, and 9 L.ha-1. Data analysis was conducted variance analysis of 5% followed with least significant different test 5% and polynomial regression test. The result of research shows that Numbu with the optimum dose of LOF 3,8 L.ha-1 produced the highest leaf area and LAI of 2570 cm2 and 2,56 compared to other varieties. Suri 3 and Numbu yield higher fresh stove weight than Kawali. This means that Numbu and Suri 3 can be used as a source of animal feed.

Multi-objective reservoir operation of the Ukai reservoir system using an improved Jaya algorithm

Abstract This paper introduces an effective and reliable approach based on a multi-population approach, namely the self-adaptive multi-population Jaya algorithm (SAMP-JA), to extract multi-purpose reservoir operation policies. The current research focused on two goals: minimizing irrigation deficits and maximizing hydropower generation. Three different models were formulated. The results were compared with those for an ordinary Jaya algorithm (JA), particle swarm optimization (PSO), and an invasive weed optimization (IWO) algorithm. In Model-1, the minimum irrigation deficit obtained by SAMP-JA and JA was 305092.99 . SAMP-JA was better than JA, PSO and IWO in terms of convergence. In Model-2, the maximum hydropower generation achieved by SAMP-JA, JA and PSO was 1723.50 . When comparing the average hydropower generation, SAMP-JA and PSO performed better than JA and IWO. In terms of convergence, SAMP-JA was better than PSO. In Model-3, a self-adaptive multi-population multi-objective Jaya algorithm (SAMP-MOJA) was better than multi-objective particle swarm optimization (MOPSO) and multi-objective Jaya algorithm (MOJA) in terms of maximum hydropower generation, and MOPSO was better than SAMP-MOJA and MOJA in terms of minimum irrigation deficiency. While comparing convergence, SAMP-MOJA was found to be better than MOPSO and MOJA. Overall, SAMP-JA was found to outperform JA, POS and IWO.

Harnessing Chlorophyll Fluorescence for Phenotyping Analysis of Wild and Cultivated Tomato for High Photochemical Efficiency under Water Deficit for Climate Change Resilience

Fluctuations of the weather conditions, due to global climate change, greatly influence plant growth and development, eventually affecting crop yield and quality, but also plant survival. Since water shortage is one of the key risks for the future of agriculture, exploring the capability of crop species to grow with limited water is therefore fundamental. By using chlorophyll fluorescence analysis, we evaluated the responses of wild tomato accession Solanum pennellii LA0716, Solanum lycopersicum cv. Μ82, the introgression line IL12-4 (from cv. M82 Χ LA0716), and the Greek tomato cultivars cv. Santorini and cv. Zakinthos, to moderate drought stress (MoDS) and severe drought stress (SDS), in order to identify the minimum irrigation level for efficient photosynthetic performance. Agronomic traits (plant height, number of leaves and root/shoot biomass), relative water content (RWC), and lipid peroxidation, were also measured. Under almost 50% deficit irrigation, S. pennellii exhibited an enhanced photosynthetic function by displaying a hormetic response of electron transport rate (ETR), due to an increased fraction of open reaction centers, it is suggested to be activated by the low increase of reactive oxygen species (ROS). A low increase of ROS is regarded to be beneficial by stimulating defense responses and also triggering a more oxidized redox state of quinone A (QA), corresponding in S. pennellii under 50% deficit irrigation, to the lowest stomatal opening, resulting in reduction of water loss. Solanumpennellii was the most tolerant to drought, as it was expected, and could manage to have an adequate photochemical function with almost 30% water regime of well-watered plants. With 50% deficit irrigation, cv. Μ82 and cv. Santorini did not show any difference in photochemical efficiency to control plants and are recommended to be cultivated under deficit irrigation as an effective strategy to enhance agricultural sustainability under a global climate change. We conclude that instead of the previously used Fv/Fm ratio, the redox state of QA, as it can be estimated by the chlorophyll fluorescence parameter 1 - qL, is a better indicator to evaluate photosynthetic efficiency and select drought tolerant cultivars under deficit irrigation.

