Center-pivot Fields Research Articles

A Retrospective Analysis of National-Scale Agricultural Development in Saudi Arabia from 1990 to 2021

Agricultural intensification has resulted in the depletion of groundwater resources in many regions of the world. A prime example is Saudi Arabia, which witnessed dramatic agricultural expansion since the 1970s. To explore the influence of policy interventions aimed to better manage water resources, accurate information on the changes in the number and acreage of center-pivot fields is required. To quantify these metrics, we apply a hybrid machine learning framework, consisting of Density-Based Spatial Clustering of Applications with Noise, Convolutional Neural Networks, and Spectral Clustering, to the annual maximum Normalized Differential Vegetation Index maps obtained from Landsat imagery collected between 1990 to 2021. When evaluated against more than 28,000 manually delineated fields, the approach demonstrated producer’s accuracies ranging from 83.7% to 94.8% and user’s accuracies ranging from 90.2% to 97.9%. The coefficient of determination (R2) between framework-delineated and manually delineated fields was higher than 0.97. Nationally, we found that most fields pre-dated 1990 (covering 8841 km2 in that year) and were primarily located within the central regions covering Hail, Qassim, Riyadh, and Wadi ad-Dawasir. A small decreasing trend in field acreage was observed for the period 1990–2010. However, by 2015, the acreage had increased to approximately 33,000 fields covering 9310 km2. While a maximum extent was achieved in 2016, recent decreases have seen levels return to pre-1990 levels. The gradual decrease between 1990 to 2010 was related to policy initiatives designed to phase-out wheat, while increases between 2010 to 2015 were linked to fodder crop expansion. There is evidence of an agricultural uptick starting in 2021, which is likely in response to global influences such as the COVID-19 pandemic or the conflict in Ukraine. Overall, this work offers the first detailed assessment of long-term agricultural development in Saudi Arabia, and provides important insights related to production metrics such as crop types, crop water consumption, and crop phenology and the overarching impacts of agricultural policy interventions.

A machine learning approach for identifying and delineating agricultural fields and their multi-temporal dynamics using three decades of Landsat data

High spatial and temporal resolution satellite imagery are essential data for land cover discrimination and mapping of vegetation dynamics, offering insights into the number, extent, and condition of agricultural fields. However, an accurate account of the number, location, and variability of fields can be challenging to obtain in a timely manner, particularly at scale or in regions where ground-supporting data are not available, limiting the capacity for food production and water use assessment and planning. To bridge this capacity gap, a convolutional neural network approach was adopted and combined with two clustering techniques: the density-based spatial clustering of applications with noise (DBSCAN) and spectral clustering, to provide an account of agricultural fields and their extent across a region lacking ground based information. A random forest classification was also employed to discriminate crop types. Using an annual maximum normalized difference vegetation index (NDVI) derived from 2018 Landsat-8 data, the approach was applied to classify the shape and delineate the extent of agricultural fields across an agricultural region in Saudi Arabia that had an area under irrigation exceeding 2,300 km2. When assessed against manually identified center-pivot fields (CPFs), the method achieved 97.4% producer’s and 98.0% user’s accuracies on an object basis, and 81.4% producer’s and 85.4% user’s accuracies on a pixel basis for identifying non-CPFs (i.e., tree crop plantations and other non-woody crops). The over- and under-segmentation error for CPFs was 1.5% and 1.0%, respectively, with intersection over union errors reported as being 3.5%. The framework showed stability when retrospectively applied to Landsat data from the year 2000, returning 97.5% producer’s and 96.6% user’s accuracies for CPF identification. In order to characterize the temporal dynamics of agricultural development over the past three decades, an analysis of field behavior between 1988 and 2020 was subsequently undertaken. The analysis indicated that the number of CPFs in the study region increased from 45 (covering 20 km2) in 1988 to 5,080 CPFs by 2016 (covering 2,368 km2), followed by a recent reduction to 3,700 CPFs in 2020 (covering 1,581 km2). Through the multi-temporal analysis, individual fields were able to be characterized in terms of their expansion, contraction and activity throughout the study period. Overall, the proposed method was simple to train, efficient in dealing with large datasets, relied on limited in-situ records to a very small degree, and has the potential to be applied to larger national scales, providing an ongoing assessment of important agroinformatic metrics.

