Application of machine learning techniques to simulate the evaporative fraction and its relationship with environmental variables in corn crops

Abstract Background The evaporative fraction (EF) represents an important biophysical parameter reflecting the distribution of surface available energy. In this study, we investigated the daily and seasonal patterns of EF in a multi-year corn cultivation located in southern Italy and evaluated the performance of five machine learning (ML) classes of algorithms: the linear regression (LR), regression tree (RT), support vector machine (SVM), ensembles of tree (ETs) and Gaussian process regression (GPR) to predict the EF at daily time step. The adopted methodology consisted of three main steps that include: (i) selection of the EF predictors; (ii) comparison of the different classes of ML; (iii) application, cross-validation of the selected ML algorithms and comparison with the observed data. Results Our results indicate that SVM and GPR were the best classes of ML at predicting the EF, with a total of four different algorithms: cubic SVM, medium Gaussian SVM, the Matern 5/2 GPR, and the rational quadratic GPR. The comparison between observed and predicted EF in all four algorithms, during the training phase, were within the 95% confidence interval: the R2 value between observed and predicted EF was 0.76 (RMSE 0.05) for the medium Gaussian SVM, 0.99 (RMSE 0.01) for the rational quadratic GPR, 0.94 (RMSE 0.02) for the Matern 5/2 GPR, and 0.83 (RMSE 0.05) for the cubic SVM algorithms. Similar results were obtained during the testing phase. The results of the cross-validation analysis indicate that the R2 values obtained between all iterations for each of the four adopted ML algorithms were basically constant, confirming the ability of ML as a tool to predict EF. Conclusion ML algorithms represent a valid alternative able to predict the EF especially when remote sensing data are not available, or the sky conditions are not suitable. The application to different geographical areas, or crops, requires further development of the model based on different data sources of soils, climate, and cropping systems

Adenoma of the Ceruminous Gland (Ceruminoma)

Tumor arising from the ceruminous glands of the external ear canal (EAC) are very rare and can present a diagnostic dilemma because of their varied clinical and histologic manifestations. To our knowledge, this is the first report to present a case of ceruminoma in pediatric age. We discuss the origin of these tumors and the importance of wide excision and of the immunohistochemistry for the diagnosis

Effects of the fertilizer added with dmpp on soil nitrous oxide emissions and microbial functional diversity

Agricultural sites contribute extensively to atmospheric emissions of climate-altering gases such as nitrous oxide. Several strategies have been considered to mitigate the impact of agriculture on climate, among these the utilization of fertilizers added with nitrification inhibitors such as DMPP (3,4-dimethylpyrazole phosphate) may represent a suitable solution. DMPP inhibits the growth and activity of ammonia-oxidizing microorganisms, particularly the ammonia-oxidizing bacteria, which are involved in N2O production. At present, little information is available on the effects of DMPP on the catabolic diversity of soil microbial community. In this study, the N2O emission by soil was performed by using the static chamber technique. The biological determinations of the microbial biomass carbon and the catabolic profile were assessed by measuring the substrate-induced respiration during the entire growing season of a potato crop under two nitrogen treatments: fertilization with and without DMPP. Our results did not show a clear mitigation of N2O emission by DMPP, even if a tendency to lower N2O fluxes in DMPP plots occurred when soil temperatures were lower than 20◦C. Conversely, DMPP deeply affected the microbial biomass and the catabolism of soil microorganisms, exerting a negative effect when it accumulated in excessive doses in the soil, limiting the growth and the capacity of soil microorganism communities to use different substrates

Paraneoplastic pemphigus: insight into the autoimmune pathogenesis, clinical features and therapy

Paraneoplastic pemphigus is a rare autoimmune skin disease that is always associated with a neoplasm. Usually, oral, skin, and mucosal lesions are the earliest manifestations shown by paraneoplastic pemphigus patients. The pathogenesis of paraneoplastic pemphigus is not yet completely understood, although some immunological aspects have been recently clarified. Because of its rarity, several diagnostic criteria have been proposed. Besides, several diagnostic procedures have been used for the diagnosis, including indirect immunofluorescence, direct immunofluorescence, and ELISA. We reviewed the most recent literature, searching on PubMed "paraneoplastic pemphigus". We included also papers in French, German, and Spanish. We found 613 papers for "paraneoplastic pemphigus". Among them, 169 were review papers. Because of its varying clinical features, paraneoplastic pemphigus still represents a challenge for clinicians. Furthermore, diagnosis and management of paraneoplastic pemphigus requires close collaboration between physicians, including dermatologist, oncologist, and otorhinolaryngologist.Paraneoplastic pemphigus is a rare autoimmune skin disease that is always associated with a neoplasm. Usually, oral, skin, and mucosal lesions are the earliest manifestations shown by paraneoplastic pemphigus patients. The pathogenesis of paraneoplastic pemphigus is not yet completely understood, although some immunological aspects have been recently clarified. Because of its rarity, several diagnostic criteria have been proposed. Besides, several diagnostic procedures have been used for the diagnosis, including indirect immunofluorescence, direct immunofluorescence, and ELISA. We reviewed the most recent literature, searching on PubMed “paraneoplastic pemphigus”. We included also papers in French, German, and Spanish. We found 613 papers for “paraneoplastic pemphigus”. Among them, 169 were review papers. Because of its varying clinical features, paraneoplastic pemphigus still represents a challenge for clinicians. Furthermore, diagnosis and management of paraneoplastic pemphigus requires close collaboration between physicians, including dermatologist, oncologist, and otorhinolaryngologist

