Center Pivot System Research Articles

Joint modeling of longitudinal and survival data for the evaluation of the impact of irrigation systems on the time until the occurrence of phoma leaf spot in Arabica coffee plants.

The coffee crop is prominent in Brazilian agriculture, making the country a global power in this area. One of the main concerns in the coffee sector is disease, which can affect coffee productivity and quality. Thus, it is important to evaluate the factors that may affect coffee quality and thus enhance the development of strategies to reduce coffee losses and costs and optimize production. This study evaluated the influence of the type of irrigation (self-propelled, drip, and center pivot) on the time until the occurrence of phoma leaf spot on Arabica coffee plants, considering the intensity of the disease. Additionally, the association between longitudinal incidence and the time until an event of interest was assessed based on the joint modeling of longitudinal and survival data. The results of this study identify the effectiveness of drip irrigation system compared with other systems; the use of such systems was associated with an ~46.5% reduction in the risk of leaf spot disease compared with the use of a self-propelled irrigation system. The use of a center pivot system increased the risk of disease progression compared with a self-propelled system. An association between the longitudinal and survival processes was also observed. The findings demonstrate the superior performance of the drip irrigation system in controlling phoma leaf spot disease in Arabica coffee plants compared with self-propelled and center pivot systems. This research highlights the potential of using drip irrigation to establish more effective agricultural practices in coffee cultivation, contributing to better disease management and improved crop quality. © 2024 Society of Chemical Industry.

Toward field-scale groundwater pumping and improved groundwater management using remote sensing and climate data

Groundwater overdraft in the western United States has prompted water managers to develop groundwater management plans that include mandatory reporting of groundwater pumping (GP). However, most irrigation systems in this region are not equipped with irrigation water flow meters to record GP and performing quality control of the available metered GP data is difficult due to the scarcity of reliable secondary GP estimates. We hypothesize that Landsat-based actual evapotranspiration (ET) estimates from OpenET can be used to predict GP and aid in quality control of the metered GP data. The objectives of this study are to: 1) pair OpenET estimates of consumptive use (Net ET, i.e., actual ET less effective precipitation) and metered annual GP data from Diamond Valley, Nevada, and Harney Basin, Oregon; 2) evaluate linear regression and machine learning models to establish the GP vs Net ET relationship; and 3) compare GP estimates at the field- and basin-scales. Results from using a bootstrapping technique showed that the mean absolute errors and root mean square errors for field-scale GP depth are ∼11 % and ∼14 % across Diamond Valley and Harney Basin based on the OpenET ensemble mean, which showed the highest skill among all the OpenET ET models. Moreover, the regression models explained 50 %-70 % variance in GP depth and ∼90 % variance in GP volumes. Our GP volume estimates are also within 7 % and 17 % of the total reported and measured volumes in Diamond Valley and Harney Basin, respectively, and the estimated average irrigation efficiency of 87 % aligns with known center-pivot system efficiencies. Additionally, the OpenET ensemble proves to be useful for identifying discrepancies in metered GP data, which are subsequently flagged as outliers. Results from this study illustrate usefulness of satellite-based ET estimates for estimating GP and metered GP data quality control and have the potential to help estimate historical GP.

Size and Dispatch Co-Optimisation of a Grid-Connected Agrivoltaic System

Agrivoltaic systems that leverage the opportunity of integrating solar photovoltaic (PV) systems into land used for agriculture, have been shown to provide an effective platform for a mutually beneficial cooperation between energy and food. However, the mainstream literature has failed to investigate the systematic design and dispatch considerations that must be made to ensure the robust and profit-maximising operation of a grid-connected agrivoltaic system from an energy perspective subject to meeting onsite load demands, such as irrigation pumps, centre pivot systems, and cow shed pumps. This necessitates formulating a coordinated, system-level strategic design and dispatch problem that considers the localised energy system and its individual components. Accordingly, this paper introduces a novel agrivoltaic system energy planning optimisation method with an integrated dispatch scheduling framework. The proposed method enables the consideration of augmenting value streams, such as temporal energy arbitrage with the grid, especially regarding the presence of behind-the-meter stationary battery storage devices and electric agricultural vehicles’ batteries. Furthermore, the proposed method has a general crop type-independent structure. This allows for greater adaptability of the method to different types of agrivoltaic systems. The effectiveness of the proposed method in improving the economic feasibility of grid-connected agrivoltaic systems is demonstrated based on simulation results obtained from its application to a conceptual agrivoltaic system backed by stationary and mobile battery storage systems, proposed for implementation in a rural location in Aotearoa New Zealand.

