Academic literature on the topic 'Commercial loans Data processing'

This study aims to find out the factors that affect non-performing loans of productive loans of commercial banks registeredin oritas Jasa Keuangan. This study examined the macroeconomic variables that affect productive credit NPLs. The sample used is a commercial bank in Indonesia, which is registered in OJK. Productive credit NPL data are taken quarterly from 2003 to 2017,and 56 observationsare obtained. The regression model used in this study is ARDL, with the help of processing Eviews 10 software. The macroeconomic variables tested in this research consist of GDP growth, loan interest rates growth, and IDR/ USD exchangerate growth. GDP growth and exchange rate growth has a negative relationship, while loan interest rates growth does not have a significant relationship with the dependent variable.

With the rapid development of entrepreneurship loans in China, the construction of a credit evaluation system of risk loans has become an important financial safeguard measure. This paper mainly studies the following three aspects. Firstly, in view of the subjective factors in the approval process of venture loans, based on the credit evaluation system of commercial banks and the data characteristics of venture loans, a credit evaluation system based on venture loans is constructed. Secondly, the randomized uniform design method is used to improve the population initialization method to realize the uniform distribution of the individual population. Finally, aiming at the problem of low efficiency of venture loan audit, this paper proposes an optimized BP neural network to evaluate the venture loan. Especially, through data processing, a credit index system is constructed, and then the optimized BP neural network model is determined in parameters. The model contains 15 input nodes, 1 hidden layer, and 2 output layers. Finally, the simulation shows that the optimized BP neural network model has obvious advantages in the loan evaluation. This paper includes the development status of credit evaluation of venture loans is empirically studied by using an optimized BP neural network model of nonexpected output.

This study aims to determine the effect of credit expansion, operational efficiency, lending interest rate, NPL in the previous period and Capital Adequacy Ratio (CAR) on Non-Performing Loans (NPL). This type of research was quantitative and used secondary data from annual reports. This study used a sample of 20 conventional commercial banks registered on the IDX in 2017-2019 with sample determination using the purposive sampling method. The technique for analyzing data in this study used the Generalized Method of Moment (GMM) with data processing using the help of the Eviews 9 application. Based on the Generalized Method of Moment (GMM) analysis, it was obtained that credit expansion as measured by loan to deposit ratio and lending interest rate did not significantly affect non-performing loans. The level of operational efficiency as measured by Operating Expenses on Operating Income (BOPO), NPL of the previous period, and Capital Adequacy Ratio (CAR) significantly affected non-performing loans.

Competition among banks is stiff in this field (mortgage loans), where everyone wants to offer clients more favourable terms and conditions, thus gaining the largest market share. However, the credit terms and conditions can vary considerably, given the purpose of credit and the collateral. The world uses different assessment methods for a borrower’s evaluation. Particular attention is paid to the borrower's credibility and additional creditworthiness by collecting, processing and evaluating important information on the credit applicant. This research work is based on theoretical knowledge in economics, legal documents and statistical data analyses to investigate mortgage lending by Latvian commercial banks, the potential risks, the solvency assessment models, including the scoring system (the system’s role in mortgage lending practised by Latvian commercial banks), and to develop proposals for the system improvement. In the paper, the authors give insight into the creditworthiness analysis model – Credit Scoring –, the Latvian mortgage credit market, different loan terms and conditions provided by Latvian commercial banks and lending development impacts and describe the essence of the scoring system and the way how Latvian commercial banks use the system. The new provisions of 2016 in the Consumer Rights Protection Law regarding requesting complete information allow considerably enhancing the loan scoring system of commercial banks in compliance with Directive 2014/17/EU of the European Parliament and of the Council of 4 February on credit agreements for consumers relating to residential immovable property.

This study aims to examine and analyze the relationship of the influence of liquidity on bank performance mediated by credit risk. Using data on Islamic banking companies listed on the IDX in 2013-2019. The methodology of this research was carried out to achieve the objectives of this study, namely how the influence of liquidity on the performance of Islamic banking companies in Indonesia which is mediated by credit risk. By processing data from data collected from the pre-pandemic and during the pandemic, this research can prove the proposed hypothesis. The analytical tool used is SEM-PLS with WarpPLS 7.0 application. The results of this study indicate that credit risk can partially mediate the relationship between the influence of liquidity on bank performance. This study succeeded in proving that the influence of liquidity on bank performance is acceptable and can be mediated by credit risk. This is in line with the Commercial Loan Theory which explains that providing loans to short-term and productive customers can minimize customer defaults, so that the company's performance will be maintained. During the current pandemic, it is one of the things that makes companies careful in managing liquidity as well as in distributing credit. Banks must be really selective in choosing loans submitted by customers, in order to avoid defaults that cause a decline in bank performance.