Center pivot field delineation and mapping: A satellite-driven object-based image analysis approach for national scale accounting

Center pivot irrigation systems are used to enhance crop production in many countries around the world. Establishing the location and extent of such fields provides information that assists in describing a range of agricultural metrics, including crop identification, yield forecasts, monitoring of irrigation requirements and crop water use, as well as supporting national and regional auditing, licensing and compliance efforts. However, detailed information on the number, extent and changing dynamics of agricultural fields is often lacking in many countries: nowhere more so than in developing regions. To address this lack, we performed a national scale accounting of center pivot fields in Saudi Arabia, using a three year multi-temporal analysis of Landsat-8 satellite data. A geographic object-based image analysis approach was developed based on five 50 × 50 km sub-areas extracted from Landsat data for the year 2015, and applied to delineate individual center pivot fields at a national scale for 2013, 2014 and 2015. The extent of fields was determined via a map of the annual maximum Normalized Difference Vegetation Index (NDVI), while a 15 m spatial resolution map of annual panchromatic band minimums was used to produce an edge detection layer to delineate individual adjoining fields. Amongst a range of classification parameters that were included in the object-based mapping approach, shape information, such as the center pivot field length, length:width ratio, and elliptic fit, were identified as critical parameters. Applying the rule-set that was developed from the five 50 × 50 km sub-regions to the national scale resulted in the identification of 36,052 (11,103 km2), 38,114 (11,902 km2), and 37,254 (11,555 km2) individual fields in 2013, 2014, and 2015, respectively. Approximately 94% of these fields were correctly detected, while their individually measured area was mapped with >95% combined accuracy for fields >0.225 km2 when evaluated against manually delineated fields. Smaller center pivot fields, and specifically those adjoining neighbouring fields, had lower area mapping accuracies (>91% in 75% of cases). The object-based approach allowed a national scale and multi-temporal assessment of center pivot field delineation and mapping, affording new insights into agricultural practice and providing a methodological basis for examining the impact of water management and related policy initiatives, amongst many other potential applications. Apart from filling a clear knowledge gap in Saudi Arabia, the approach has the potential to be expanded elsewhere: particularly to similar locations within the Middle East and North Africa.

Mapping groundwater abstractions from irrigated agriculture: big data, inverse modeling, and a satellite–model fusion approach

Abstract. The agricultural sector in Saudi Arabia has witnessed rapid growth in both production and area under cultivation over the last few decades. This has prompted some concern over the state and future availability of fossil groundwater resources, which have been used to drive this expansion. Large-scale studies using satellite gravimetric data show a declining trend over this region. However, water management agencies require much more detailed information on both the spatial distribution of agricultural fields and their varying levels of water exploitation through time than coarse gravimetric data can provide. Relying on self-reporting from farm operators or sporadic data collection campaigns to obtain needed information are not feasible options, nor do they allow for retrospective assessments. In this work, a water accounting framework that combines satellite data, meteorological output from weather prediction models, and a modified land surface hydrology model was developed to provide information on both irrigated crop water use and groundwater abstraction rates. Results from the local scale, comprising several thousand individual center-pivot fields, were then used to quantify the regional-scale response. To do this, a semi-automated approach for the delineation of center-pivot fields using a multi-temporal statistical analysis of Landsat 8 data was developed. Next, actual crop evaporation rates were estimated using a two-source energy balance (TSEB) model driven by leaf area index, land surface temperature, and albedo, all of which were derived from Landsat 8. The Community Atmosphere Biosphere Land Exchange (CABLE) model was then adapted to use satellite-based vegetation and related surface variables and forced with a 3 km reanalysis dataset from the Weather Research and Forecasting (WRF) model. Groundwater abstraction rates were then inferred by estimating the irrigation supplied to each individual center pivot, which was determined via an optimization approach that considered CABLE-based estimates of evaporation and TSEB-based satellite estimates. The framework was applied over two study regions in Saudi Arabia: a small-scale experimental facility of around 40 center pivots in Al Kharj that was used for an initial evaluation and a much larger agricultural region in Al Jawf province comprising more than 5000 individual fields across an area exceeding 2500 km2. Total groundwater abstraction for the year 2015 in Al Jawf was estimated at approximately 5.5 billion cubic meters, far exceeding any recharge to the groundwater system and further highlighting the need for a comprehensive water management strategy. Overall, this novel data–model fusion approach facilitates the compilation of national-scale groundwater abstractions while also detailing field-scale information that allows both farmers and water management agencies to make informed water accounting decisions across multiple spatial and temporal scales.