Developing a Hybrid Irrigation System for Smart Agriculture Using IoT Sensors and Machine Learning in Sri Ganganagar, Rajasthan

The agriculture sector is one of the largest consumers of fresh water. Different types of irrigation systems are available, including center pivot, drip and sprinkler systems, and linear motion systems. However, the complex structure of existing irrigation systems and their high maintenance costs encourage Indian farmers to continue using these methods. Due to its ease of use and low energy consumption, surface irrigation is one of the most popular irrigation techniques. Although the main reasons for poor irrigation application efficiency are uneven irrigation water distribution and deep absorption, using a variety of technologies, countries are trying to increase the sustainability of agriculture. Automated irrigation systems contribute significantly to water conservation. The combination of automation and Internet of Things (IoT) improves agricultural practices. These technologies help farmers understand their crops, minimize their impact on the environment, and preserve resources. They also enable efficient monitoring of the weather, water resources, and soil. This research proposes an intelligent, low-cost field irrigation system. The proposed prototype can measure soil moisture, rain status, wind speed, water level, temperature, and humidity using a hardware sensor and unit. To decide whether to turn on or off the motor, a variety of sensors are used to get a range of readings and conclusions. They enable automatic watering when soil moisture levels are below a certain threshold, and if soil moisture is equal to the required moisture, then the irrigation process stops. Every few minutes, the sensors measure the environmental factors. Data are collected and stored on a ThingSpeak cloud server for analysis. To evaluate the data we collected, we used a variety of models, such as K-nearest neighbors (KNN), Naïve Bayes, random forest, and logistic regression. Compared to other Naïve Bayes and random forest models, the accuracy rate was 98.8%, the mean square error was 0.16, and the results of logistic regression, KNN, and SVM were in order: (98.3%/1.66), (99.3%/0.66), and (99.5%/0.5), respectively. In the end, an automated irrigation system run on IoT applications gives farmers access to remote monitoring and control, as well as information about the specifics of the irrigation field.

Water and Carbon Footprints of Biomass Production Assets: Drip and Center Pivot Irrigation Systems

Studies on the environmental footprints of agricultural production have strong links with 4 out of the 17 Sustainable Development Goals (SDG) established by the United Nations. Irrigation systems are essential tools for increasing agricultural yields, particularly in arid regions. However, the production and assembly of these systems can have significant environmental impacts, including excessive water consumption and greenhouse gas emissions. Although studies have approached biomass production, few of them have provided data about asset depreciation, such as irrigation systems, machinery, etc. Trying to fill this gap, this study aimed at determining the water and carbon footprints of two commonly used irrigation systems: center pivot and drip. Several variables, including the irrigated area, pump power, filter type, system flow, and pipe length, were analyzed to determine the carbon and water footprints of each component of the irrigation systems. The results reveal that the materials used for pipes and filters had the most significant impact on the water and carbon footprints, with galvanized steel pipes and sand filters having the highest footprints. Additionally, the irrigated area affected the center pivot and drip systems differently, with the depreciation of the irrigation systems being a significant variable for both water and carbon footprints. These results can support the development of sustainable irrigation practices that reduce environmental impacts while enhancing agricultural yields. Decision-makers can use this information to establish a life-cycle database and evaluate the impact of irrigation systems on water and carbon footprints.