Purpose: The journal aims at assessing how organizational policy framework leads to strategy implementation gaps. The study population was made up of management and support staff of Kenyan commercial banks.Methodology: It employed the use of questionnaires to obtain relevant data from respondents. The study focused on 250 top, middle and lower level employees from Kenyan commercial banks. Data was analyzed using descriptive and inferential statistics. The descriptive statistics methods used include mean and standard deviation. The inferential statistics used in the study include Pearson correlation, analysis of variance (ANOVA), and coefficients. The research data was analyzed using Statistical Package for Social Sciences (SPSS) version 20 and Microsoft Excel programs. Organizational policy framework was divided into ten parameters; standard operating procedures, operations manuals, loan processing policies, human resource policies, company circulars, departmental communication, memos, instructional letters, email instructions and information technology policies. Of all the ten factors of organizational policy framework, the highly statistical significant ones were standard operational procedures and loan processing policies.Results: A correlation analysis to determine the relationship between standard operating procedures and loan processing policies established that standard operating procedures influenced loan processing policies.Unique Contribution to Theory, Practice and Policy: The study assessed how policy framework leads to strategy implementation gaps in Kenyan Commercial Banks. Further studies about factors leading to strategy implementation gaps should be conducted on other financial institutions like insurance and, indeed, other industries.

Abstract—This study aims to determine the effect of Capital Adequacy Ratio (CAR), Third Party Funds (DPK), Financing to Deposit Ratio (FDR), Non Performing Financing (NPF), and Return On Ratio (ROA) to Financing Murabaha at Islamic Commercial Banks in Indonesia for the 2012-2018 Period. There are ten samples in this study that meet the research criteria, namely BCA Syariah Bank, BRI Syariah Bank, BNI Syariah Bank, Mandiri Syariah Bank, Syariah Bukopin Bank, Panin Indonesia Syariah Bank, Jabar Banten Syariah Bank, Mega Syariah Bank, Muamalat Bank, Victoria Bank Sharia. This research method is quantitative with data processing tools usingEviews 9 and the analysis tool used was panel data regression analysis. The selected model is the modelFixed Effect which was tested by F test and t test, with a significance of 5%. The results of the study show that the ratio of Third Party Funds has a positive and significant effect on financing Murabaha which means that no matter how big the deposited DPK will affect any amount of financing Murabaha. Capital Adequacy Ratio, Financing to Deposit Ratio, Net Performing Financing, and Return on Assets does not affect financing Murabaha, which means that no matter how big the CAR, FDR, NPF, and ROA will not affect the distribution of capital adequacy, distribution of total loans, nonperforming financing and investment profits to Financing Murabaha.

The study focused on the profitability of pigeon pea production in Riyom LGA of Plateau State. Data for the study were obtained from both primary and secondary sources. Questionnaires were administered to 80 targeted farmers to get information on their socio-economic characteristics, years of experience in pigeon pea production and the reason for growing pigeon pea. The analytical tool used includes descriptive statistics to analyze the socio-economic characteristics of the farmers and farm budgeting technique (gross margin analysis) for assessing the profitability of pigeon pea production. The study also identified the constraints encountered by farmers such as high cost of labour, inadequate processing / storage facilities, problems of access to credit/loans and weed/pest infestation. The results shows that pigeon pea production is profitable with a total variable cost (TVC) at N22620.68 per ha, while gross revenue (GR) was N50185.25 per ha, the profit margin (GM) at N32564.59 and the net return per Naira invested was N0.69. It was recommended that farm inputs and improved seed varieties should be made available to farmers at affordable rates so as to enable them operate at a commercial and profitable level of pigeon pea production. These farmers should be encouraged to form a cooperative society so as to alleviate the problem of credit/ loan acquisition and procurement of genuine products as well as avoid exploitation

The study focused on the profitability of pigeon pea production in Riyom LGA of Plateau State. Data for the study were obtained from both primary and secondary sources. Questionnaires were administered to 80 targeted farmers to get information on their socio-economic characteristics, years of experience in pigeon pea production and the reason for growing pigeon pea. The analytical tool used includes descriptive statistics to analyze the socio-economic characteristics of the farmers and farm budgeting technique (gross margin analysis) for assessing the profitability of pigeon pea production. The study also identified the constraints encountered by farmers such as high cost of labour, inadequate processing / storage facilities, problems of access to credit/loans and weed/pest infestation. The results shows that pigeon pea production is profitable with a total variable cost (TVC) at N22620.68 per ha, while gross revenue (GR) was N50185.25 per ha, the profit margin (GM) at N32564.59 and the net return per Naira invested was N0.69. It was recommended that farm inputs and improved seed varieties should be made available to farmers at affordable rates so as to enable them operate at a commercial and profitable level of pigeon pea production. These farmers should be encouraged to form a cooperative society so as to alleviate the problem of credit/ loan acquisition and procurement of genuine products as well as avoid exploitation

