Iran is suffering from a state of water bankruptcy. Several factors have contributed to the current water resources bankruptcy, ranging from anthropogenic impacts, such as an inefficient agricultural sector and aggressive withdrawal of groundwater, to climatological impacts. This presentation suggests that water resources mismanagement in Iran should be evaluated beyond the policy-makers decisions, as it recognizes that the bankruptcy has been intensified due to the structural and institutional form of the political system in Iran. This study discusses the roots of the water bankruptcy and identifies four major shortcomings caused by the political system: (1) the absence of public engagement due to the lack of a democratic and decentralized structure; (2) adopting ideological policies in domestic and foreign affairs; (3) conflicts of interest and the multiplicity of governmental policy-makers and sectors; and (4) a state-controlled, resource-dependent economy. Through the development of a generic causal model, this study recommends a systematic transition towards a democratic, decentralized, non-ideological, and economically diverse political governance as the necessary–but not necessarily sufficient–adaptive and sustainable solution for mitigating the impacts of water resources bankruptcy in Iran. The insights highlighted in this presentation could be employed to inform water resources decision-makers and political actors in other non-democratic and ideological political structures struggling with a water resources crisis or bankruptcy.
To accelerate plant breeding genetic gain, spatial heterogeneity must be considered. Previously, design randomizations and spatial corrections have increased understanding of genotypic, spatial, and residual effects in field experiments. This study proposes a two-stage approach for improving agronomic trait genomic prediction (GP) using high-throughput phenotyping (HTP) via unoccupied aerial vehicle (UAV) imagery. The normalized difference vegetation index (NDVI) is measured using a multi-spectral MicaSense camera and ImageBreed. The first stage separates additive genetic effects from local environmental effects (LEE) present in the NDVI throughout the growing season. Considered NDVI LEE (NLEE) are spatial effects from univariate/multivariate two-dimensional splines (2DSpl) and separable autoregressive (AR1) models, as well as permanent environment (PE) effects from random regression models (RR). The second stage leverages the NLEE within genomic best linear unbiased prediction (GBLUP) in two distinct implementations, either modelling an empirical plot-to-plot covariance (L) for random effects or modelling fixed effects (FE). Testing on Genomes-to-Fields (G2F) hybrid maize (Zea mays) field experiments in 2017, 2019, and 2020 for grain yield (GY), grain moisture (GM), and ear height (EH) improves heritability and model fit equally-or-greater than spatial corrections; however, genotypic effect estimation across replicates is not significantly improved. Electrical conductance (EC), elevation, and curvature from a 2019 soil survey significantly improve GP model fit, but less than NLEE. Soil EC and curvature are most correlated to univariate 2DSpl NLEE. Defining L significantly improves genomic heritability and model fit more than setting FE, and RR NLEE can most significantly improve GP for GY and GM.
Carbon, water, and energy cycles are key processes controlling land-atmosphere interactions. Carbon and energy fluxes are both coupled with soil moisture (SM), leading to water-limited and energy-limited regimes. Functional relationships between carbon, water, and energy are key to understanding feedbacks between the land surface and atmosphere and give an estimate of the land-atmosphere coupling strength. Current carbon, water, and energy coupling relationships are commonly estimated at the point or satellite scale. These coupling relationships are expected to vary across scales. We investigated the carbon, water, and energy coupling relationships and the limiting thresholds from the field to satellite scale. We also investigated the effect of weather, seasons, and land cover on carbon-water-energy coupling strength. Carbon (carbon dioxide: CO2), energy (evapotranspiration: ET), and water (SM) were estimated using Eddy covariance and soil moisture monitoring systems at various FLUXNET sites in the Continental United States. These field measurements were used together with LANDSAT, MODIS, SMAP remote sensing satellites estimates of ET, CO2, and SM, respectively. Weather variables were obtained from Daymet (a gridded daily surface weather data product) and weather monitoring systems at the FLUXNET sites. This analysis provided insights to the spatial and temporal variations of the carbon, energy, and SM at different scales.