Forecasting corn yield at the farm level in Brazil based on the FAO-66 approach and soil-adjusted vegetation index (SAVI)

Crop yield forecasting at the field level is essential for decision-making and the prediction of agricultural economic returns for farmers. Thus, this study evaluated the performance of a methodology for corn yield prediction in irrigated fields in the western region of the state of Bahia, Brazil. This methodology integrates a time series of the basal crop coefficient (Kcb) estimated from the soil-adjusted vegetation index (SAVI) into a simple model based on the water productivity as presented in the FAO-66 manual. In this context, an extensive field-level dataset of 52 center pivot fields of cultivated with corn was used for four consecutive growing seasons (2013 to 2016). Surface reflectance images from the Landsat series were used to calculate the SAVI. The methodology performance was assessed through RMSE, RRMSE, MBE, MAE, and r². The results revealed that the difference between the predicted and actual yield values ranged between −12.2% and 18.8% but that the majority of the estimates remained between −10% and 10%, considering that a single harvest index (HI) was used for the hybrids cultivated in the growing seasons of 2014, 2015 and 2016. After a new reanalysis (by grouping the similar hybrids and using specific HIs), the performance of the predictions increased, especially for the Pioneer hybrids; the majority of the differences between the predicted yield values and the measured yield values remained between -5% and 5%. The results of this research showed that it is essential to work with different HIs when considering different hybrids and years under different weather conditions.

Using an integrated crop water stress index for irrigation scheduling of two corn hybrids in a semi-arid region

Different thermal-based plant feedback systems have been used for irrigation management of cotton and grain crops in the Texas High Plains region, producing yields that are similar or better than irrigation scheduling using the neutron probe. However, there are limited studies using plant feedback systems to actively scheduling irrigations for corn. In this 2-year study, a drought tolerant and a conventional hybrid were managed under a variable rate center pivot irrigation system. The main treatments were manual and plant feedback irrigation scheduling based on weekly neutron probe readings and an integrated crop water stress index (CWSI), respectively. In each main treatment, three irrigation treatment levels were established. Crop responses were compared between irrigation methods and levels. Results demonstrated that overall grain and biomass yields and grain WUE for the plant feedback-control plots were similar to those from the manual-control plots for both years. These results indicate that a plant feedback system using a CWSI could be used to manage corn in a semi-arid region and over a large-sized field. The plant feedback system could provide convenience and time savings to farmers who manage multiple center pivot fields.

Estimation of Basal Crop Coefficient Using Remotely Sensed Vegetation Indices for Center Pivot Irrigated Maize in Southern Brazil