Women are responsible for the fastest economic growth in the world through their commercial activities. Despite this notable act, women in developing countries are most times sidelined in accessing financial incentives from banks. The purpose of this study was to evaluate the criteria used by banks and the problems encountered by women in accessing building credits in Nigeria. The study used a cross-sectional survey research design that utilized an electronic questionnaire instrument. The data obtained were analyzed using frequencies, percentages, 100% stacked bars, mean score, ANOVA, and categorical regression (CAT-REG) tests. The result revealed that the primary criteria to access building credits across different banks in Nigeria were the source of income/level of income, credit status/review, and the value of the collateral. When women can access building credits from banks, it can lead to improved living conditions for women, improved work-life, and benefits for their children. However, the lack of collateral, lack of financial literacy, lack of formal employment, and lack of right to ownership of property are limiting factors in women lending from banks. Furthermore, gender discrimination, lack of financial literacy, and low educational background could influence women’s access to building credits from banks. To facilitate the provision of loans to women from banks, it is necessary to improve government policy, economic reforms and banking legislation for women’s access to loans. AcknowledgmentThe article processing charge (APC) for this paper was supported by Covenant University Centre for Research, Innovation and Discovery, Nigeria.

This study consists of two essays. The first essay investigates whether non-CEO inside director reputation matters in bank loan contracting. Reputable inside directors (RIDs) can improve borrowers’ financial reporting quality and reduce agency risk in loan contracting. Based on regression analysis of 5,104 loan facilities during 1999-2007 in the U.S., I find that borrowers with reputable inside directors enjoy lower loan interest rate and a smaller number of restrictive covenants, and are less likely to have the loans secured by collateral, compared with borrowers without RIDs. The results still hold after I control for CEO reputation, and address the endogeneity of RIDs and the joint determination of various loan contracting terms. These findings shed new light on the impact of director-level reputation in the context of bank loan market. The second essay investigates whether non-CEO inside directors with reputation incentives affect the effectiveness of a firm’s internal control over financial reporting. Internal control effectiveness is an important indicator of financial reporting quality. Using a large sample of 7,352 firm-year observations from 2004 to 2012, I find that firms with RIDs are less likely to have reported internal control weaknesses (ICWs). I also find that RIDs have a more pervasive impact on account-level ICWs than company-level ICWs. Empirical results also demonstrate that the association between RIDs and ICWs is more pronounced for firms with lower audit quality, higher CEO entrenchment, and higher cost of misreporting. Further test shows that RIDs can help to improve earnings quality through mitigating ICWs. The study results still hold after I control for CEO reputation, employing alternative proxies and estimation methods, and address the potential endogeneity problems of RIDs. The study findings add to the few empirical studies examining the determinants of ICWs and have corporate governance policy implications for regulators by supporting the desirable role of inside directors in terms of efficient contracting.

The objective of this study was to analyse how young Finnish information technology (IT) companies utilize the public sector’s open spatial data. The aim was to find out to what extent companies use public sector’s open spatial data in products and how companies are using it. In addition, defects related to data and its use and companies’ awareness of public sector open data were canvassed. Defects and unawareness might prevent or retard the utilization of public sector’s data. Public sector is collecting vast amount of data from various areas when performing public tasks. The major part of the data is spatial, meaning the data has a location aspect. Public sector is opening the data for everybody to use freely and companies could use this open spatial data for commercial purposes. High expectations have been set for the data opening: along with it, innovations and business — new companies and digital products — will be created. The European Union has promoted greatly the public sector data opening with its legislative actions. First with the PSI directive (directive on re-use of public sector data) and later with the INSPIRE directive (directive on establishing and Infrastructure for Spatial Information in the European Community). The both directives are aiming to facilitate the re-use and dissemination of public sector data, whereas the INSPIRE directive has focused on the use of interoperable spatial data by creating the spatial data infrastructure. Even if the developments are still on going, these undertakings have already created possibilities for companies to use public sector data. This applies especially to the spatial data. This study was quantitative by nature and the empirical data for the study was collected through online survey, which was targeted to randomly selected Finnish IT companies established during the years 2009–2012. Data was analyzed by descriptive statistics. The results can be generalized to the whole target population in Finland. The results of this study shows that the number of companies utilizing public sector’s open spatial data is small and the public sector’s open spatial data has not yet enabled establishing of new companies. However, companies have developed few new products with the contribution of public sector’s open spatial data and the value of the data for the products is not minor. The thesis concludes that there is a need for greater investment in promoting the public sector’s open data amongst companies: the awareness of public sector’s open spatial data could be increased. In addition, coverage of datasets and interface services could be improved. Perhaps by eliminating these defects, the number of utilizers of public sector’s open spatial data would increase. Now there is a quiet sign of awakening of the business to utilize public sector’s data.

