Greenhouse cultivation has evolved from simple covered rows of open-fields crops to highly sophisticated controlled environment agriculture (CEA) facilities that projected the image of plant ...factories for urban agriculture. The advances and improvements in CEA have promoted the scientific solutions for the efficient production of plants in populated cities and multi-story buildings. Successful deployment of CEA for urban agriculture requires many components and subsystems, as well as the understanding of the external influencing factors that should be systematically considered and integrated. This review is an attempt to highlight some of the most recent advances in greenhouse technology and CEA in order to raise the awareness for technology transfer and adaptation, which is necessary for a successful transition to urban agriculture. This study reviewed several aspects of a high-tech CEA system including improvements in the frame and covering materials, environment perception and data sharing, and advanced microclimate control and energy optimization models. This research highlighted urban agriculture and its derivatives, including vertical farming, rooftop greenhouses and plant factories which are the extensions of CEA and have emerged as a response to the growing population, environmental degradation, and urbanization that are threatening food security. Finally, several opportunities and challenges have been identified in implementing the integrated CEA and vertical farming for urban agriculture.
Remote sensing of evapotranspiration (ET) can help detect, map and provide guidance for crop water needs in irrigated lands. Two remote sensing ET models based on thermal infrared (TIR), the ...Two-Source Energy Balance (TSEB) and the Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC), were tested for accuracy, and bias at fine (1m) and moderate (30–120m) spatial scales. Airborne and Landsat data were collected over Maricopa, Arizona in 2009 and 2011 as part of a cotton irrigation scheduling study. Based on soil moisture observations at 112 locations across 4.9ha and image data spanning two growing seasons, TSEB and METRIC were found similarly accurate at both fine and moderate scales with average discrepancies no more than 1.9mm/day. Tests at 1-m scales showed that TSEB and METRIC model sensitivities were seasonally correlated, with greater sensitivity modeled by METRIC in early growth and slightly greater sensitivity by TSEB at maturity. Time integration of flux estimates was done by assuming constant evaporative fraction and was also tested for 2011 data using ground-based TIR radiometers; this latter approach improved daily ET estimates by 0.8mm/day or better in two cases. Time-series assessment of the utility of using evaporative fraction as a water-stress indicator was tested using Landsat data and both TSEB and METRIC. Two early season water depletion events were detected and none in mid-season. The impact of overpass frequency upon ET estimates was tested for the field as a whole and found that cumulative ET estimates were significantly affected, up to 200mm out of ~1000mm consumed. Results from this study showed that for ET accuracy, TSEB and METRIC perform similarly. METRIC is preferred when model ancillary data are sparse, while TSEB is preferred when support data are plentiful. Future ET modeling should consider implementing both to take advantage of their seasonally dependent sensitivities.
•Thermal infrared-based ET remote sensing models, TSEB and METRIC both accurate to 1.9mm/day.•TSEB and METRIC model sensitivities seasonally correlated.•8-day satellite overpass frequency significantly improves ET estimates compared to 16-days.
High-throughput plant phenotyping (HTPP) involves the application of modern information technologies to evaluate the effects of genetics, environment, and management on the expression of plant traits ...in plant breeding programs. In recent years, HTPP has been advanced via sensors mounted on terrestrial vehicles and small unoccupied aircraft systems (sUAS) to estimate plant phenotypes in several crops. Previous reviews have summarized these recent advances, but the accuracy of estimation across traits, platforms, crops, and sensors has not been fully established. Therefore, the objectives of this review were to (1) identify the advantages and limitations of terrestrial and sUAS platforms for HTPP, (2) summarize the different imaging techniques and image processing methods used for HTPP, (3) describe individual plant traits that have been quantified using sUAS, (4) summarize the different imaging techniques and image processing methods used for HTPP, and (5) compare the accuracy of estimation among traits, platforms, crops, and sensors. A literature survey was conducted using the Web of ScienceTM Core Collection Database (THOMSON REUTERSTM) to retrieve articles focused on HTPP research. A total of 205 articles were obtained and reviewed using the Google search engine. Based on the information gathered from the literature, in terms of flexibility and ease of operation, sUAS technology is a more practical and cost-effective solution for rapid HTPP at field scale level (>2 ha) compared to terrestrial platforms. Of all the various plant traits or phenotypes, plant growth traits (height, LAI, canopy cover, etc.) were studied most often, while RGB and multispectral sensors were most often deployed aboard sUAS in HTPP research. Sensor performance for estimating crop traits tended to vary according to the chosen platform and crop trait of interest. Regardless of sensor type, the prediction accuracies for crop trait extraction (across multiple crops) were similar for both sUAS and terrestrial platforms; however, yield prediction from sUAS platforms was more accurate compared to terrestrial phenotyping platforms. This review presents a useful guide for researchers in the HTPP community on appropriately matching their traits of interest with the most suitable sensor and platform.