An autonomous mobile ground-control point (AMGCP) was redesigned and refined for improved collaborative operation with an unmanned aerial vehicle (UAV) to enable calibration of image mosaics from multispectral (MS) and thermal cameras. The AMGCP has built-in reflectance panels and electronically controlled thermal panels that provide high and low reflectance and temperature references that can be used for calibration of reflectance measurements in MS images and temperature measurements in thermal-infrared images. The AMGCP also has an onboard temperature sensor that enables image-based temperature measurements to be compared to ambient temperature so that canopy temperature depression (CTD) can be calculated. The collaborative robotic system consists of the AMGCP and a UAV that have real-time kinematic (RTK) geographic positioning system (GPS) receivers onboard so their precise position can be determined in real time. The system also includes wireless communication capability between the AMGCP and UAV so they can transmit their position and other data to each other during a mission, in which the AMGCP positions itself at multiple locations under the flight path of the UAV, providing multiple instances of reflectance and temperature references in image mosaics collected by the UAV. Testing has shown that reflectance measurements can be calibrated to less than 1% reflectance error, and canopy temperatures of crop plants can be calibrated to within 1.0 C, enabling consistently accurate measurements to be made efficiently and without human intervention in various fields and regions and at different times and dates. This system is also suited to accurate measurement of CTD to facilitate genetic selection relative to various stresses and resilience characteristics like drought tolerance.
The conservation of natural ecosystems is an essential component for sustainable land use (LU). One of the challenges facing society worldwide is climate change, where reduce emissions, and sequestrate greenhouse gases from the atmosphere are fundamental to mitigate its effects. LU change plays a major role in the carbon (C) cycle, and understanding and quantifying its effects is one of the main challenges for effectively implementing climate change mitigation actions. Given this scenario, our objective was to calibrate the Daycent model to estimate the references equilibrium soil organic matter (SOM) for three important Brazilian biomes: Atlantic Forest (AF), Cerrado (CE), and Pampa (PA). Together, they represent 39% of native vegetation area, and over them are concentrated the majority of the agricultural production in Brazil. Estimating the equilibrium for major soil types in the three biomes is fundamental for evaluating C dynamics and the soil C loss regarding LU changes. Data from literature, including SOM, were collected for the three biomes: PA (29°30’S, 54°15’W; soil with sandy loam texture), CE (19°28’S, 44°15’W; very clayey texture) and AF (10°92’S, 37°19’W; sandy texture). Daycent parameters to represent the biomes biophysical properties were initially set up with values from local literature. Measured SOM was then employed during the calibration of the Daycent model. We ran the model for 6,000 years for the equilibrium simulations, obtaining the stabilization of the SOM compartments (active, slow, and passive). For the biomes’ biophysical properties the parameters for maximum potential production (PRDX) were adjusted for each biome, PA with 0.92 g C m-2 , AF with 1.5 g C m-2 and CE with 0.9 g C m-2 (default = 0.5 g C m-2). The relative error between measured and predicted total SOM was lower than 2% for all biomes, thus representing the equilibrium properly for the study conditions. The largest C compartment of the biomes (slow organic matter in the soil) had 71.7% for AF, 68.5% for PA, and 63.7% for CE of the total SOM. The highest SOM values were found in the CE, with 53 Mg C ha-1, followed by the PA with 37 Mg C ha-1, and in the AF with 35 Mg C ha-1. Eventual LU changes will impact the SOM equilibrium of these native vegetation, but sustainable practices must take place to avoid C losses as far as possible.