International Telemetering Conference Proceedings / October 17-20, 1994 / Town & Country Hotel and Conference Center, San Diego, California<br>The rapid technology growth in the aerospace industry continues to manifest itself in increasingly complex computer systems and weapons systems platforms. To meet the data processing challenges associated with these new weapons systems, the Air Force Flight Test Center (AFFTC) is developing the next generation of data acquisition and processing systems under the Advanced Data Acquisition and Processing Systems (ADAPS) Program. The ADAPS program has evolved into an approach that utilizes Commercial-Off-The-Shelf (COTS) components as the foundation for Air Force enhancements to meet specific customer requirements. The ADAPS program has transitioned from concept exploration to engineering and manufacturing development (EMD). This includes the completion of a detailed requirements analysis and a overall system design. This paper will discuss the current status of the ADAPS program including the requirements analysis process, details of the system design, and the result of current COTS acquisitions.

International Telemetering Conference Proceedings / October 28-31, 1996 / Town and Country Hotel and Convention Center, San Diego, California<br>This paper discusses turnkey telemetry data acquisition and analysis systems. A brief history of previous systems used at Lockheed Martin Vought Systems is presented. Then, the paper describes systems that utilize more COTS hardware and software and discusses the time and resources saved by integrating these products into a complete system along with a description of what some newer systems will offer.

This project was created using ColdFusion 4.5 to build and implement a commercial web site to present a real picture of electronic commerce. This project is intended to provide enough information for other students who are looking for a guideline for further study and to improve their skills in business from an information management aspect.

AbstractTo remain competitive, organizations are increasingly taking advantage of the high volumes of data produced in real time for actionable insights and operational decision-making. In this chapter, we present basic concepts in real-time analytics, their importance in today’s organizations, and their applicability to the bioeconomy domains investigated in the DataBio project. We begin by introducing key terminology for event processing, and motivation for the growing use of event processing systems, followed by a market analysis synopsis. Thereafter, we provide a high-level overview of event processing system architectures, with its main characteristics and components, followed by a survey of some of the most prominent commercial and open source tools. We then describe how we applied this technology in two of the DataBio project domains: agriculture and fishery. The devised generic pipeline for IoT data real-time processing and decision-making was successfully applied to three pilots in the project from the agriculture and fishery domains. This event processing pipeline can be generalized to any use case in which data is collected from IoT sensors and analyzed in real-time to provide real-time alerts for operational decision-making.

AbstractThe extraction, transformation, and loading of event logs from information systems is the first and the most expensive step in process mining. In particular, extracting event logs from popular ERP systems such as SAP poses major challenges, given the size and the structure of the data. Open-source support for ETL is scarce, while commercial process mining vendors maintain connectors to ERP systems supporting ETL of a limited number of business processes in an ad-hoc manner. In this paper, we propose an approach to facilitate event data extraction from SAP ERP systems. In the proposed approach, we store event data in the format of object-centric event logs that efficiently describe executions of business processes supported by ERP systems. To evaluate the feasibility of the proposed approach, we have developed a tool implementing it and conducted case studies with a real-life SAP ERP system.

AbstractThe current digital transformation of many businesses and the exponential growth of digital data are two of the key factors of digital revolution. For the successful meeting of high expectations, the data platforms need to employ the recent theoretical, technological, and methodological advances in contemporary computing and data science and engineering. This chapter presents an approach to address these challenges by combining logical methods for knowledge processing and machine learning methods for data analysis into a hybrid AI-based framework. It is applicable to a wide range of problems that involve both synchronous operations and asynchronous events in different domains. The framework is a foundation for building the GATE Data Platform, which aims at the application of Big Data technologies in civil and government services, industry, and healthcare. The platform implementation will utilize several recent distributed technologies such as Internet of Things, cloud, and edge computing and will integrate them into a multilevel service-oriented architecture that supports services along the entire data value chain, while the service orchestration guarantees a high degree of interoperability, reusability, and automation. The platform is designed to be compliant with the open-source software, but its open architecture supports also mixing with commercial components and tools.

