Optimizing machine learning for agricultural productivity: A novel approach with RScv and remote sensing data over Europe

CONTEXT: Accurate estimating of crop yield is crucial for developing effective global food security strategies which can lead to reduce of hunger and more sustainable development. However, predicting crop yields is a complex task as it requires frequent monitoring of many weather and socio-economic factors over an extended period. Satellite remote sensing products have become a reliable source for climate-based variables. They are easier to obtain and provide detailed spatial and temporal coverage. OBJECTIVE: The aim of this study is to assess the effectiveness of implement a novel optimization algorithm, called Randomized Search cross validation (RScv), on various machine learning algorithms and measure the prediction accuracy enhancement. METHODS: Annual yields of four crops (Barley, Oats, Rye, and Wheat) were predicted across 20 European countries for 20 years (2000–2019). Two NASA missions, namely GPCP and GLDAS satellites, provided us with climate- and soil-based input variables. Those variables were employed as the input of four ensemble Machine Learning (ML) algorithms (Ada-Boost (AB), Gradient Boost (GB), Random Forest (RF) and Extra Tree (ET)) which are faster and more adoptable compare to classic AI algorithms. RESULTS AND CONCLUSIONS: Main results show that applying RScv improves the prediction ability of all ML models over the four crops. In particular, the RScv-AB reaches the overall highest accuracy for predicting yields (R2max = 0.9). Spatial evaluation of predicting errors depicts that the proposed models were more shifted toward underestimation. An uncertainty analysis was also carried out which shows that applying ML algorithms creates higher and lowers uncertainty in Barley and Wheat respectively. SIGNIFICANCE: Considering the robustness of the optimised ML models and the global coverage of remote sensing data, our current methodology demonstrates great transferability and can be applied in other regions across the globe with higher temporal extents. In addition, this tool could be beneficial to decision makers in various sectors to improve the water allocations, deal with climate change effects and keep sustainable agricultural development.Antonio Jodar-Abellan acknowledges financial support received form the Margarita Salas Postdoc Spanish Program

Exploring the independent association of employment status to cancer survivors’ health-related quality of life

Background: Having a job has been associated with better Health-Related Quality of Life (HRQOL) in cancer survivors. However, the sociodemographic and disease-related profiles characterizing the survivors being employed and those having better HRQOL largely overlap. The present study aims to discern the degree to which employment status is independently associated with cancer survivors' HRQOL or if it mainly reflects the impact of other sociodemographic and cancer-related variables. Methods: Cross-sectional study on a heterogeneous sample of 772 working-age survivors of adult-onset cancer. An instrument specifically designed to assess HRQOL in cancer survivors and Multivariate Variance Analysis (MANOVA) were used. Results: Survival phase, cancer type, and employment status showed the main effects on cancer survivors' HRQOL. In particular, being employed (vs unemployed) had the greatest positive association with HRQOL, affecting ten of the twelve HRQOL domains considered. Also, interaction effects highlighted the role of age (younger) and marital status (single) as risk factors for a greater negative impact of variables affecting the survivor's HRQOL. Conclusions: The application of a multivariate methodology sheds new light on two relevant issues for the cancer survivor's HRQOL: (i) the existence of differences between diagnostic groups that are not attributed to other variables such as sex, and (ii) the important and independent role that employment status plays. Comprehensive cancer survivorship care should focus more on high-risk groups and include having a job as an essential aspect to consider and prompt. The fact that the employment status is susceptible to change represents a valuable opportunity to care for the wellbeing of this population

Potential of VIS-NIR hyperspectral imaging and chemometric methods to identify similar cultivars of nectarine

[EN] Product inspection is essential to ensure good quality and to avoid fraud. New nectarine cultivars with similar external appearance but different physicochemical properties may be mixed in the market, causing confusion and rejection among consumers, and consequently affecting sales and prices. Hyperspectral reflectance imaging in the range of 450¿1040 nm was studied as a non-destructive method to differentiate two cultivars of nectarines with a very similar appearance but different taste. Partial least squares discriminant analysis (PLS-DA) was used to develop a prediction model to distinguish intact fruits of the cultivars using pixel-wise and mean spectrum approaches, and then the model was projected onto the complete surface of fruits allowing visual inspection. The results indicated that mean spectrum of the fruit was the most accurate method, a correct discrimination rate of 94% being achieved. Wavelength selection reduced the dimensionality of the hyperspectral images using the regression coefficients of the PLS-DA model. An accuracy of 96% was obtained by using 14 optimal wavelengths, whereas colour imaging and a trained inspection panel achieved a rate of correct classification of only 57% of the fruits.This work was partially funded by INIA and FEDER funds through project RTA2015-00078-00-00. Sandra Munera thanks INIA for the FPI-INIA grant num. 43 (CPR2014-0082), partially supported by European Union FSE funds. The authors wish to thank Fruits de Ponent (Lleida) for providing the fruit.Munera-Picazo, S.; Amigo, JM.; Aleixos Borrás, MN.; Talens Oliag, P.; Cubero-García, S.; Blasco Ivars, J. (2018). Potential of VIS-NIR hyperspectral imaging and chemometric methods to identify similar cultivars of nectarine. Food Control. 86:1-10. https://doi.org/10.1016/j.foodcont.2017.10.037S1108