Engineering technologies for site-specific irrigation management (SSIM) have already been developed for applications in precision irrigation. However, further studies are needed to identify scenarios ...where SSIM leads to better agronomic outcomes than conventional uniform irrigation management (CUIM). The objective was to conduct a long-term simulation study to compare SSIM and CUIM given spatial soil variability at the Maricopa Agricultural Center (MAC) in Arizona. More than 500 surface soil samples were collected across a 730-ha area of the MAC from 1984 to 1987. A more detailed soil data set was more recently obtained across a 5.9-ha area at a MAC location designated for SSIM studies. Ordinary kriging was used for spatial interpolation of soil hydraulic properties within
10
m
×
10
m
zones across the MAC, and 11 field parcels with an area of approximately 60 ha were delineated on the MAC quarter sections. Using an agroecosystem model, simulations of cotton production at the zone level with a 30-year weather record were conducted using a field-tested algorithm to optimize irrigation schedules for SSIM and CUIM. Long-term seed cotton yield, irrigation requirements, water use efficiency, and marginal net return for SSIM and CUIM strategies were often not different (
p
>
0.05
). Differences in seed cotton yield and irrigation requirements among the tested irrigation strategies were less than 11% and 6%, respectively, and within the typical range of model error. Most soils on the MAC have enough available water holding capacity to sustain cotton production at full potential with weekly CUIM, and advantages of SSIM were not consistently demonstrated by the simulations.
Irrigated agriculture consumes a large amount of groundwater resources with a huge energy requirement, and this has seriously restricted the development of green and efficient agriculture in China. ...However, recent studies on energy consumption for irrigation focus mainly on individual irrigation systems or single wells, and few spatial-temporal energy assessments have been carried out at regional scale. This is needed for effective management of regional energy consumption for groundwater utilization. Based on single-well pumping method, a distributed energy consumption model for groundwater irrigation (DPE_GI) was proposed in this study. The North China Plain (NCP) was selected as the research area, which is a typical groundwater irrigated area and has severe issues with aquifer depletion. The results showed that the average annual energy consumption for groundwater pumping was 13.67 billion kW h, and the energy consumption per area was 1122.4 kW h hm−2 under the winter wheat - summer maize rotation system in NCP. Current groundwater pumping energy consumption in the NCP is 2.9 times of the initial value in 1986, and the NCP has already become the world's largest energy consumer for groundwater irrigation. Due to the uncertainty of precipitation, energy consumption for irrigation fluctuates per growing season. Groundwater level also impacts energy consumption. Popularizing water-saving irrigation technology such as drip or sprinkler irrigation, changing cropping systems and habits can effectively reduce energy consumption for irrigation.
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•The North China Plain is the largest energy consumption area for irrigation over world.•A distributed pumping energy model for groundwater irrigation (DPE_GI) was proposed.•This model can estimate the energy consumption of groundwater irrigation at county level.•Groundwater condition, planting and climate influence irrigation energy consumption together.