The partitioning between basal crop coefficient (Kcb) and soil evaporation coefficient (Ke) of the crop coefficient (Kc) during the crop cycle is lately been used to develop a more reliable and precise irrigation management. A currently approach under development is estimating Kcb using vegetation indices, obtained from remote sensors. This approach allows an alternative and independent procedure in comparison with the classical methodological approach described in FAO 56 or models that simulate the soil water balance using land information; one model that uses this approach is SIMDualKc. This estimation could also be done using surface energy balance models, however, these models present greater complexity and greater number of input data than the conventional Kc-ETo approach. The objective of this study was to determine the initial, mid-season and lateseason basal crop coefficients (Kcb ini, Kcb mid, Kcb end) using historical records of the red and near infrared reflectance of central pivots, to calculate the normalized vegetation index (IV), adjusted for the entire maize growth, in conjunction with soil field and phenology data of seven center pivot fields in Southern Brazil. Eight Landsat5/TM satellite images of 222 and 223 orbits in point 80, and information from these seven maize monitoring fields during 2004/2005 growing season were used. Crop height and crop growth stages, as well as meteorological weather data collected from a weather station installed in the area were used. A density coefficient (Kd) determined using the fraction of vegetation ground cover (fc) was used for the Kcb estimation through VI, incorporating the impact of both the vegetation density and the plant height. The estimated values were: Kcb ini = 0.20 ± 0.09, Kcb mid = 0.20 ± 0.95 and Kcb end 0.52 ± 0.22, considering 95% of probability. The results showed good agreement with Kcb values obtained using SIMDualKc model in a previous study of this research group for the southern region of Brazil, which were Kcb ini = 0.20, Kcb mid = 1.12 and Kcb end = 0.80.

Estimation of Basal Crop Coefficient Using Remotely Sensed Vegetation Indices for Center Pivot Irrigated Maize in Southern Brazil

The partitioning between basal crop coefficient (Kcb) and soil evaporation coefficient (Ke) of the crop coefficient (Kc) during the crop cycle is lately been used to develop a more reliable and precise irrigation management. A currently approach under development is estimating Kcb using vegetation indices, obtained from remote sensors. This approach allows an alternative and independent procedure in comparison with the classical methodological approach described in FAO 56 or models that simulate the soil water balance using land information; one model that uses this approach is SIMDualKc. This estimation could also be done using surface energy balance models, however, these models present greater complexity and greater number of input data than the conventional Kc-ETo approach. The objective of this study was to determine the initial, mid-season and late-season basal crop coefficients (Kcb ini, Kcb mid, Kcb end) using historical records of the red and near infrared reflectance of central pivots, to calculate the normalized vegetation index (IV), adjusted for the entire maize growth, in conjunction with soil field and phenology data of seven center pivot fields in Southern Brazil. Eight Landsat5/TM satellite images of 222 and 223 orbits in point 80, and information from these seven maize monitoring fields during 2004/2005 growing season were used. Crop height and crop growth stages, as well as meteorological weather data collected from a weather station installed in the area were used. A density coefficient (Kd) determined using the fraction of vegetation ground cover (fc) was used for the Kcb estimation through VI, incorporating the impact of both the vegetation density and the plant height. The estimated values were: Kcb ini = 0.20 ± 0.09, Kcb mid = 0.20 ± 0.95 and Kcb end 0.52 ± 0.22, considering 95% of probability. The results showed good agreement with Kcb values obtained using SIMDualKc model in a previous study of this research group for the southern region of Brazil, which were Kcb= 0.20, Kcb mid = 1.12 and Kcb end = 0.80.