First evidence of widespread, severe soil erosion underneath centre-pivot irrigation systems

Centre-pivot systems are widely used for irrigation in agriculture. However, excessive water application rates under low pressure centre-pivot systems can lead to soil erosion, which degrades soil structure and increases crop vulnerability to droughts. Although efforts have been deployed to measure soil erosion underneath individual centre pivots, a large-scale systematic assessment of extent and severity of soil erosion in centre-pivot irrigated fields is currently lacking. Here we used Google Earth™ satellite images to provide first evidence of widespread, severe soil erosion in centre-pivot irrigated agricultural land. We focused on the municipality of Cristalina (6154 km2), in the Brazilian Central Highlands, where centre pivots irrigate approximately 60,000 ha of cropland. The study area is in the Cerrado biome, which is one of the most important grain-producing regions in the world and Brazil's main centre-pivot irrigation area. By mapping erosion features under centre pivots, we found that 29 % of centre-pivot fields displayed signs of rill erosion, with individual rills up to a length of 1200 m. Most erosion features were identified during the dry season of the Brazilian Cerrado, which coincided with the period of greater satellite-image availability. Moreover, we found that compacted centre-pivot-wheel tracks often triggered rill incision and that eroding centre-pivot fields displayed higher slope gradients and were better connected to surface waters than the non-eroding fields. Ultimately, the frequent identification of severe erosion features in the centre-pivot fields during the dry season indicates that irrigation causes and/or aggravates soil erosion in Cristalina and likely in other parts of the Brazilian Cerrado. This first systematic evidence of widespread soil erosion underneath centre-pivot systems highlights that irrigation erosion is an important but neglected driver of land degradation, and that urgent action is required to protect affected soils for future generations.

Greenhouse gas emissions and ammonia loss of maize fertigation with a centre pivot system in the North China Plain

AbstractWhether sprinkler fertigation stimulates greenhouse gas emissions and ammonia loss is an indispensable consideration for nitrogen management. To assess the effect of sprinkler fertigation on N2O, CH4, CO2 and NH3 emissions, three N recommendation methods for summer maize, including the local empirical value, mineral nitrogen method and modified mineral nitrogen method, and three topdressing methods, including no topdressing, one topdressing and two topdressings following intermittent rainfall events, were tested in the 2020 and 2021 seasons. Surface irrigation with fertilizer broadcasting was used as the control treatment. The results indicated that nitrogen topdressing and rainfall promoted soil N2O, CO2 and NH3 emissions but had no obvious effect on CH4 emissions. The increasing nitrogen application rate increased gaseous nitrogen losses of N2O and NH3. Sprinkler fertigation with the modified mineral nitrogen method and two topdressings obtained the greatest yield and nitrogen use efficiency. Compared to the control treatment, the yield and nitrogen use efficiency in the sprinkler fertigation treatments was maximally increased by 20%, and the cumulative NH3 volatilization losses and gaseous nitrogen loss rate were both reduced by 16%, while the cumulative N2O emissions increased by 4%. No significant difference in global warming potential was obtained among all treatments.