Improvement of crop water use efficiency (CWUE), defined as crop yield per volume of water used, is an important goal for both crop management and breeding. While many technologies have been ...developed for measuring crop water use in crop management studies, rarely have these techniques been applied at the scale of breeding plots. The objective was to develop a high-throughput methodology for quantifying water use in a cotton breeding trial at Maricopa, AZ, USA in 2016 and 2017, using evapotranspiration (ET) measurements from a co-located irrigation management trial to evaluate the approach. Approximately weekly overflights with an unmanned aerial system provided multispectral imagery from which plot-level fractional vegetation cover ( f c ) was computed. The f c data were used to drive a daily ET-based soil water balance model for seasonal crop water use quantification. A mixed model statistical analysis demonstrated that differences in ET and CWUE could be discriminated among eight cotton varieties ( p < 0 . 05 ), which were sown at two planting dates and managed with four irrigation levels. The results permitted breeders to identify cotton varieties with more favorable water use characteristics and higher CWUE, indicating that the methodology could become a useful tool for breeding selection.
Soils lie at the interface between the atmosphere and the subsurface and are a key component that control ecosystem services, food production, and many other processes at the Earth's surface. There ...is a long-established convention for identifying and mapping soils by texture. These readily available, georeferenced soil maps and databases are used widely in environmental sciences. Here, we show that these traditional soil classifications can be inappropriate, contributing to bias and uncertainty in applications from slope stability to water resource management. We suggest a new approach to soil classification, with a detailed example from the science of hydrology. Hydrologic simulations based on common meteorological conditions were performed using HYDRUS-1D, spanning textures identified by the United States Department of Agriculture soil texture triangle. We consider these common conditions to be: drainage from saturation, infiltration onto a drained soil, and combined infiltration and drainage events. Using a k-means clustering algorithm, we created soil classifications based on the modeled hydrologic responses of these soils. The hydrologic-process-based classifications were compared to those based on soil texture and a single hydraulic property, Ks. Differences in classifications based on hydrologic response versus soil texture demonstrate that traditional soil texture classification is a poor predictor of hydrologic response. We then developed a QGIS plugin to construct soil maps combining a classification with georeferenced soil data from the Natural Resource Conservation Service. The spatial patterns of hydrologic response were more immediately informative, much simpler, and less ambiguous, for use in applications ranging from trafficability to irrigation management to flood control. The ease with which hydrologic-process-based classifications can be made, along with the improved quantitative predictions of soil responses and visualization of landscape function, suggest that hydrologic-process-based classifications should be incorporated into environmental process models and can be used to define application-specific maps of hydrologic function.
Ecophysiological crop models encode intra-species behaviors using parameters that are presumed to summarize genotypic properties of individual lines or cultivars. These genotype-specific parameters ...(GSP's) can be interpreted as quantitative traits that can be mapped or otherwise analyzed, as are more conventional traits. The goal of this study was to investigate the estimation of parameters controlling maize anthesis date with the CERES-Maize model, based on 5,266 maize lines from 11 plantings at locations across the eastern United States. High performance computing was used to develop a database of 356 million simulated anthesis dates in response to four CERES-Maize model parameters. Although the resulting estimates showed high predictive value (R2 = 0.94), three issues presented serious challenges for use of GSP's as traits. First (expressivity), the model was unable to express the observed data for 168 to 3,339 lines (depending on the combination of site-years), many of which ended up sharing the same parameter value irrespective of genetics. Second, for 2,254 lines, the model reproduced the data, but multiple parameter sets were equally effective (equifinality). Third, parameter values were highly dependent (p<10-6919) on the sets of environments used to estimate them (instability), calling in to question the assumption that they represent fundamental genetic traits. The issues of expressivity, equifinality and instability must be addressed before the genetic mapping of GSP's becomes a robust means to help solve the genotype-to-phenotype problem in crops.
The pyfao56 software package is a Python-based implementation of (1) the American Society of Civil Engineers (ASCE) Standardized Reference Evapotranspiration Equation and (2) the Food and ...Agricultural Organization of the United Nations (FAO) Irrigation and Drainage Paper No. 56 (FAO-56) dual crop coefficient methodology. The software was initially developed to support crop water use estimation and irrigation scheduling for field research at the Maricopa Agricultural Center in Arizona. Recent efforts to generalize and modularize the software design have increased its applicability and relevance for broader scientific studies on crop evapotranspiration and irrigation management worldwide.
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