Dynamic prescription maps for site-specific variable rate irrigation of cotton

A prescription map is a set of instructions that controls a variable rate irrigation (VRI) system. These maps, which may be based on prior yield, soil texture, topography, or soil electrical conductivity data, are often manually applied at the beginning of an irrigation season and remain static. The problem with static prescription maps is that they ignore spatiotemporal changes in crop water status. In a two-year study (2012 and 2013), a plant feedback system, including a wireless sensor network of infrared thermometers (IRTs), was used to develop dynamic prescription maps to accomplish adaptive irrigation scheduling for cotton (Gossypium hirsutum L.). One-half of a center pivot field was divided into manually and plant feedback-controlled irrigation treatment plots. Irrigation treatments were at three levels, 75, 50 and 25 percent of full as defined by either replenishment of crop water use to field capacity or by the equivalent threshold of the IRT sensed crop water stress. The system accepted user input to control irrigation for the manual treatment plots (I75M, I50M, and I25M), and calculated and compared a thermal stress index for each plant feedback-controlled treatment plot (I75C, I50C and I25C) with a pre-determined threshold for automated irrigation scheduling. The effectiveness of the plant feedback irrigation scheduling system was evaluated by comparing measured lint yield, crop water use (ETc), and water use efficiency (WUE) with the manually scheduled treatment plots. Results for both years indicated that average lint yields were similar between the manual and plant feedback-control plots at the I75 level (181 and 182gm−2, respectively, in 2012; 115 and 103gm−2, respectively, in 2013) and I50 level (146 and 164gm−2, respectively, in 2012; 95 and 117gm−2, respectively, in 2013). At the I25 level, average lint yield was significantly greater for the plant feedback-compared with the manual-control treatment plots (142gm−2 and 92gm−2, respectively), but the mean amount of irrigation was twice that of the manual-control plots. Mean water use efficiencies (WUE) within the same irrigation treatment levels were similar between methods. Importantly, the automatic plant feedback system did not require the time consuming and expensive manual reading of neutron probe access tubes that was required to schedule the manual treatments. These results demonstrate that the integration of a plant feedback system with a commercial VRI system could be used to control site-specific irrigation management for cotton at higher irrigation treatment levels, i.e., I75 percent and I50 percent of full. Such a system can facilitate the use of a VRI system by automating prescription map coding and providing dynamic irrigation control instructions to meet variable crop water needs throughout the irrigation season. As of yet, further research is required to maintain automatic deficit irrigation at a level equivalent to 25 percent replenishment of crop water use relative to field capacity.

Evapotranspiration models assessment under hyper-arid environment

In this study, we develop more accurate hyper-arid evapotranspiration (ET) models to help improve irrigation water conservation. We examine five ET models (one combination model, three radiation-based models, and one temperature-based model) under hyper-arid condition at three center-pivot fields in the Kingdom of Saudi Arabia. These models were evaluated and calibrated for the alfalfa crop of 2010 and validated for the wheat and potato crops of 2011. The FAO-56 Penman–Monteith (PM) was the most accurate ET model for estimating crop water irrigation needs. The Turc and the Makkink solar radiation-based ET models provided the least accurate estimates even after calibration, while the calibrated Hargreaves–Samani temperature-based model provided the second most accurate estimates for irrigation scheduling in hyper-arid environments. Unlike the FAO-56 PM model, Hargreaves–Samani does not require wind speed or relative humidity data. The most sensitive parameter for this model is air temperature, which is readily available at most sites. The Priestley–Taylor model is highly sensitive to solar radiation data that may not be locally available. The main drawback of the FAO-56 PM model is that it requires extensive list of meteorological data. Weather forecasts are often limited to air temperature data that limit the use of the FAO-56 PM model for irrigation scheduling compared to the calibrated Hargreaves–Samani model.

IN-SEASON ASSESSMENT OF WHEAT CROP HEALTH USING VEGETATION INDICES BASED ON GROUND MEASURED HYPER SPECTRAL DATA

An experiment on a 50 ha center pivot field was conducted to determine the Vegetation Indices (VI’s) that were helpful in assessing the in-season performance of wheat crop treated with graded levels of irrigation water and fertilizers. The irrigation levels were at 100, 90, 80 and 70% evapotranspiration (ETc); however, the fertilizer levels of N: P: K kg-1ha included 300:150:200 (low); 400:250:300 (medium) and 500:300:300 (High). The crop was sown on January 1st and harvested on May 9th, 2012. Temporal data on biophysical parameters and reflectance of the crop in hyper spectral bands (350-2500 nm) were collected at booting and ripening growth stages (February 17th and April 5th, 2012). Results of the study revealed that many of the tested spectral indices showed significant response to irrigation levels. Out of those, only two spectral indices (Plant Senescence Reflectance Index ’PSRI’ and Photochemical Reflectance Index ’PRI’) also exhibited significant response to fertilizer levels. The Middle Infrared-Based Vegetation Index (MIVI) showed a significant response to the irrigation levels for both sampling dates. Among the tested spectral indices, Normalized Difference Infrared Index (NDII) and Normalized Difference Nitrogen Index (NDNI) exhibited the highest correlation to crop Leaf Area Index (LAI). Five indices showed the most response to wheat grain yield. These indices included Near Infrared band (NIR), Water Band Index (WBI), Normalized Water Index-1 (NWI-1), Normalized Water Index-3 (NWI-3) and Normalized Water Index-4 (NWI-4).