Hybrid Bermudagrass and Tall Fescue Turfgrass Irrigation in Central California: I. Assessment of Visual Quality, Soil Moisture and Performance of an ET-Based Smart Controller

Research-based information regarding the accuracy and reliability of smart irrigation controllers for autonomous landscape irrigation water conservation is limited in central California. A two-year irrigation research trial (2018–2019) was conducted in Parlier, California, to study the response of hybrid bermudagrass and tall fescue to varying irrigation scenarios (irrigation levels and irrigation frequency) autonomously applied using a Weathermatic ET-based smart controller. The response of turfgrass species to the irrigation treatments was visually assessed and rated. In addition, turfgrass water response functions (TWRFs) were developed to estimate the impact of irrigation scenarios on the turfgrass species based on long-term mean reference evapotranspiration (ETo) data. The Weathermatic controller overestimated ETo between 5% and 7% in 2018 and between 5% and 8% in 2019 compared with California Irrigation Management Information System values. The controller closely followed programmed watering-days restrictions across treatments in 2018 and 2019 and adjusted the watering-days based on ETo demand when no restriction was applied. The low half distribution uniformity and precipitation rate of the irrigation system were 0.78 and 28 mm h−1, respectively. The catch-cans method substantially underestimated the precipitation rate of the irrigation system and caused over-irrigation by the smart controller. No water-saving and turfgrass quality improvement was observed owing to restricting irrigation frequency (watering days). For the hybrid bermudagrass, the visual rating (VR) for 101% ETo treatment stayed above the minimum acceptable value of six during the trial. For tall fescue, the 108% ETo level with 3 d wk−1 frequency kept the VR values in the acceptable range in 2018 except for a short period in mid-trial. The TWRF provided a good fit to experimental data with r values of 0.79 and 0.75 for tall fescue and hybrid bermudagrass, respectively. The estimated VR values by TWRF suggested 70–80% ETo as the minimum irrigation application to maintain the acceptable hybrid bermudagrass quality in central California during the high water demand months (i.e., May to August) based on long-term mean ETo data. The TWRF estimations suggest that 100% ETo would be sufficient to maintain the tall fescue quality for only 55 days. This might be an overestimation impacted by the relatively small tall fescue VR data in 2019 owing to minimal fertilizer applications and should be further investigated in the future.

Dry matter accumulation after silking and kernel weight are the key factors for increasing maize yield and water use efficiency

Reducing use of irrigation water and improving crop yield are important since water shortage and food security are the two main problems in developing sustainable agriculture. In this study, we aim to reduce the conventional irrigation amount and to determine the mechanisms for yield formation and water use efficiency (WUE) increasing, and to identify the optimal irrigation amount for high yield maize (> 15 Mg ha–1) under mulch drip irrigation in arid, semi-arid, and semi-humid areas. Experiments carried out in the arid area (Changji), semi-arid area (Qitai), and semi-humid area (Xinyuan) in 2018 and 2019 showed that the optimal irrigation amount was 720 mm, 540 mm, and 180 mm, respectively. As compared to the irrigation amount used by farmers irrigation amount (maximum irrigation amount), the optimal irrigation amount recorded in our study could save 11.1–60.0% of water. At the same time, the WUE and irrigation water use efficiency (IWUE) increased by 5.2–60.3% and 7.9–181.6% with maize yield > 15 Mg ha–1. Less than optimal irrigation, increasing yield and WUE by increasing both dry matter accumulation after silking (DMA) and thousand kernel weight (TKW). Over optimal irrigation, the yield further increased mainly by increasing dry matter at maturity, and increasing WUE by kernel number per ear. The most photosynthate was transferred from leaves and stems to grains in minimum irrigation, but the lowest that in maximum irrigation. Therefore, DMA and TKW are the key factors to increase yield and WUE under less than optimal irrigation. Optimize irrigation goal may be to improve the transport of photosynthate to grain in the future.