Following the establishment of the first STEM school in Egypt (in 2011), the Egyptian Ministry of Education and the USAID-funded Egypt’s STEM School Project began joint work creating a public STEM high school model, supported by US STEM education experts, that addresses 11 major Grand Challenges (GCs) identified by Egyptians. In 2018, the Egyptian Ministry of Higher Education and Scientific Research and US STEM faculty, coordinated by 21PSTEM, began creating 4-year undergraduate and 1-year post-Bachelor programs to prepare teachers for these schools, under the USAID-funded STEM Teacher Education and School Strengthening Activity (STESSA), also based on the GCs. Traditional Earth science alone was not sufficient to prepare students to meet these transdisciplinary GCs. Instead, the STEM high schools, as well as the graduate and undergraduate programs, use a transdisciplinary curriculum, with biology, chemistry, physics, Earth science, and math taught every semester. The content is further integrated every semester in capstone project experiences. These curricula were jointly developed by US and Egyptian STEM content experts who also did teacher training. These STEM schools have been a major success, catapulting Egyptian youth into wins at international STEM competitions and earning them admission to elite universities around the world. As the schools developed, the Ministry of Education and 21PSTEM (which implements STESSA) found that US-Egyptian professional development helped ease teachers’ transition to the integrated curriculum. But a growing number of STEM high schools made a new teacher pipeline imperative. US and Egyptian faculty are developing new 4-year undergraduate programs to prepare teachers in 5 STEM disciplines. These programs echo the high school curriculum and the GCs, but are more explicitly transdisciplinary, beginning with 6 integrated STEM courses in the first two years. Earth science plays a prominent role in these integrated courses and Earth science faculty from the US and Egypt have played a significant role in course development. We will report on the development and progress of the first two of these transdisciplinary courses, and the potential of truly transdisciplinary course work to develop stronger Earth scientists, ready to meet grand challenges in any nation on Earth.
The water production function (Ky) defines the quantitative response of the water deficit to overall yield during a given phenological stage and is a key parameter in deficit irrigation planning in water-scarce scenarios A three-year field trials were carried out on clay loam soil of semiarid India in complete ran-domized blocks with 27 treatments and 2 replicates. Treatments consisted of applying irrigation depths equivalents to 100%, 70% and 40% replenishments of the soil water from the root zone at development, mid-season and end stages of sugarcane. Each treatment was defined to investigate effect of specified water depth on specified phenological stage independently. The actual evapotranspi-ration (ETa) was determined by the field water balance of the root zone while the Ky were calculated according to the FAO-33 report methodology. In particular, during the mid-season and development stages, the referred yield decreases have been shown to be responsive to water deficits. Seasonal Ky values ranged from 1.05 to 1.18 over 3 seasons with an average value of 1.11 showing sugarcane intolerant to water deficit (Ky > 1). Based on the phenological stage ETa, Ky values for development, mid-season and end stages were 0.31, 0.76 and 0.07, respectively. Ky values calculated for development and mid-season stage in this research was different than FAO-33. It could be concluded that during mid-season, water deficit must be avoided; 30 % and 60 % water deficit are appropriate if applied respectively in the in development and end stages.
Most remote sensing-based surface energy balance (SEB) models are limited by data availability and physical constraints to fully capture the non-linear and temporally varying nature of atmospheric, biophysical, and environmental controls on evapotranspiration (ET). As such, currently, no single SEB model is considered to work best under all conditions particularly in irrigated croplands where surface moisture conditions could change dramatically in a short amount of time. Hence, irrigation water management based on a single remotely sensed ET model is often required to cope with model limitations and data latency issues, which could lead to unsustainable and unreliable accounting of water use over time. The recent inception of ensemble-based ET modeling takes the advantage of the strengths of the several SEB models under different conditions and is found to perform better as compared to an Individual model. Yet, challenges remain in how high-temporal ET outputs from different models are accurately assembled in a way that yields the most reliable estimates of ET across any environmental and surface conditions. Specifically, existing simple or Bayesian average and machine learning-based ensemble approaches have not been able to optimally utilize the comprehensive suite of existing SEB models and the availability of multiple remotely sensed datasets. Here, we discuss the utility of convolutional neural networks (CNNs) to assemble the outputs from a host of SEB models that can robustly capture the non-linear dynamics of ET under all conditions. We will also discuss the advantage and potential limitations of using the CNN-based ensemble ET modeling framework with respect to the individual, simple or Bayesian average, and other machine learning approaches and their implications for use in allocating water use across critically dry regions. Several ensemble models will be trained using eddy covariance flux data globally and will be evaluated based on their ability to estimate ET from MODIS and Landsat sensors with both individual and fused products and minimal weather inputs. The results can provide useful insights into how multiple datasets and SEB models could be optimally utilized to accurately monitor crop water status and support sustainable water resource management in drylands.