Forecasting of Canopy Temperatures Using Machine Learning Algorithms

Highlights This study analyzes the feasibility of using Artificial Neural Networks (ANNs) to estimate canopy temperatures. A methodology is introduced to forecast canopy temperatures using historical canopy temperatures. ANNs can predict canopy temperatures with satisfactory accuracy for plant stress-based irrigation scheduling. The methodology can be useful to add redundancy to plant stress-based irrigation scheduling methods. Abstract. Recent advances can provide farmers with irrigation scheduling tools based on crop stress indicators to assist in the management of Variable Rate Irrigation (VRI) center pivot systems. These tools were integrated into an Irrigation Scheduling Supervisory Control and Data Acquisition System (ISSCADAS) developed by scientists with the USDA-Agricultural Research Service (ARS). The ISSCADAS automates the collection of data from a network of wireless infrared thermometers (IRTs) distributed on a center pivot’s lateral and in the field irrigated by the center pivot, as well as data from a wireless soil water sensor network and a microclimate weather station. This study analyzes the use of Artificial Neural Networks (ANNs), a type of machine learning algorithm, for the forecasting of canopy temperatures obtained by a wireless network of IRTs mounted on a three-span VRI center pivot irrigating corn near Bushland, TX, during the summer of 2017. Among the predictors used by the ANNs were weather variables relevant to the estimation of evapotranspiration (i.e., air temperature, relative humidity, solar irradiance, and wind speed), irrigation management variables (irrigation treatment, irrigation scheduling method, and the amount of water received during the last 5 days as irrigation or rainfall), and days after planting. Two case studies were conducted using data collected from periodic scans of the field performed during the growing season by running the pivot dry. In the first case, data from the first three scans were used to train an ANN, and canopy temperatures estimated using the ANN were then compared against canopy temperatures measured by the network of IRTs during the fourth scan. In the second case, data from the first six scans were used to train ANNs, and canopy temperatures estimated using the ANN were then compared against canopy temperatures measured by the network of IRTs during the seventh scan. The Root of the Mean Squared Error (RMSE) of ANN predictions in the first case ranged from 1.04°C to 2.49°C, whereas the RMSE of ANN predictions in the second case ranged from 2.14°C to 2.77°C. To assess the impact of ANN accuracy on irrigation management, estimated canopy temperatures were fed to a plant-stress-based irrigation scheduling method, and the resulting prescription maps were compared against prescription maps obtained by the same method using the canopy temperatures measured by the network of IRTs. In the first case, no difference was found between both prescription maps. In the second case, only one plot (out of 26) was assigned a different prescription. Results of this study suggest that machine learning techniques can be used to assist the ISSCADAS in situations where canopy temperatures cannot be measured by the network of IRTs due to poor visibility conditions, or because the center pivot cannot traverse the field within a reasonable amount of time. Keywords: Artificial Neural Network, Canopy temperature sensing, Center pivot irrigation, Irrigation scheduling, Machine learning, Metamodeling, Variable rate irrigation.

Irrigation management zone delineation and optimal irrigation scheduling for center pivot irrigation systems

In this study, a three-stage irrigation management zone delineation approach and a model-based scheduler are proposed to provide variable rate irrigation schedules for a 26.4-hectare field at the Alberta Irrigation Technology Centre in Lethbridge, Alberta, Canada. After an initial demarcation of the investigated field into quadrants according to the four different crops growing in the field, the k-means clustering approach is used to delineate each quadrant further into smaller irrigation management zones on the basis of soil hydraulic parameters, elevation of the field and the variable rate irrigation resolution of the center pivot system. For each management zone, a long short-term memory (LSTM) neural network model is then developed to characterize the soil water dynamics in the root zone. Based on the LSTM models, and based on the LSTM models, a mixed-integer model predictive controller (MPC) is designed to prescribe irrigation schedules for the various management zones in each quadrant to ensure optimal crop water uptake while minimizing water consumption and irrigation costs. The applicability of the proposed approach is illustrated in simulations. The results show that the proposed approach achieves 26% water savings compared to a triggered scheduling scheme.

Profitability of Supplemental Irrigation and Soil Dewatering for Potato Production in Atlantic Canada: Insights from Historical Yield and Weather Data

