Sustainable Waste Sorter

Indiana University Purdue University IndianapolisThe purpose of this project is to help people eliminate the confusion on whether they should throw their trash away or dispose of it in a recycling bin. The sustainable waste sorter is an informative device that tells the user where to place their trash. Our customer and the origin of the idea came from an organization called Roche Diagnostics Operations. Roche Diagnostics Operations is a multinational healthcare organization, the Indianapolis location focuses more on creating and developing their diabetic test strips. The device is created of four main components which include a Raspberry Pi 2 Model B, a camera module, an LCD screen, and a casing/mount that holds all of these components together. All of these components are compatible with the Raspberry Pi 2 Model B. The software was programmed in Python and the database in MySQL. During the development of the device, the most challenging task was learning how to develop in the new language, Python. Once the device reached a stable state it was piloted at Roche Diagnostics Operations. The purpose of the first of three pilot sessions was to verify that the device worked in the environment and that the items entered in the database were recognized; as a result, the device passed that test. The second pilot session had the same purpose as the first pilot session but with more items in the database. The device received more interaction during the second pilot session, though the team decided to schedule a third pilot session once all the items were entered into the database and a revamped user interface was completed. The team entered about 800 entries into the database and created a new and interactive user interface for the device. The third pilot session was a success; the items that were scanned by testers were recognized and the new user interface was a success as well. Overall, the sustainable waste sorter project was successful and educational. We, as students, took all of our fundamental learnings from our previous courses and applied them to this project. This allowed us to enhance our problem solving and project management skills. As people use the device, we hope that it educates them on how to properly recycle therefore improving the environmental state of our planet.Computer Engineering Technolog

Can Courts be Bulwarks of Democracy?

Independent judges are thought to promote democratic regime survival by allowing perceived violations of rules limiting arbitrary power to be challenged non-violently in a fair setting, governed by transparent rules. Yet, judges are often subjected to public shaming and politically motivated removals. Courts are sometimes packed with partisan allies of the government, their jurisdiction is nearly always subject to political control and their decisions can be ignored. For all of these reasons, scholars have identied patterns of prudential decision-making that is sensitive to political interests even on the most well-respected courts in the world. If these forces all operate on judges, what, if any, are the conditions under which judges can be conceived of as defenders of democracy? How could judges subject to political pressures stabilize a democratic regime? This document summarizes a book that addresses these questions. We argue that despite these pressures judges can enhance regime stability by incentivizing prudence on behalf of elites, both those who control that state, i.e., leaders, and those on whose support leaders depend. Empirically, we leverage original data on judicial behavior, judicial institutions, and policy using a sample of all democratic political systems for over 100 years. We re-examine empirical claims of existing models of courts and democracy as well as original claims derived from our own work

Update, A Global Measure of Judicial Independence, 1900-2015

Update of Latent Judicial Independence scores described in Linzer and Staton (2015). Newer versions of Cingranelli-Richards (CIRI), Polity IV’s executive constraints (XCONST), PRS Group, Feld-Voigt, and Global Competitiveness Report (GCR) are included. Two measures of de facto judicial independence were included from the Varieties of Democracy Project (V-Dem), high court independence (v2juhcind) and high court compliance (v2juhccomp). Suggested citation. We suggest that you cite both the original paper describing the model as well as the dataset itself. See citation information

Management strategies for early- and late-planted soybean in the north-central United States

It is widely recognized that planting soybean [Glycine max (L.) Merr.] early is critical to maximizing yield, but the influence of changing management factors when soybean planting is delayed is not well understood. The objectives of this research were to (a) identify management decisions that increase seed yield in either early- or late-planted soybean scenarios, and (b) estimate the maximum break-even price of each management factor identified to influence soybean seed yield in early- or late-planted soybean. Producer data on seed yield and management decisions were collected from 5682 fields planted with soybean during 2014−2016 and grouped into 10 technology extrapolation domains (TEDs) based on growing environment. A subsample of 1512 fields was classified into early- and late-planted categories using terciles. Conditional inference trees were created for each TED to evaluate the effect of management decisions within the two planting date timeframes on seed yield. Management strategies that maximized yield and associated maximum break-even prices varied across TEDs and planting date. For early-planted fields, higher yields were associated with artificial drainage, insecticide seed treatment, and lower seeding rates. For late-planted fields, herbicide application timing and tillage intensity were related to higher yields. There was no individual management decision that consistently increased seed yield across all TEDs

Sifting and winnowing: Analysis of farmer field data for soybean in the US North-Central region