Knowing how many plants have germinated in a farmer’s or researcher’s field is central to crop management and research. Plants are commonly counted by walking the field and counting the plants in each row. This technique is labor-intensive, slow, and error-prone. Automating stand counts using RGB imagery from Unmanned Aerial Vehicles (UAV) is an obvious solution. We propose DeepMaizeCounter, a robust computational system that provides accurate stand counts for research and production fields from imagery captured by freely flown, inexpensive drones and processed with an inexpensive computer. DeepMaizeCounter exploits mosaics computed from RGB videos, using a YOLOv4 model that is trained to recognize seedling maize plants in the V2–V10 growth stages (approximately 10–40cm in height), singly or in groups of two and three plants, and determines its accuracy on these classes of seedlings. We evaluated DeepMaizeCounter against in-field and on-frame manual stand counts for a number of different maize lines in both nursery and production fields. DeepMaizeCounter can reliably distinguish corn from weeds and other grasses, counting only the maize. The network is light and able to run 175 test frames in 6 seconds, or 29 frames per second. This opens the prospect that DeepMaizeCounter can eventually be deployed on cheap platforms for real-time counting.
Plant growth and development is impacted by the ability to capture resources including sunlight, determined in part by the arrangement of plant parts throughout the canopy. This is a very complex trait to describe, but has a major impact on downstream traits such as biomass or grain yield per acre. Though some is known about genetic factors contributing to leaf angle, maturity, and leaf size and number, these discrete traits do not encompass the structural complexity of the canopy. In addition, modeling and prediction for plant developmental traits using genomics or phenomics are usually conducted separately. We have developed proof-of-concept models that incorporate spatio-temporal factors from drone-acquired LiDAR features in a maize diversity panel to predict plant growth and development over time to improve our understanding of the biology of canopy formation and development. Briefly, voxel models for probability of beam penetration into the foliage were generated from 3D LiDAR scans collected at seven dates throughout crop canopy development. From the same plots, key architectural features of the maize canopy were measured by hand: stand count; plant, tassel, and flag leaf height; anthesis and silking dates; ear leaf, total leaf, and largest leaf number; and largest leaf length and width. We develop a self-supervised autoencoding neural network architecture that separately encodes plant temporal growth patterns for individual genotypes and plant spatial distributions for each plot. Then, leveraging the resulting latent space encoding of the LiDAR scans, we train and demonstrate accurate prediction of hand-measured crop traits.
Evaluation of spatially distributed crop coefficient (Kc) for estimating evapotranspiration (ETc) based on remotely sensed imagery has become an essential topic in managing the demand for agricultural water. Currently, satellite (MODIS, Landsat, etc.) imageries are not insufficient to detect variability within the small agricultural field due to its lack of desired spatial and temporal resolutions. Unmanned Aerial Vehicle (UAV) equipped with various sensors like Multispectral (MS), Thermal, and Hyperspectral cameras is becoming an emerging technology to overcome these limitations over small agricultural fields. A field experiment is carried out in the Agricultural and Food Engineering (AGFE) Department, IIT Kharagpur, to estimate Kc over the small Agri. Field using UAV-based MS cameras during Kharif (monsoon) 2019-2020 season. Lysimeters are used for estimating daily ETc for conventionally irrigated paddy crops. Reference evapotranspiration (ET0) is also calculated using the weather data of the study area. High-resolution multispectral imageries are acquired using a quad-copter UAV. The imageries are pre-processed using Pix4Dmapper software, and various vegetation indices (such as NDVI, TNDVI, NDRE, RVI, GNDVI, and LCI) are evaluated. The vegetation indices (VIs) are correlated with ground truth Kc values and spatially distributed Kc maps for the whole study area are generated based upon the excellent correlation between the VIs and ground Kc. The spatial Kc maps clearly show the variation in Kc within the plots and will be helpful for the calculation of Kc for any field without a lysimeter experiment. Generated Kc maps describe the crop water demand by visual color variations within the field. This approach may be helpful in understanding the variability in crop water requirements within the field Keywords: UAV, Crop Coefficient (Kc), Crop Evapotranspiration (ETc), Vegetation Indices, Remote Sensing.