AbstractHighly variable and unpredictable precipitation in humid regions makes water management important for consistent potato production. This study assessed the influence of supplemental irrigation (SI) and soil dewatering on potato productivity and profitability in Prince Edward Island, Canada. The average yields of Russet Burbank (RB), Shepody, Kennebec and Goldrush cultivars from 2000 to 2020 (excluding 2018) were conceptualized as the results of an un-replicated experiment with growing season (GS) precipitation representing water supply treatment. GS precipitation varied from 155 to 479 mm, with an average of 338 mm. Yield increased with increasing GS precipitation in the 155–257 mm range (Rainfall Zone 1; 3/20 seasons), became relatively insensitive to GS precipitation in the 258–425 mm range (Rainfall Zone 2; 12/20 seasons), and decreased as GS precipitation increased from 426 to 479 mm (Rainfall Zone 3; 5/20 seasons). Yields responded to GS precipitation following second-order polynomial regressions, with GS precipitation explaining 69%, 65%, 29% and 50% of yield variation for RB, Shepody, Kennebec and Goldrush cultivars, respectively. These yield regression equations predict that SI using a center-pivot system would produce a positive profit in the first half of Rainfall Zone 1 regardless of field size and in the second half of Rainfall Zone 1 in fields over 40 ha. SI would not produce a positive profit in Rainfall Zone 2 regardless of field size because precipitation was high enough that additional water supply would not have resulted in sufficient yield gains to offset the cost of SI. Soil dewatering would be beneficial for optimal production in Rainfall Zone 3 in which precipitation was excessive. The annual variation in precipitation, unpredictability of SI requirements, and unprofitability in most seasons, present a significant financial barrier for the widespread implementation of SI. On the other hand, the gross income gained from increased yields by soil dewatering would fully cover the cost of tile drain installation after only two to three extremely wet seasons, making tile drainage a good investment for consistent production. This study demonstrates that historical rain-fed yield and weather data can be used to assess the economics of potato production with SI and soil dewatering, and provides important insights on potato water management in a humid temperate climate.

A Revised Equation of Water Application Efficiency in a Center Pivot System Used in Crop Rotation in No Tillage

Correctly quantifying total losses of irrigation in a center pivot system is important for improving application management and efficiency (Ea). The equations usually used to estimate Ea in sprinkler irrigation systems do not consider certain aspects, such as height of sprinklers relative to crop height, leaf interception (LI) of tall-growing crops or partial residue retention (PRR). The aim of this study was to incorporate these components into a new Ea equation adapted to the center pivot system. The trials were conducted in corn grown under no tillage in Córdoba, Argentina. To determine the distribution uniformity (DUpa), 96 catch cans were arranged at a spacing of 3 m, and the sprinklers with similar discharge flow from a center pivot of five towers (27.8 ha) were grouped together. Four irrigation depths (40, 24, 12 and 6 mm) were evaluated at different phenological stages, as well as the control condition without crop. Twenty-eight measurements were taken, and DUpa was statistically compared with respect to the different depths applied and phenological stages as well as the impact on yield. For the 11 grouped segments, with irrigation intensity between 5.7 and 77.4 mm h−1, DUpa for the control condition ranged from very good to excellent (85 to 90%) but decreased significantly with crop growth. Neither the different intensities nor the irrigation depths influenced DUpa up to V10, when it decreased significantly for the 6 mm depth. The spacing between sprinklers had an effect on DUpa and crop yield, decreasing from 18 to 14 ton ha−1 with the largest spacing (5 m). PRR and LI were statistically adjusted, and a revised equation of application efficiency was obtained.

Shared centre pivot. An experience of smallholder irrigation in Midwest Brazil

Irrigation enables crop intensification and improves the smallholder’s household livelihood. Centre pivot irrigation, a system predominantly used in medium to large individual farms, has recently been promoted for smallholder collective irrigation in developing countries. The objective of this research was to assess the irrigation performance and evolution of shared centre pivot systems at the Itamarati Rural Settlement in the Midwest of Brazil, where around 1000 smallholders were organized in small groups between 2001 and 2004, to farm 85 centre pivots collectively. Quantitative and qualitative data were collected in a mixed-approach, and used to characterize the evolution and sustainability of the centre pivots shared. We collected information from published and unpublished reports, and we conducted our own surveys, interviewing twenty-five farm groups and forming focus groups in a 3-year phased process. Irrigation intensity (the ratio of irrigated area over the area equipped for irrigation) declined sharply from 0.75 in 2004/2005–0.12 in 2020. The immediate pressures triggering the abandonment process were: the farmers’ insufficient technical capability, low productivity of the irrigated crops, and high operational and maintenance costs. At the root of these drivers was the incapacity to exert the collective action required by the highly centralized shared centre pivot system. The progressive giving up of collective management and the concentration of land in larger farms seems to be inexorable. The Itamarati experience was a paradigm of a top-down project whose lessons are important for policymakers. We concluded that centralized collective systems are unsustainable unless the community that adopts them has a high sense of union and a capacity for collective action.