Application of Drought Management Guidelines in Spain

The Spanish case study presents the drought planning process carried in the Tagus Basin. The presentation is structured in four parts: organizational, methodological, operational and public review components. The organizational component presents the framework and specific legislations and the organizations and institutions in Spain that work on drought preparedness and mitigation. The methodological component presents the analytical techniques used for drought risk analysis and management. The operational component describes the proposed structure for the drought management plan and presents the specific actions that are contemplated in it. The process review component identifies stakeholders that are involved in the decision making process and presents their views on the process

Non-destructive assessment of the internal quality of intact persimmon using colour and VIS/NIR hyperspectral imaging

The internal quality of intact persimmon cv. Rojo Brillante was assessed trough visible and near infrared hyperspectral imaging. Fruits at three stages of commercial maturity were exposed to different treatments with CO2 to obtain fruit with different ripeness and level of astringency (soluble tannin content). Spectral and spatial information were used for building classification models to predict ripeness and astringency trough multivariate analysis techniques like linear and quadratic discriminant analysis (LDA and QDA) and support vector machine (SVM). Additionally, flesh firmness was predicted by partial least square regression (PLSR). The full spectrum was used to determine the internal properties and later principal component analysis (PCA) was used to select optimal wavelengths (580, 680 and 1050 nm). The correct classification was above 92% for the three classifiers in the case of ripeness and 95% for QDA in the case of astringency. A value of R2 = 0.80 and a ratio of prediction deviation (RPD) of 1.86 were obtained with the selected wavelengths for the prediction of firmness which demonstrated the potential of hyperspectral imaging as a non-destructive tool in the assessment of the firmness, ripeness state and astringency level of Rojo Brillante persimmon.This work has been partially funded by the INIA and FEDER through projects RTA2012-00062-C04-01, RTA2012-00062-C04-03 and RTA2013-00043-C02, GVA through the project AICO/2015/122, the International S&T Cooperation Programs of China (2015DFA71150), and the International S&T Cooperation Program of Guangdong Province, China (2013B051000010). Sandra Munera thanks INIA for the grant FPI-INIA #43 (CPR2014-0082) partially supported by FSE funds.Munera-Picazo, S.; Besada Ferreiro, CM.; Aleixos Borrás, MN.; Talens Oliag, P.; Salvador, A.; Sun, D.; Cubero-García, S.... (2017). Non-destructive assessment of the internal quality of intact persimmon using colour and VIS/NIR hyperspectral imaging. Food Science and Technology. 77:241-248. https://doi.org/10.1016/j.lwt.2016.11.063S2412487

Is eco-efficiency in greenhouse gas emissions converging among European Union countries?

Eco-efficiency refers to the ability to produce more goods and services with less impact on the environment and less consumption of natural resources. This issue has become a matter of concern that is receiving increasing attention from politicians, scientists and researchers. Furthermore, greenhouse gases emitted as a result of production processes have a marked impact on the environment and are also the foremost culprit of global warming and climate change. This paper assesses convergence in eco-efficiency in greenhouse gas emissions in the European Union. Eco-efficiency is assessed at both country and greenhouse-gas-specific levels using Data Envelopment Analysis techniques and directional distance functions, as recently proposed by Picazo-Tadeo et al. (Eur J Oper Res, 220:798–809, 2012). Convergence is then evaluated using the Phillips and Sul (Econometrica, 75:1771–1855, 2007) approach that allows testing for the existence of convergence groups. Although the results point to the existence of different convergence clubs depending on the specific pollutant considered, they signal the existence of at least four clear groups of countries. The first two groups are core European Union high-income countries (Benelux, Germany, Italy, Austria, the United Kingdom and Scandinavian countries). A third club is made up of peripheral countries (Spain, Ireland, Portugal and Greece) together with some Eastern countries (Latvia and Slovenia), while the remaining clubs consist of groups containing Eastern European countries