Field trials are commonly used to estimate the effects of different factors on crop yields. In the present study, we followed an alternative approach to identify factors that explain field-to-field yield variation, which consisted of farmer survey data, a spatial framework, and multiple statistical procedures. This approach was used to identify management factors with strongest association with on-farm soybean yield variation in the US North Central (NC) region. Field survey data, including yield and management information, were collected over two crop growing seasons (2014 and 2015) from rainfed and irrigated soybean fields (total of 3568 field-year observations). Fields were grouped into technology extrapolation domains (TEDs) that accounted for soil and climate variation and 9 TEDs were selected based on the number of fields needed to detect yield differences due to management as determined using power analysis. Average yield ranged from 2.5 to 5 Mg ha−1 across TEDs, with field yield distributions in half of the domains having a distributional peak that was close to maximum yields. Conditional inference trees analysis was chosen among 26 statistical procedures as the approach that best combines ability to detect and rank factors (and their interactions) with greatest influence on on-farm yield and relatively easy interpretation of results. Survey data from ca. 150 fields in each of the nine TEDs allowed us to identify key management factors influencing yields for an agricultural area that includes ca. 7 million ha sown with soybean. In five of the nine TEDs, highest yields were observed in early-sown fields. Other factors explaining on-farm yield variation were maturity group, and in-season foliar fungicide and/or insecticide application, but, in some cases, their influence on yield depended upon sowing date and water regime. While the approach proposed here cannot establish cause-effect relationships conclusively, it can certainly provide a focus to replicated field experiments in relation to which management factors to investigate. We believe that future agronomic studies based on farmer survey data can greatly benefit from ex-ante identification of most important TEDs (relative to crop area and production) as well as determination of minimum number of farmer survey data that needs to be collected from each of them based on expected yield differences and variability. The approach is generic enough to be applied in other crop producing regions as long as farmer data and associated climate and soil databases are available.This article is published as Mourtzinis, Spyridon, Juan I. Rattalino Edreira, Patricio Grassini, Adam C. Roth, Shaun N. Casteel, Ignacio A. Ciampitti, Hans J. Kandel et al. "Sifting and winnowing: Analysis of farmer field data for soybean in the US North-Central region." Field crops research 221 (2018): 130-141. doi: 10.1016/j.fcr.2018.02.024.</p

Assessing causes of yield gaps in agricultural areas with diversity in climate and soils

Identification of causes of gaps between yield potential and producer yields has been restricted to small geographic areas. In the present study, we developed a novel approach for identifying causes of yield gaps over large agricultural areas with diversity in climate and soils. This approach was applied to quantify and explain yield gaps in rainfed and irrigated soybean in the North-Central USA (NC USA) region, which accounts for about one third of soybean global production. Survey data on yield and management were collected from 3568 producer fields over two crop seasons and grouped into 10 technology extrapolation domains (TEDs) according to their soil, climate, and water regime. Yield potential was estimated using a combination of crop modeling and boundary functions for water productivity and compared against highest producer yields derived from the yield distribution in each TED-year. Yield gaps were calculated as the difference between yield potential and average producer yield. Explanatory factors for yield gaps were investigated by identifying management practices that were concordantly associated with high- and low-yield fields. Management × TED interactions were then evaluated to elucidate the underlying causes of yield gaps. The chosen spatial TED framework accounted for about half of the regional variation in producer yield within the NC USA region. Across the 10 TEDs, soybean average yield potential ranged from 3.3 to 5.3 Mg ha−1 for rainfed fields and from 5.3 to 5.6 Mg ha−1for irrigated fields. Highest producer yields in each TED were similar (±12%) to the estimated yield potential. Yield gap, calculated as percentage of yield potential, was larger in rainfed (range: 15–28%) than in irrigated (range: 11–16%) soybean. Upscaled to the NC USA region, yield potential was 4.8 Mg ha−1 (rainfed) and 5.7 Mg ha−1 (irrigated), with a respective yield gap of 22 and 13% of yield potential. Sowing date, tillage, and in-season foliar fungicide and/or insecticide were identified as explanatory causes for yield variation in half or more of the 10 TEDs. However, the degree to which these three factors influenced producer yield varied across TEDs. Analysis of in-season weather helped interpret management × TED interactions. For example, yield increase due to advances in sowing date was greater in TEDs with less water limitation during the pod-setting phase. The present study highlights the strength of combining producer survey data with a spatial framework to measure yield gaps, identify management factors explaining these gaps, and understand the biophysical drivers influencing yield responses to crop management.This article is published as Edreira, Juan I. Rattalino, Spyridon Mourtzinis, Shawn P. Conley, Adam C. Roth, Ignacio A. Ciampitti, Mark A. Licht, Hans Kandel et al. "Assessing causes of yield gaps in agricultural areas with diversity in climate and soils." Agricultural and Forest Meteorology 247 (2017): 170-180. doi: 10.1016/j.agrformet.2017.07.010.</p