Management of the fertilization in Brazilian Savannas with phosphorus and potassium in the succession of soybean, millet and common bean irrigated with center-pivot system

In Brazilian Savannas (Cerrado), soil is managed in a sustainable way with built fertility and the demand for new fertilization will essentially involve the replacement of exported compounds through harvested products. The objective of this study is to improve criteria for fertilization with phosphorus and potassium in the soybean (summer)/millet (off-season)/irrigated common bean (winter) agricultural system. The study was carried out for three growing seasons under randomized blocks design with five treatments and five replications. The treatments consisted of T1= NPK common bean (recommended fertilization) + PK soybean (recommended fertilization); T2= NPK common bean (recommended fertilization) + soybean (without fertilization); T3= Common bean (without fertilization) + soybean PK (recommended fertilization); T4= NPK common bean (recommended fertilization) + P (amount exported by soybean) + soybean K (recommended fertilization); T5= NPK common bean (recommended fertilization) + P and K (amount exported by soybean) + soybean (without fertilization). The treatments T4 and T5 provided common bean and soybean yields (2945 and 4485 kg ha-1 and 2829 and 4412 kg ha-1, respectively) similar to the treatment T1 (2830 and 4353 kg ha-1), in which these crops received pre-fixed doses of recommended fertilization. It was necessary to make the fertilization only once in the year, and not in common bean and thereafter in soybean. We concluded that once there is no need to supply constant seeder with fertilizers for soybean, these treatments can be considered as fertilization management options in the soybean/millet/irrigated common bean agricultural system, which aims at greater operational profitability in planting activities

Evaluating spatial and temporal variations in sub-field level crop water demands

Variable rate irrigation (VRI) requires accurate knowledge of crop water demands at the sub-field level. Existing VRI practices commonly use one or more variables like soil electrical conductivity, historical yields, and topographic maps to delineate variable rate zones. However, these data sets do not quantify within season variability in crop water demands. Crop coefficients are widely used to help estimate evapotranspiration (ET) at different stages of a crop’s growth cycle, and past research has shown how remotely sensed data can identify differences in crop coefficients at regional and field levels. However, the amount of spatial and temporal variation in crop coefficients at the sub-field level (i.e. within a single center pivot system) has not been widely researched. This study aims to compare sub-field ET estimates from two remote sensing platforms and quantify spatial and temporal variations in aggregated sub-field level ET. Vegetation indices and reference ET data were collected at Kansas State University’s Southwest Research Extension Center (SWREC) and two Water Technology Farms during the 2020 corn growing season. Weekly maps of the Normalized Difference Vegetation Index (NDVI) and the Soil Adjusted Vegetation Index (SAVI) from aerial imagery are combined with empirical equations from existing literature to estimate both basal and combined crop coefficients at a 1-meter resolution. These ET estimates are aggregated to a 30 m resolution and compared to the Landsat Provisional Actual ET dataset. Finally, actual ET estimates from aerial images were aggregated using k-means clustering and stationary variable speed zones to determine if there is enough variation in actual ET at the sub-field level to build variable rate irrigation schedules. An equivalence test demonstrated that the aerial imagery and Landsat data sources produce significantly different crop coefficient estimates. However, the two datasets were moderately correlated with Pearson’s product-moment correlation coefficients ranging from -0.95 to 0.86. Both the aerial imaging and Landsat datasets showed high variability in crop coefficients during the first 5-6 weeks after emergence, with these coefficients becoming more spatially uniform later in the growing season. These crop coefficients may help irrigators make more informed irrigation management decisions during the growing season. However, more research is needed to validate these remotely sensed ET estimates and integrate them into an irrigation decision support system.