Evaluation of the Uncertainty in Satellite-Based Crop State Variable Retrievals Due to Site and Growth Stage Specific Factors and Their Potential in Coupling with Crop Growth Models

Coupling crop growth models and remote sensing provides the potential to improve our understanding of the genotype x environment x management (G X E X M) variability of crop growth on a global scale. Unfortunately, the uncertainty in the relationship between the satellite measurements and the crop state variables across different sites and growth stages makes it diffcult to perform the coupling. In this study, we evaluate the effects of this uncertainty with MODIS data at the Mead, Nebraska Ameriflux sites (US-Ne1, US-Ne2, and US-Ne3) and accurate, collocated Hybrid-Maize (HM) simulations of leaf area index (LAI) and canopy light use effciency (LUECanopy). The simulations are used to both explore the sensitivity of the satellite-estimated genotype X management (G X M) parameters to the satellite retrieval regression coeffcients and to quantify the amount of uncertainty attributable to site and growth stage specific factors. Additional ground-truth datasets of LAI and LUECanopy are used to validate the analysis. The results show that uncertainty in the LAI/satellite measurement regression coeffcients lead to large uncertainty in the G X Mparameters retrievable from satellites. In addition to traditional leave-one-site-out regression analysis, the regression coeffcient uncertainty is assessed by evaluating the retrieval performance of the temporal change in LAI and LUECanopy. The weekly change in LAI is shown to be retrievable with a correlation coeffcient absolute value (|r|) of 0.70 and root-mean square error (RMSE) value of 0.4, which is significantly better than the performance expected if the uncertainty was caused by random error rather than secondary effects caused by site and growth stage specific factors (an expected |r| value of 0.36 and RMSE value of 1.46 assuming random error). As a result, this study highlights the importance of accounting for site and growth stage specific factors in remote sensing retrievals for future work developing methods coupling remote sensing with crop growth models

How Universal is the Relationship Between Remotely Sensed Vegetation Indices and Crop Leaf Area Index? A Global Assessment

Leaf Area Index (LAI) is a key variable that bridges remote sensing observations to the quantification of agroecosystem processes. In this study, we assessed the universality of the relationships between crop LAI and remotely sensed Vegetation Indices (VIs). We first compiled a global dataset of 1459 in situ quality-controlled crop LAI measurements and collected Landsat satellite images to derive five different VIs including Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI), two versions of the Enhanced Vegetation Index (EVI and EVI2), and Green Chlorophyll Index (CI(sub Green)). Based on this dataset, we developed global LAI-VI relationships for each crop type and VI using symbolic regression and Theil-Sen (TS) robust estimator. Results suggest that the global LAI-VI relationships are statistically significant, crop-specific, and mostly non-linear. These relationships explain more than half of the total variance in ground LAI observations (R2 greater than 0.5), and provide LAI estimates with RMSE below 1.2 m2/m2. Among the five VIs, EVI/EVI2 are the most effective, and the crop-specific LAI-EVI and LAI-EVI2 relationships constructed by TS, are robust when tested by three independent validation datasets of varied spatial scales. While the heterogeneity of agricultural landscapes leads to a diverse set of local LAI-VI relationships, the relationships provided here represent global universality on an average basis, allowing the generation of large-scale spatial-explicit LAI maps. This study contributes to the operationalization of large-area crop modeling and, by extension, has relevance to both fundamental and applied agroecosystem research

Stomatal response to decreased relative humidity constrains the acceleration of terrestrial evapotranspiration