Water stress mapping in crops and its spatial disparity study at field scale is important for precise management of irrigation. Results obtained from conventional airborne practice (balloons, airplanes, and satellites) are less acceptable for timely irrigation management due to lack in spatial and temporal resolutions. Unmanned Aerial Vehicle (UAV) equipped with multispectral (MS) and thermal cameras with higher spectral and temporal resolutions can be used as a promising tool for preparing water stress maps under different water deficit conditions. In this study, Water Deficit Index (WDI) maps are generated at different days after sowing (DAS) in wheat crops under three different water conditions (WI (well water), WS1(irrigation at 5 days’ interval), and WS2 (irrigation at 11 days’ interval)) using the concept of Vegetation Index Trapezoid (VIT) using UAV based thermal and MS imageries. The UAV is flown at 60m altitude during the Rabi season 2018-19. After pre-processing of images in Pix4dMapper, nine vegetation indices are calculated from MS images and one of the indices, Normalized Green Red Difference Index (NGRDI) is selected based on the higher correlation with ground truth data (R2 greater than 0.5) and visual interpretation according to the real field condition to construct the VIT. Vegetation index and temperature values are calculated for four points of VIT by using four boundary conditions such as bare soil with (1) dry and (2) wet conditions, and full vegetation with (3) well-watered and (4) water stress conditions. By using the ArcGIS, geo-referencing of thermal images with respect to MS images is done to get the exact overlap of both images, and resampling of thermal and MS images are also carried out to get the same pixel size. WDI values are estimated using VIT of the surface-air temperature difference and NGRDI, and WDI maps are generated from the UAV-based thermal and MS imageries for potential detection of crop water stress. The conventional Crop Water Stress Index (CWSI) which is solely based on the crop canopy temperature is outperformed by the WDI, which is integration of composite land surface temperature (LST) and degree of greenness, and could be effective enough for irrigation water management. Keywords: UAV, Multispectral and Thermal imageries, NGRDI, WDI, and Wheat crop.
Suitable planting areas for winter wheat in north China are expected to shift northwardly due to climate change, however, the increasing extreme events and the deficiency of water supply are threatening the security of planting system. Thus, based on predicted climate data for 2021–2050 under the SSP1-2.6, SSP3-7.0, and SSP5-8.5 emission scenarios, as well as historical data from 1961–1990, we use four critical parameters of percentage of extreme minimum temperature occurrence, first day of the overwintering period (FD), sowing date (SD), and precipitation before winter (PBW) to determine the planting boundary of winter wheat. The results show that, the frequency of extreme minimum temperature occurrence is expected to decrease in the North winter wheat area, which will result in a northward movement of the western part of northern boundary by 73, 94, and 114 km on average, as well as FD delays ranging from 6.0 to 10.5 days. Moreover, the agrometeorological conditions in the Huang-Huai winter wheat area are expected to exhibit more pronounced changes than the rest of the studied areas, especially near the southern boundary, which is expected to retreat by approximately 213, 215, and 233 km northwardly. The north boundary is expected to move 90–140 km northward. Therefore, the change in southern and northern boundaries will lead the potential planting areas of the entire North winter wheat area to increase by 10,700 and 28,000 km2 on average in the SSP3-7.0 and SSP5-8.5 scenarios but decrease 38,100 km2 in the SSP1-2.6 scenario.