A Pivotal New Approach to Groundwater Quality Assessment

Areas of intensive agriculture and irrigation are prone to groundwater nitrate contamination, which can threaten drinking water supplies. Irrigation center pivots are a common feature in heavily irrigated regions and have the potential to provide insight into subsurface redox chemistry. In this study, we hypothesized that the same geochemical condition(s) that causes rust staining on center pivot systems will strongly influence groundwater nitrate concentrations. In south central Nebraska, 700 center pivot irrigation systems were classified by appearance of iron staining (full rust, part rust, or no rust) using Google Earth imagery and/or ground-based surveys. Ground-based observation of 270 center pivots yielded the same classifications as Google Earth imagery 83% of the time. Groundwater nitrate concentrations correlated with pivot classifications show lower nitrate concentrations in full rust and part rust pivots when compared with no rust pivots. The novelty of this work is to provide a framework for understanding groundwater quality using an inexpensive method applicable to both established and developing agricultural communities.

Water-energy nexus: A systematic analysis and evaluation of a center-pivot irrigation system

ABSTRACT Water-energy nexus plays a crucial role in addressing the issue of water wasting as well as planning, developing, and optimization of the agricultural product irrigation demands. The objective of this paper is to improve the water and energy use in value crops irrigated by center pivot systems under conventional irrigation planning in southern Algeria. This study is based on the field evaluation of the real water and electrical energy consumption, in two types of center pivot systems, large pivots and traditional Mini-Center Pivot Systems in two different areas (Ouargla and El Oued). Over two years, irrigation campaigns on six pivots systems with six different crops were analyzed to evaluate and characterize their water and electrical energy use. The CROPWAT model was used to determine the crop water requirements and compared with the water volumes currently applied by conventional irrigation planning. The results showed that there was over-consumption of water followed by high electrical energy use due to the poor planning of irrigation, which was generally excessive. In addition, the amount of water applied by conventional irrigation planning and that estimated by CROPWAT do not coincide during the most stages of crop growth. Closer inspection of the results shows that the irrigation water use efficiency for different crops was low and ranged between 0.25 and 3.27 kg/m3 due to the Over-irrigation. Furthermore, the modern pivots presented the most efficient equipment in terms of Energy Use Indicators with 1.1 kW ha−1 while the average of the traditional mini pivots was four times more (4.15 kWh−1). No significant difference was evident between the present specific consumption Es (in the average of 0.274 kWh m−3) and those reported in the literature due to high power installed in the pumping station for Mini-Center Pivot Systems. Therefore, the study findings are expected to draw the attention of decision-makers to the extent of wasting water and energy. Also, these findings reveal the harmful effects of farmers’ incorrect irrigation practices and motivate them to take strong measures toward sustainable agriculture.

Sampling patterns may influence the evaluation of irrigation uniformity of Center Pivot systems

Abstract The evaluation of irrigation uniformity can be affected by the sampling pattern in the field. Thus, this work aimed to compare the water application uniformities of a Center Pivot using three sampling patterns: along the equipment radius (radial), in the direction of movement of the pivot (circular) and in a two-dimensional way (meshed). For this, samplers were positioned under the spans of a Center Pivot system, being evaluated the effects of the sampling pattern and of the span, in a 3×3 statistical factorial design, with three replications. The results showed that circular and meshed arrangements were statistically equal and had higher values of Christiansen's and Distribution Uniformity Coefficients. The mesh type arrangement represented a more uniform distribution profile of irrigation depths on the surface. For areas of flat or slightly undulating topography and when using pressure regulating valves for the emitters, sampling in a radial pattern is sufficient, but for terrains with irregular topography or when pressure head variations along the lateral line are important for the operation of the emitters, the combination of the radial and circular pattern is interesting. The meshed sampling detects the stoppage effect of the towers and drift, however, in a reduced area.