Soil profile sulfate in irrigated southern high plains cotton fields and Ogallala groundwater

Sulfate (SO4) is one of the most important anions in soils and groundwater in semiarid regions, including west Texas. Crops9 sulfur (S) requirement is about 10% to 20% of the nitrogen requirements. However, there is far less information on soil test SO4-S (S in the SO4 form [sulfate-S]) for the western United States, compared to the humid eastern United States. The deposition of S from the atmosphere has declined nationwide, as coal-fired power plants have been scrubbing their emissions of S. This, combined with greater cotton yields and the lack of S fertilization on the Texas Southern High Plains (SHP) point to the need for a soil profile SO4-S assessment. The first objective of this study was to assess residual soil profile SO4-S content in irrigated cotton (Gossypium hirsutum) fields across a range of soil types and counties of the Texas SHP. The second objective was to compile regional SO4-S concentration data from the Texas Water Development Board irrigation well water quality database and analyze Ogallala groundwater SO4-S levels by county, soil map unit, and well depth. Soil profile (0 to 0.9 m [0 to 36 in]) SO4-S content ranged from 120 to 475 kg ha−1 (107 to 424 lb ac−1) among seven center-pivot fields sampled. About half of this S was in the 0.6 to 0.9 m (24 to 36 in) soil layer, which typically was sandy clay loam or clay loam in texture. Well water SO4-S concentration ranged from 9 to 220 mg L−1 (ppm) in clayey soils with deep groundwater to sandy soils with shallow groundwater, respectively. Groundwater SO4-S concentration was greater than the USEPA drinking water standard of 83 mg L−1 in 32% of the wells, especially in the southeastern corner of the study area. There was no consistent relationship between soil map unit and SO4-S content in the soil profile (0 to 0.9 m). A significant negative relationship between well depth and well water SO4-S concentration was observed in all the eleven-county study area of the SHP of West Texas. In conclusion, soil profile SO4 content to 0.9 m is high in SHP irrigated cotton fields and in most cases should be adequate for crop S nutritional requirements.

Decision Support System for Design of Center Pivots

Sprinkler irrigation package selection for center pivot systems depends on soil type and affects water and energy use. A decision support system (DSS) was developed to provide designers and irrigators with a rapid method to analyze many design alternatives. A digitized soils database is accessed by the DSS to determine the predominate soil for each 0.64-ha parcel of land in a center pivot field or site. Flow rates needed to meet crop water requirements and expected application losses are calculated and spatially averaged based on these water requirements for each cell. Uniformity of the irrigation applications are estimated for level land only. Energy usage for various sprinkler packages and power sources can be analyzed. Potential runoff from each cell is displayed by the DSS. The user selects the parameters for multiple system comparisons. The DSS enhances the analyses of management information seldom utilized in current design methods and presents the results of database lookups and numerical model calculations in easy-to-interpret graphical formats. The design can then integrate the users’ attitudes and values regarding energy and water savings.

Center Pivot End Sprinkler Pattern Analysis and Selection

ABSTRACT INCREASED importance is being placed on irri-gating the corners of center pivot fields. A common approach to irrigating these areas involves the use of large end sprinklers on center pivot machines. The question of which end sprinkler to select for a machine is addressed by developing procedures for analyzing the performance of such selections. Generalized application rate curves are presented for common types of end sprinklers. Criteria for evaluating end sprinkler selections are defined to measure the efficiency with which land, water, and energy resources are utilized. The performance of a particular end sprinkler depends not only on the criterion for measurement, but on sprinkler and machine characteristics, and on how well sprinkler and machine are matched, Performance evaluations of sample end sprinkler selections are made for a typical center pivot ma-chine.