AbstractThe article seeks to emphasize existing tendencies in regulation personal data in Russia and in foreign countries. The wide use of modern technologies of data processing “big data”, “artificial intelligence”, “internet of things” does not only open new opportunities for business and people but also makes more evident the gap between individual’s interests for control of his data processing and thus protecting its privacy and commercial use of data by Internet companies. The state, on the other hand, seeks to get a more wide and exclusive access to the data collected by business entities, trying to apply a renewed concept of data sovereignty using its citizens’ personal data protection as a legal ground. The author notes the growing desire from both the state and business entities to undermine individual’s right to control his data processing as an inherent right of a data subject in order to facilitate the access to them and guarantee their interests. Awareness by the state and business of the new opportunities given by processing metadata including personal data, as a fundamental resource for the digital economy development can potentially lead to the situation where an individual will no longer be able to participate in determining the key parameters of their use. Most recent changes in Russian legislation on open access personal data that are to come into force in 2021 also leave much ground for uncertainty. In fact, they can shift the balance even more towards the interests of big business and the state.

AbstractCities demand spatial efficiencies that can be achieved only through sharing of information. Current technologies support collection, processing, and dissemination of unprecedented quantities of personal, public, and corporate information. Inherent in this milieu is an inevitable contest among societal efficiency, corporate profits, consumer convenience, personal privacy, and even freedom. The authors examine current trends in technology, data collection, legislation, and public acceptance. They find that without broad specific regulations limiting location data collection and use—including a universal protected right for individuals to pursue anonymity—governments, commercial enterprises, employers, and individuals increasingly will exploit tracking technologies at the expense of geoprivacy.

The 1980s were the worst of times for many in the banking industry. Following the deregulation of the early 1980s, the industry nearly went into shock as more than 2,000 banks and savings and loans failed. Banking could no longer be characterized as a staid enterprise of blue pin-striped suits and “banker’s hours.” Deregulation and competition made traditional ways of doing business obsolete and, in many cases, disastrous for banks that failed to adapt. A record number of banks were unable to make the transition successfully and, among those that survived, it was a difficult period that required them to reassess their operations. Software developed for the banking industry during this period reflected the contradictions of an industry in transition. Some banks tried to maintain their traditional values and operating procedures in their system design requirements. However, some software developers found that changes in the banking environment and their own software market required them to consider different values and methods of service delivery in their designs for new systems. A brief account of the industry’s evolution with computer systems sets the stage for the cases presented in this chapter. Banks are large users of computer systems and were among the first large commercial institutions to use computers. Design of banking systems has been affected by changes in technology, external regulation, competition, and the labor market. These changes have combined to define the operational objectives of new computer system designs. During the 1950s and 1960s, banks installed large mainframe systems for back office transaction processing, such as account updating, on a batch system mode. By the 1970s large banks had invested extensively in mainframe computer systems and smaller banks were beginning to adopt computer systems as prices dropped and smaller computers became more powerful. In both large and small banks, computer applications were increasingly introduced to support front office transactions of tellers and customer service representatives in branch offices. Because these systems make data instantly available for a variety of steps in processing transactions, they integrate people throughout the banking organization.

Understanding and mitigating against high heat loads at leading and blunt aerodynamic surfaces during hypersonic flight represents an ongoing technological challenge. Recent work has shown that the commercial software package, STAR CCM+, can provide reliable predictions of hypersonic aerothermodynamic flow and heating, under a wide range of complex, but common conditions. This chapter presents a preliminary experimental and numerical investigation of hypersonic flow over closed- and open-nose missile bodies, where the latter have been proposed as a means of reducing leading edge heat transfer. Four contributions are presented. First, a novel singular value decomposition (SVD)-based image processing technique is introduced, which significantly enhances the quality of raw schlieren images obtained in high-speed compressible flows. Second, numerically predicted hypersonic flow about a scale-model missile body, obtained using STAR-CCM+, is validated against experimental schlieren image data, an empirical correlation connecting bow shock stand-off distance and shock density ratio, and estimated drag forces. Third, scaling and physical arguments are presented as a means of choosing appropriate gas equations of state and for interpreting results of numerical simulations and experiments. Last, numerical experiments show that the forward facing cavity used in our wind tunnel experiments functions as a heat sink, reducing heat fluxes on the missile body downstream of the cavity.