Full Information Product Pricing: An Information Strategy for Harnessing Consumer Choice to Create a More Sustainable World

Research and practice in the information systems (IS) field have been evolving over time, nourishing and promoting the development of applications that transform the relationships of individuals, corporations, and governments. Building on this evolution, we push forward a vision of the potential influence of the IS field into one of the most important problems of our times, an increasingly unsustainable world, which is traditionally considered the product of imperfect markets or market externalities. We describe our work in Full Information Product Pricing (FIPP) and our vision of a FIPP global socio-technical system, I-Choose, as a way to connect consumer choice and values with environmental, social, and economic effects of production and distribution practices. FIPP and I-Choose represent a vision about how information systems research can contribute to interdisciplinary research in supply chains, governance, and market economies to provide consumers with information packages that help them better understand how, where, and by whom the products they buy are produced. We believe that such a system will have important implications for international trade and agreements, for public policy, and for making a more sustainable world

Stratification strength and light climate explain variation in chlorophyll a at the continental scale in a European multilake survey in a heatwave summer.

To determine the drivers of phytoplankton biomass, we collected standardized morphometric, physical, and biological data in 230 lakes across the Mediterranean, Continental, and Boreal climatic zones of the European continent. Multilinear regression models tested on this snapshot of mostly eutrophic lakes (median total phosphorus [TP] = 0.06 and total nitrogen [TN] = 0.7 mg L 1), and its subsets (2 depth types and 3 climatic zones), show that light climate and stratification strength were the most significant explanatory variables for chlorophyll a (Chl a) variance. TN was a significant predictor for phytoplankton biomass for shallow and continental lakes, while TP never appeared as an explanatory variable, suggesting that under high TP, light, which partially controls stratification strength, becomes limiting for phytoplankton development. Mediterranean lakes were the warmest yet most weakly stratified and had significantly less Chl a than Boreal lakes, where the temperature anomaly from the long-term average, during a summer heatwave was the highest (+4 C) and showed a significant, exponential relationship with stratification strength. This European survey represents a summer snapshot of phytoplankton biomass and its drivers, and lends support that light and stratification metrics, which are both affected by climate change, are better predictors for phytoplankton biomass in nutrient-rich lakes than nutrient concentrations and surface temperature

Mitogen-Activated Protein Kinase Phosphatase-1 Is Overexpressed in Non-Small Cell Lung Cancer and Is an Independent Predictor of Outcome in Patients

An increase in the activity of the mitogen-activated protein kinases (MAPKs) has been correlated with a more malignant phenotype in several tumor models in vitro and in vivo. A key regulatory mechanism of the MAPKs [extracellular signal-regulated kinase (ERK); c-jun NH(2)-terminal kinase (JNK); and p38] is the dual specificity phosphatase CL100, also called MAPK phosphatase-1 (MKP-1). This study was designed to examine the involvement of CL100/MKP-1 and stress-related MAPKs in lung cancer. EXPERIMENTAL DESIGN: We assessed the expression of CL100/MKP-1 and the activation of the MAPKs in a panel of 18 human cell lines [1 primary normal bronchial epithelium, 8 non-small cell lung cancer (NSCLC), 7 small cell lung cancer (SCLC), and 2 carcinoids] and in 108 NSCLC surgical specimens. RESULTS: In the cell lines, CL100/MKP-1 expression was substantially higher in NSCLC than in SCLC. P-ERK, P-JNK, and P-p38 were activated in SCLC and NSCLC, but the degree of their activation was variable. Immunohistochemistry in NSCLC resection specimens showed high levels of CL100/MKP-1 and activation of the three MAPK compared with normal lung. In univariate analysis, no relationship was found among CL100/MKP-1 expression and P-ERK, P-JNK, or P-p38. Interestingly, high CL100/MKP-1 expression levels independently predicted improved survival in multivariate analysis. JNK activation associated with T(1-2) and early stage, whereas ERK activation correlated with late stages and higher T and N. Neither JNK nor ERK activation were independent prognostic factors when studied for patient survival. CONCLUSIONS: Our data indicate the relevance of MAPKs and CL100/MKP-1 in lung cancer and point at CL100/MKP-1 as a potential positive prognostic factor in NSCLC. Finally, our study supports the search of new molecular targets for lung cancer therapy within the MAPK signaling pathway