Climate impact from agricultural management practices in the Canadian Prairies: Carbon equivalence due to albedo change.

It is generally accepted that land use and land management practices impact climate change through sequestration of carbon in soils, but modulation of surface energy budget can also be important. Using Landsat data to characterize cropland albedos in Canada's three prairie soil zones, this study estimates the atmospheric carbon equivalent drawdown of albedo radiative forcing for three management practices: 1) moving from conventional tillage to no-till, 2) eliminating summer fallow in crop rotations, and 3) growing crops with higher albedos. In a 50-year time horizon, conversion from conventional tillage to no-till results in a total equivalent atmospheric CO2 (CO2-eq) drawdown of 1.0-1.5 kg m-2, and conversion from summer fallow to crops results in CO2-eq drawdown of 1.1-2.4 kg m-2. Conversion of summer fallow to crops results in different magnitudes of CO2-eq drawdown depending on specific crops. Lentils, peas, and canola have relatively higher albedo than that of spring wheat and flax; hence, a larger magnitude of CO2-eq drawdown results when they replace summer fallow in the rotation. For the management changes from 1990 to 2019 for the whole Canadian Prairies, albedo changes induced a CO2-eq drawdown of about 179.3 ± 20.9 Tg due to increased area of no-till, and 101.6 ± 9.5 Tg due to reduced area under fallow. The study shows that the magnitudes of CO2-eq drawdown due to albedo change are comparable to that due to soil carbon sequestration. Therefore, it is important to account for cropland albedo changes in assessing the potential of agricultural management practices to mitigate climate change.

Generating Time-Series LAI Estimates of Maize Using Combined Methods Based on Multispectral UAV Observations and WOFOST Model.

Green leaf area index (LAI) is an important variable related to crop growth. Accurate and timely information on LAI is essential for developing suitable field management strategies to mitigate risk and boost yield. Several remote sensing (RS) based methods have been recently developed to estimate LAI at the regional scale. However, the performance of these methods tends to be affected by the quality of RS data, especially when time-series LAI are required. For crop LAI estimation, supplementary growth information from crop model is helpful to address this issue. In this study, we focus on the regional-scale LAI estimations of spring maize for the entire growth season. Using time-series multispectral RS data acquired by an unmanned aerial vehicle (UAV) and the World Food Studies (WOFOST) crop model, three methods were applied at different crop growth stages: empirical method using vegetation index (VI), data assimilation method and hybrid method. The VI-based method and assimilation method were used to generate time-series LAI estimations for the whole crop growth season. Then, a hybrid method specially for the late-stage LAI retrieval was developed by integrating WOFOST model and data assimilation. Using field-collected LAI data in Hongxing Farm in 2014, the performances of these three methods were evaluated. At the early stage, the VI-based method (R2 = 0.63, RMSE = 0.16, n = 36) achieved higher accuracy than the assimilation method (R2 = 0.54, RMSE = 0.52, n = 36), whereas at the mid stage, the assimilation method (R2 = 0.63, RMSE = 0.46, n = 28) showed higher accuracy than the VI-based method (R2 = 0.41, RMSE = 0.51, n = 28). At the late stage, the hybrid method yielded the highest accuracy (R2 = 0.63, RMSE = 0.46, n = 29), compared with the VI-based method (R2 = 0.19, RMSE = 0.43, n = 28) and the assimilation method (R2 = 0.20, RMSE = 0.44, n = 29). Based on the results above, we considered a combination of the three methods, i.e., the VI-based method for the early stage, the assimilation method for the mid stage, and the hybrid method for the late stage, as an ideal strategy for spring-maize LAI estimation for the entire growth season of 2014 in Hongxing Farm, and the accuracy of the combined method over the whole growth season is higher than that of any single method.

Carbon dynamics on agricultural land reverting to woody land in Ontario, Canada.

The 2015 Paris Agreement reinforces the importance of the land sector and its contribution to greenhouse gas (GHG) reductions. Thus, there is growing interest in improving estimates of the GHG balance in response to land-use changes (LUCs) involving agriculture and forestry, for national-scale reporting, and for carbon (C) offsets. Large agricultural areas in Europe, Russia and North America are reverting to forest, either naturally or through planting, after abandonment of agricultural land, and this trend may have a substantial impact on carbon budgets. We report results of a pilot project in the Mixedwood Plains ecozone of eastern Canada to analyze the change in the C budget on a landscape over 15 years on abandoned cropland where woody vegetation is regenerating. Thirty-six plots (2 km × 2 km) with paired aerial photographs taken circa 1994 and circa 2008 at a scale of 1:10,000 or larger were randomly selected from the 20 km × 20 km National Forest Inventory (NFI) grid. A spatially-explicit version of the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3) was used to estimate impacts of LUC on C stocks and fluxes. Polygons identifying areas of LUC within each photo plot were delineated, classified, and evaluated to provide input data for the model. The rate of C accumulation in our study area was found to be relatively constant over the entire simulation period, at 1.07 Mg C/ha/yr. Abandoned agricultural land reverting to woody lands could play an important role in regional and national C sequestration in Canada, but more research is required to quantify the areal extent of this LUC.

Integration of multi-disciplinary geospatial data for delineating agroecosystem uniform management zones.