Capacity of center pivot systems and emitter discharges for citrus irrigation. A case study

Introduction— Given the reduction in water resources, irrigation should be carried out with the highest application efficiency. Irrigation pivots achieve application efficiencies close to localized systems; however, for citrus irrigation, the equations available for its design and management do not contribute to the efficient and rational use of water. Objective— Adapt the expression of the capacity of the machine and propose a new formulation for the discharge of the emitters, to adequately describe the operation of the central pivots for citrus irrigation. Methodology— Logical research methods were used and within these, the historical method and the deductive logic, to obtain the adequacy and the new equations presented. Results— The expression of the α coefficient was obtained to adapt the expression of the capacity of the machine and two new equations to calculate the necessary discharges of the emitters. They were applied to a case study. Conclusions— It was confirmed that the center pivot machines to irrigate citrus obtain water consumption and application efficiency comparable could with localized irrigation systems.

Sultansuyu Tarım İşletme Müdürlüğüne Ait Silajlık Mısır Üretim Alanlarındaki Vejetasyon Değişiminin Landsat-8 Uydu Görüntüleri ile Değerlendirilmesi

Remote sensing (RS) is used effectively in agriculture as well as in many different fields. There are many mathematical models developed to predict characteristics such as yield, plant water consumption, plant growth and development in agricultural areas using RS techniques. In this study, the Normalized Vegetation Change Index (NDVI) and Vegetation Index Considering Soil Reflections (SAVI) were used. NDVI and SAVI satellite images are calculated using bands of near infrared (NIR) and red (RED) light wavelengths. Within the scope of the study, by using LANDSAT 8 satellite images of 2018 and 2019, the change in the silage corn production areas irrigated by the circular moving sprinkler irrigation (Center Pivot) systems in Yeniköy belonging to the General Directorate of Agriculture, Sultansuyu Agriculture Operation Directorate was analyzed during the plant development period. In this context, NDVI and SAVI maps obtained from satellite images were determined. Plant growth changes were evaluated for the period from sowing to harvest from the maps obtained. According to the results obtained from the study, it was determined that the plant growth and development satellite images of maize silage can be followed by NDVI and SAVI plant parameters obtained by using NIR and RED bands.

EVALUATION OF FINANCIAL PERFORMANCE OF CENTER PIVOT VERSUS FURROW IRRIGATION SYSTEMS IN SUGARCANE PRODUCTION AT GREEN FUEL ESTATE IN CHIPINGE, ZIMBABWE

The study was conducted at Green Fuel Chisumbanje Estate in Chipinge, Zimbabwe. The study evaluated financial performance of Center pivot versus Furrow irrigation systems in sugarcane production. Secondary data were obtained from the Estate, Zimbabwe Electricity Transmission and Distribution Company, Agricultural Bank of Zimbabwe, Commercial Bank of Zimbabwe, First Capital bank, Zimbabwe National Water Authority and FAOSTAT. Cost Benefit Analysis (CBA) was used to assess financial performance. Furthermore, a sensitivity analysis was done to measure the responsiveness of financial performance to changes in market interest rate. CBA results showed that the Center pivot system is more financially viable [Net Present Value (NPV) =USD36 472; Internal Rate of Return (IRR) =50% and Benefit Cost Ratio (BCR) =1.26] than Furrow the system (NPV=USD24 414; IRR=49% and BCR=1.06). Furrow system was found to be less desirable (BCR falling from 1.06 to 0.99) than Center pivot system (BCR falling from 1.26 to 1.17) in case the discount rate increases from 15% (lowest market rate by commercial banks during study period) to 18% (highest market rate charged by commercial banks). The study concluded that the Center pivot system is more financially viable for sugarcane production despite having higher investment costs than the Furrow system. The study recommends Green Fuel Estate to invest more on the Center pivot system than Furrow system in the long-run.