" Most commercial banks face the problem of defaulting on their loans, and this problem has received great attention from officials in the banking sector, and its expansion is due to a number of economic, social and political factors .... etc., so the research came to shed light on the reality of non-performing loans in banks. commercial banks in Iraq, and revealing the relationship between non-performing loans and the profitability of commercial banks, the research sample for the period 2010-2020. The method of cross-sectional time-series data (Panel Data), or the so-called longitudinal "

" Most commercial banks face the problem of defaulting on their loans, and this problem has received great attention from officials in the banking sector, and its expansion is due to a number of economic, social and political factors .... etc., so the research came to shed light on the reality of non-performing loans in banks. commercial banks in Iraq, and revealing the relationship between non-performing loans and the profitability of commercial banks, the research sample for the period 2010-2020. The method of cross-sectional time-series data (Panel Data), or the so-called longitudinal "

Fatigue is the major aging mechanism for pipelines and components that are subject to transient during plant operation. In order to avoid abrupt accidents caused by fatigue failure, it is necessary to adopt corresponding techniques to monitor components condition due to fatigue damage. Experience shows that on-line fatigue monitoring system is the most effective way to monitor fatigue damage status of pressure-retaining boundary subjected to operational loads in real-time for NPPs. Several commercial fatigue monitoring systems have been put into application in the nuclear power plants. In this paper, the difference of the international fatigue monitoring systems were compared. As the key technologies of fatigue monitoring system development, the processing of operational load data and the stress influencing function development at monitored locations were researched in the paper. Furthermore, in order to study on the methodologies of the key issues of the system development, the experimental facility was established to improve the knowledge on fatigue monitoring system development for two kinds of input data modules, and the reasonability of the different methods in the study was proved reciprocally. Finally, the on-line fatigue monitoring system for pipelines was established to evaluate actual fatigue damage status.

Microfinance Institutions (MFIs) are organizations that provide small loans to borrowers who typically lack collateral, steady employment, or a verifiable credit history and therefore do not have access to traditional commercial banking. Blockchain technology could be used to create a more holistic view of the financial position of a potential borrower, which could result in better lending decisions. This study explores how blockchain technology has the potential to assist Microfinance Institutions in the water and sanitation sector through a pilot project developed in Peru. The improvements seen in the existing microfinance ecosystem during the implementation of the blockchain platform can be sorted into two main groups: improved institutional performance, and data ownership for the individuals.

The effect of rumen available protein amino acids and carbohydrates on microbial protein synthesis, amino acid flow and performance of high yielding dairy cows was studied. A significant relationship between the effective degradabilities of OM in feedstuffs and the in vivo ruminal OM degradation of diets of dairy cows was found. The in situ method enabled the prediction of ruminal nutrients degradability response to processing of energy and nitragenous supplements. The AA profile of the rumen undegradable protein was modified by the processing method. In a continuous culture study total N and postruminal AA flows, and bacterial efficiency, is maximal at rumen degradable levels of 65% of the CP. Responses to rumen degradable non carbohydrate (NSC) were linear up to at least 27% of DM. Higher CP flow in the abomasum was found for cows fed high ruminally degradable OM and low ruminally degradable CP diet. It appeared that in dairy cows diets, the ratio of rumen degradable OM to rumenally degradable CP should be at least 5:1 in order to maximize postruminal CP flow. The efficiency of microbial CP synthesis was higher for diets supplemented with 33% of rumen undegradable protein, with greater amounts of bacterial AA reaching the abomasum. Increase in ruminal carbohydrate availability by using high moisture corn increased proportions of propionate, postruminal nutrients flow, postruminal starch digestibility, ruminal availability of NSC, uptake of energy substrates by the mammory gland. These modifications resulted with improvement in the utilization of nonessential AA for milk protein synthesis, in higher milk protein yield. Higher postruminal NSC digestibility and higher efficiency of milk protein production were recorded in cows fed extruded corn. Increasing feeding frequency increased flow of N from the rumen to the blood, reduced diurnal variation in ruminal and ammonia, and of plasma urea and improved postruminal NSC and CIP digestibility and total tract digestibilities. Milk and constituent yield increased with more frequent feeding. In a study performed in a commercial dairy herd, changes in energy and nitrogenous substrates level suggested that increasing feeding frequency may improve dietary nitrogen utilization and may shift metabolism toward more glucogenesis. It was concluded that efficiency of milk protein yield in high producing cows might be improved by an optimization of ruminal and post-ruminal supplies of energy and nitrogenous substrates. Such an optimization can be achieved by processing of energy and nitrogenous feedstuffs, and by increasing feeding frequency. In situ data may provide means for elucidation of the optimal processing conditions.