Understanding agricultural ecosystems and their complex interactions with the environment is important for improving agricultural sustainability and environmental protection. Developing the necessary understanding requires approaches that integrate multi-source geospatial data and interdisciplinary relationships at different spatial scales. In order to identify and delineate landscape units representing relatively homogenous biophysical properties and eco-environmental functions at different spatial scales, a hierarchical system of uniform management zones (UMZ) is proposed. The UMZ hierarchy consists of seven levels of units at different spatial scales, namely site-specific, field, local, regional, country, continent, and globe. Relatively few studies have focused on the identification of the two middle levels of units in the hierarchy, namely the local UMZ (LUMZ) and the regional UMZ (RUMZ), which prevents true eco-environmental studies from being carried out across the full range of scales. This study presents a methodology to delineate LUMZ and RUMZ spatial units using land cover, soil, and remote sensing data. A set of objective criteria were defined and applied to evaluate the within-zone homogeneity and between-zone separation of the delineated zones. The approach was applied in a farming and forestry region in southeastern Ontario, Canada, and the methodology was shown to be objective, flexible, and applicable with commonly available spatial data. The hierarchical delineation of UMZs can be used as a tool to organize the spatial structure of agricultural landscapes, to understand spatial relationships between cropping practices and natural resources, and to target areas for application of specific environmental process models and place-based policy interventions.

Biomarkers for patients with Wilms tumor: a review.

Wilms tumor, originating from aberrant fetal nephrogenesis, is the most common renal malignancy in childhood. The overall survival of children is approximately 90%. Although existing risk-stratification systems are helpful in identifying patients with poor prognosis, the recurrence rate of Wilms tumors remains as high as 15%. To resolve this clinical problem, diverse studies on the occurrence and progression of the disease have been conducted, and the results are encouraging. A series of molecular biomarkers have been identified with further studies on the mechanism of tumorigenesis. Some of these show prognostic value and have been introduced into clinical practice. Identification of these biomarkers can supplement the existing risk-stratification systems. In the future, more biomarkers will be discovered, and more studies are required to validate their roles in improving the detection rate of occurrence or recurrence of Wilms tumor and to enhance clinical outcomes.

Using spatio-temporal fusion of Landsat-8 and MODIS data to derive phenology, biomass and yield estimates for corn and soybean.

The Simple Algorithm for Yield estimates (SAFY) is a crop yield model that simulates crop growth and biomass accumulation at a daily time step. Parameters in the SAFY model can be determined from literature, in situ measurements, or optical remote sensing data through data assimilation. For effective determination of parameters, optical remote sensing data need to be acquired at high spatial and high temporal resolutions. However, this is challenging due to interference of cloud cover and rather long revisiting cycles of high resolution satellite sensors. Spatio-temporal fusion of multi-source remote sensing data may represent a feasible solution. Here, crop phenology-related parameters in the SAFY model were derived using an improved Two-Step Filtering (TSF) model from remote sensing data generated through spatio-temporal fusion of Landsat-8 and Moderate Resolution Imaging Spectroradiometer (MODIS) data. Remaining parameters were determined through an optimization procedure using the same dataset. The SAFY model was then used for dry aboveground biomass and yield estimation at a subfield scale for corn (Zea mays) and soybean (Glycine max). The results show that the improved TSF method is able to determine crop phenology stages with an error of <5 days. After calibration, the SAFY model can reproduce daily Green Leaf Area Index (GLAI) effectively throughout the growing season and estimate crop biomass and yield accurately at a subfield scale using three Landsat-8 and 10 MODIS images acquired for the season. This approach improves the accuracy of biomass estimation by about 4% in relative Root Mean Square Error (RRMSE), compared with the SAFY model without forcing the phenology-related parameters. The RMSE of yield estimation is 146.33 g/m2 for corn and 82.86 g/m2 for soybean. The proposed framework is applicable for local-scale or field-scale phenology detection and yield estimation.

Capability of crop water content for revealing variability of winter wheat grain yield and soil moisture under limited irrigation.

Winter wheat (Triticum aestivum L.) is a major crop in the Guanzhong Plain, China. Understanding its water status is important for irrigation planning. A few crop water indicators, such as the leaf equivalent water thickness (EWT: g cm-2), leaf water content (LWC: %) and canopy water content (CWC: kg m-2), have been estimated using remote sensing techniques for a wide range of crops, yet their suitability and utility for revealing winter wheat growth and soil moisture status have not been well studied. To bridge this knowledge gap, field-scale irrigation experiments were conducted over two consecutive years (2014 and 2015) to investigate relationships of crop water content with soil moisture and grain yield, and to assess the performance of four spectral process methods for retrieving these three crop water indicators. The result revealed that the water indicators were more sensitive to soil moisture variation before the jointing stage. All three water indicators were significantly correlated with soil moisture during the reviving stage, and the correlations were stronger for leaf water indicators than that of the canopy water indicator at the jointing stage. No correlation was observed after the heading stage. All three water indicators showed good capabilities of revealing grain yield variability in jointing stage, with R2 up to 0.89. CWC had a consistent relationship with grain yield over different growing seasons, but the performances of EWT and LWC were growing-season specific. The partial least squares regression was the most accurate method for estimating LWC (R2=0.72; RMSE=3.6%) and comparable capability for EWT and CWC. Finally, the work highlights the usefulness of crop water indicators to assess crop growth, productivity, and soil water status and demonstrates the potential of various spectral processing methods for retrieving crop water contents from canopy reflectance spectrums.