Terrestrial evapotranspiration (ET) is thermodynamically expected to increase with increasing atmospheric temperature; however, the actual constraints on the intensification of ET remain uncertain due to a lack of direct observations. Based on the FLUXNET2015 Dataset, we found that relative humidity (RH) is a more important driver of ET than temperature. While actual ET decrease at reduced RH, potential ET increases, consistently with the complementary relationship (CR) framework stating that the fraction of energy not used for actual ET is dissipated as increased sensible heat flux that in turn increases potential ET. In this study, we proposed an improved CR formulation requiring no parameter calibration and assessed its reliability in estimating ET both at site-level with the FLUXNET2015 Dataset and at basin-level. Using the ERA-Interim meteorological dataset for 1979-2017 to calculate ET, we found that the global terrestrial ET showed an increasing trend until 1998, while the trend started to decline afterwards. Such decline was largely associated with a reduced RH, inducing water stress conditions that triggered stomatal closure to conserve water. For the first time, this study quantified the global-scale implications of changes in RH on terrestrial ET, indicating that the temperature-driven acceleration of the terrestrial water cycle will be likely constrained by terrestrial vegetation feedbacks.Peer reviewe

Weakened growth of cropland‐N2O emissions in China associated with nationwide policy interventions

This study was supported by the National Natural Science Foundation of China (41671464; 7181101181), the National Key Research and Development Program of China (2016YFD0800501; 2018YFC0213304), 111 Project (B14001), the GCP-INI Global N2O Budget and the INMS Asia Demo Activities. The input of P.S. contributes to the UK-China Virtual Joint Centre on Nitrogen ìN-Circleî funded by the Newton Fund via UK BBSRC/NERC (BB/N013484/1). We acknowledged Eric Ceschia, Kristiina Regina, Dario Papale, and the NANORP for sharing a part of observation data.Peer reviewedPostprin

Ecosystem transpiration and evaporation : Insights from three water flux partitioning methods across FLUXNET sites

We apply and compare three widely applicable methods for estimating ecosystem transpiration (T) from eddy covariance (EC) data across 251 FLUXNET sites globally. All three methods are based on the coupled water and carbon relationship, but they differ in assumptions and parameterizations. Intercomparison of the three dailyTestimates shows high correlation among methods (Rbetween .89 and .94), but a spread in magnitudes ofT/ET (evapotranspiration) from 45% to 77%. When compared at six sites with concurrent EC and sap flow measurements, all three EC-basedTestimates show higher correlation to sap flow-basedTthan EC-based ET. The partitioning methods show expected tendencies ofT/ET increasing with dryness (vapor pressure deficit and days since rain) and with leaf area index (LAI). Analysis of 140 sites with high-quality estimates for at least two continuous years shows thatT/ET variability was 1.6 times higher across sites than across years. Spatial variability ofT/ET was primarily driven by vegetation and soil characteristics (e.g., crop or grass designation, minimum annual LAI, soil coarse fragment volume) rather than climatic variables such as mean/standard deviation of temperature or precipitation. Overall,TandT/ET patterns are plausible and qualitatively consistent among the different water flux partitioning methods implying a significant advance made for estimating and understandingTglobally, while the magnitudes remain uncertain. Our results represent the first extensive EC data-based estimates of ecosystemTpermitting a data-driven perspective on the role of plants' water use for global water and carbon cycling in a changing climate.Peer reviewe

Ecosystem transpiration and evaporation: Insights from three water flux partitioning methods across FLUXNET sites

We apply and compare three widely applicable methods for estimating ecosystem transpiration (T) from eddy covariance (EC) data across 251 FLUXNET sites globally. All three methods are based on the coupled water and carbon relationship, but they differ in assumptions and parameterizations. Intercomparison of the three daily T estimates shows high correlation among methods (R between .89 and .94), but a spread in magnitudes of T/ET (evapotranspiration) from 45% to 77%. When compared at six sites with concurrent EC and sap flow measurements, all three EC‐based T estimates show higher correlation to sap flow‐based T than EC‐based ET. The partitioning methods show expected tendencies of T/ET increasing with dryness (vapor pressure deficit and days since rain) and with leaf area index (LAI). Analysis of 140 sites with high‐quality estimates for at least two continuous years shows that T/ET variability was 1.6 times higher across sites than across years. Spatial variability of T/ET was primarily driven by vegetation and soil characteristics (e.g., crop or grass designation, minimum annual LAI, soil coarse fragment volume) rather than climatic variables such as mean/standard deviation of temperature or precipitation. Overall, T and T/ET patterns are plausible and qualitatively consistent among the different water flux partitioning methods implying a significant advance made for estimating and understanding T globally, while the magnitudes remain uncertain. Our results represent the first extensive EC data‐based estimates of ecosystem T permitting a data‐driven perspective on the role of plants’ water use for global water and carbon cycling in a changing climate