Commercial agriculture has come under increasing pressure to reduce nitrogen fertilizer inputs in order to minimize potential nonpoint source pollution of ground and surface waters. This has resulted in increased interest in site specific fertilizer management. One way to solve pollution problems would be to determine crop nutrient needs in real time, using remote detection, and regulating fertilizer dispensed by an applicator. By detecting actual plant needs, only the additional nitrogen necessary to optimize production would be supplied. This research aimed to develop techniques for real time assessment of nitrogen status of corn using a mobile sensor with the potential to regulate nitrogen application based on data from that sensor. Specifically, the research first attempted to determine the system parameters necessary to optimize reflectance spectra of corn plants as a function of growth stage, chlorophyll and nitrogen status. In addition to that, an adaptable, multispectral sensor and the signal processing algorithm to provide real time, in-field assessment of corn nitrogen status was developed. Spectral characteristics of corn leaves reflectance were investigated in order to estimate the nitrogen status of the plants, using a commercial laboratory spectrometer. Statistical models relating leaf N and reflectance spectra were developed for both greenhouse and field plots. A basis was established for assessing nitrogen status using spectral reflectance from plant canopies. The combined effect of variety and N treatment was studied by measuring the reflectance of three varieties of different leaf characteristic color and five different N treatments. The variety effect on the reflectance at 552 nm was not significant (a = 0.01), while canonical discriminant analysis showed promising results for distinguishing different variety and N treatment, using spectral reflectance. Ambient illumination was found inappropriate for reliable, one-beam spectral reflectance measurement of the plants canopy due to the strong spectral lines of sunlight. Therefore, artificial light was consequently used. For in-field N status measurement, a dark chamber was constructed, to include the sensor, along with artificial illumination. Two different approaches were tested (i) use of spatially scattered artificial light, and (ii) use of collimated artificial light beam. It was found that the collimated beam along with a proper design of the sensor-beam geometry yielded the best results in terms of reducing the noise due to variable background, and maintaining the same distance from the sensor to the sample point of the canopy. A multispectral sensor assembly, based on a linear variable filter was designed, constructed and tested. The sensor assembly combined two sensors to cover the range of 400 to 1100 nm, a mounting frame, and a field data acquisition system. Using the mobile dark chamber and the developed sensor, as well as an off-the-shelf sensor, in- field nitrogen status of the plants canopy was measured. Statistical analysis of the acquired in-field data showed that the nitrogen status of the com leaves can be predicted with a SEP (Standard Error of Prediction) of 0.27%. The stage of maturity of the crop affected the relationship between the reflectance spectrum and the nitrogen status of the leaves. Specifically, the best prediction results were obtained when a separate model was used for each maturity stage. In-field assessment of the nitrogen status of corn leaves was successfully carried out by non contact measurement of the reflectance spectrum. This technology is now mature to be incorporated in field implements for on-line control of fertilizer application.

Our original objectives were to assess the unexplored dimension of alternative splicing as a source of genetic variation. In particular, we sought to initially establish an alternative splicing database for Arabidopsis, the only plant for which a near-complete genome has been assembled. Our goal was to then use the database, in part, to advance plant gene prediction programs that are currently a limiting factor in annotating genomic sequence data and thus will facilitate the exploitation of the ever increasing quantity of raw genomic data accumulating for plants. Additionally, the database was to be used to generate probes for establishing high-throughput alternative transcriptome analysis in the form of a splicing-specific oligonucleotide microarray. We achieved the first goal and established a database and web site termed Alternative Splicing In Plants (ASIP, http://www.plantgdb.org/ASIP/). We also thoroughly reviewed the extent of alternative splicing in plants (Arabidopsis and rice) and proposed mechanisms for transcript processing. We noted that the repertoire of plant alternative splicing differs from that encountered in animals. For example, intron retention turned out to be the major type. This surprising development was proven by direct RNA isolation techniques. We further analyzed EST databases available from many plants and developed a process to assess their alternative splicing rate. Our results show that the lager genome-sized plant species have enhanced rates of alternative splicing. We did advance gene prediction accuracy in plants by incorporating scoring for non-canonical introns. Our data and programs are now being used in the continuing annotation of plant genomes of agronomic importance, including corn, soybean, and tomato. Based on the gene annotation data developed in the early part of the project, it turned out that specific probes for different exons could not be scaled up to a large array because no uniform hybridization conditions could be found. Therefore, we modified our original objective to design and produce an oligonucleotide microarray for probing alternative splicing and realized that it may be reasonable to investigate the extent of alternative splicing using novel commercial whole genome arrays. This possibility was directly examined by establishing algorithms for the analysis of such arrays. The predictive value of the algorithms was then shown by isolation and verification of alternative splicing predictions from the published whole genome array databases. The BARD-funded work provides a significant advance in understanding the extent and possible roles of alternative splicing in plants as well as a foundation for advances in computational gene prediction.