Shifting cultivation is an important driver of forest disturbance in the tropics. However, studies of shifting cultivation are limited and current area estimates of shifting cultivation are highly uncertain. Although Southeast Asia is a hotspot of shifting cultivation, there are no national maps of shifting cultivation in Southeast Asia at moderate or high resolution (less than or equal to 30 m). Monitoring shifting cultivation is challenging because the slash-and-burn events are highly dynamic and small in size. In this research, we present and test an approach to monitoring shifting cultivation using Landsat data on Google Earth Engine. CCDC-SMA (Continuous Change Detection and Classification - Spectral Mixture Analysis) is used to detect forest disturbances. Then, these disturbances are attributed by combining time series analysis, object-based image analysis (OBIA), and post-disturbance land-cover classification. Forest disturbances are assigned to shifting cultivation, new plantation, deforestation, severe drought, and subtle disturbance annually from 1991 to 2020 at a 30-meter resolution for the country of Laos. The major forest disturbances in 1991-2020 are mapped with an overall accuracy of 85%. Shifting cultivation is mapped with a producer’s accuracy of 88% and a user’s accuracy of 80%. The margin of error of the sampling-based area estimate of Shifting cultivation is 5.9%. The area estimates indicate that shifting cultivation is the main type of forest-disturbance in Laos, affecting 32.9% ± 1.9% of Laos over the past 30 years. To study the development of shifting cultivation over time, the area of slash-and-burn events is estimated at 5-year intervals of 1991-2020 with all margins of error less than 17%. Results show that the area of slash-and-burn activities in Laos increased in the most recent 5-year period. We believe that the methods developed and tested in Laos can be applied to other regions.
The traditional method of measuring the lettuce height is a manual measurement with instruments, which is greatly affected by human error.At present, researchers have proposed to use color cameras to obtain RGB images of lettuce, and to obtain the height of lettuce from the images. However, these tasks usually require camera calibration or a reference object with a known height, which is somewhat restrictive. Considering that deep neural networks have a powerful ability to feature extraction and expression, without camera calibration and reference objects, we try to use four networks of image recognition to explore the effect of deep learning on abstracting the lettuce height from RGB images. On the test set, including 80 images and height from 0.9 cm to 7.5 cm, we achieve a good result with a mean absolute error of 1.22 mm.
In order to count soybean seeds quickly and accurately, improve the speed of seed test and the level of soybean breeding , this dissertation developed a method of soybean seed counting based on VGG-Two (VGG-T). Firstly, in view of the lack of available image dataset in the field of soybean seed counting, a fast target point labeling method of combining pre-annotation based on digital image processing technology with manual correction annotation is proposed to speed up the establishment of publicly available soybean seed image dataset with annotation. This method only takes 197 minutes to mark 37,563 seeds, which saves 1,592 minutes than ordinary manual marking and replaces 96% of manual workload. Secondly, a method that would combine the density estimation-based methods and the convolution neural network (CNN)-based methods is developed to accurately estimate the seed count from an individual threshed seed image with a single perspective. Finally, the model is tested, and verify the effectiveness of the algorithm through three comparative experiments (with and without data enhancement, VGG16 and VGG-T, multiple sets of test set), which respectively provided 0.6 and 0.2 mean absolute error (MAE) in the original image and patch cases, while mean squared error (MSE) is 0.6 and 0.3. Compared with traditional image image morphology operations, ResNet18, ResNet18-T and vgg16, this method improves the accuracy of soybean seed counting.
To obtain phenotypic parameters by means of lossy measurement, we proposed a comprehensive and integrated approach to predict different parameters of four varieties of lettuces. By building different prediction models, we required predicted value of five phenotypic parameters of lettuce. Test results indicate that prediction models we have constructed are reliable and feasible. In addition,our methods can be better transferred to the research of other crops, and producers can adjust the growing environment of crops in time, so as to obtain higher yield.
Focusing on non-destructive and automated acquisition of plant phenotypic parameters,this extended abstract proposed an end-to-end deep RNN based network structure for single perspective sparse raw point cloud regression task called DRN. It has been proven to achieve accuracy improvements in PointNet++ and PonitCNN when it comes to regression of lettuce plant height. We believe DRN structure is suitable for feature extraction from plant point cloud data and regression of spatial distance related plant phenotypes like plant height.