1.1 Macroeconomic summary Economic recovery has consistently outperformed the technical staff’s expectations following a steep decline in activity in the second quarter of 2020. At the same time, total and core inflation rates have fallen and remain at low levels, suggesting that a significant element of the reactivation of Colombia’s economy has been related to recovery in potential GDP. This would support the technical staff’s diagnosis of weak aggregate demand and ample excess capacity. The most recently available data on 2020 growth suggests a contraction in economic activity of 6.8%, lower than estimates from January’s Monetary Policy Report (-7.2%). High-frequency indicators suggest that economic performance was significantly more dynamic than expected in January, despite mobility restrictions and quarantine measures. This has also come amid declines in total and core inflation, the latter of which was below January projections if controlling for certain relative price changes. This suggests that the unexpected strength of recent growth contains elements of demand, and that excess capacity, while significant, could be lower than previously estimated. Nevertheless, uncertainty over the measurement of excess capacity continues to be unusually high and marked both by variations in the way different economic sectors and spending components have been affected by the pandemic, and by uneven price behavior. The size of excess capacity, and in particular the evolution of the pandemic in forthcoming quarters, constitute substantial risks to the macroeconomic forecast presented in this report. Despite the unexpected strength of the recovery, the technical staff continues to project ample excess capacity that is expected to remain on the forecast horizon, alongside core inflation that will likely remain below the target. Domestic demand remains below 2019 levels amid unusually significant uncertainty over the size of excess capacity in the economy. High national unemployment (14.6% for February 2021) reflects a loose labor market, while observed total and core inflation continue to be below 2%. Inflationary pressures from the exchange rate are expected to continue to be low, with relatively little pass-through on inflation. This would be compatible with a negative output gap. Excess productive capacity and the expectation of core inflation below the 3% target on the forecast horizon provide a basis for an expansive monetary policy posture. The technical staff’s assessment of certain shocks and their expected effects on the economy, as well as the presence of several sources of uncertainty and related assumptions about their potential macroeconomic impacts, remain a feature of this report. The coronavirus pandemic, in particular, continues to affect the public health environment, and the reopening of Colombia’s economy remains incomplete. The technical staff’s assessment is that the COVID-19 shock has affected both aggregate demand and supply, but that the impact on demand has been deeper and more persistent. Given this persistence, the central forecast accounts for a gradual tightening of the output gap in the absence of new waves of contagion, and as vaccination campaigns progress. The central forecast continues to include an expected increase of total and core inflation rates in the second quarter of 2021, alongside the lapse of the temporary price relief measures put in place in 2020. Additional COVID-19 outbreaks (of uncertain duration and intensity) represent a significant risk factor that could affect these projections. Additionally, the forecast continues to include an upward trend in sovereign risk premiums, reflected by higher levels of public debt that in the wake of the pandemic are likely to persist on the forecast horizon, even in the context of a fiscal adjustment. At the same time, the projection accounts for the shortterm effects on private domestic demand from a fiscal adjustment along the lines of the one currently being proposed by the national government. This would be compatible with a gradual recovery of private domestic demand in 2022. The size and characteristics of the fiscal adjustment that is ultimately implemented, as well as the corresponding market response, represent another source of forecast uncertainty. Newly available information offers evidence of the potential for significant changes to the macroeconomic scenario, though without altering the general diagnosis described above. The most recent data on inflation, growth, fiscal policy, and international financial conditions suggests a more dynamic economy than previously expected. However, a third wave of the pandemic has delayed the re-opening of Colombia’s economy and brought with it a deceleration in economic activity. Detailed descriptions of these considerations and subsequent changes to the macroeconomic forecast are presented below. The expected annual decline in GDP (-0.3%) in the first quarter of 2021 appears to have been less pronounced than projected in January (-4.8%). Partial closures in January to address a second wave of COVID-19 appear to have had a less significant negative impact on the economy than previously estimated. This is reflected in figures related to mobility, energy demand, industry and retail sales, foreign trade, commercial transactions from selected banks, and the national statistics agency’s (DANE) economic tracking indicator (ISE). Output is now expected to have declined annually in the first quarter by 0.3%. Private consumption likely continued to recover, registering levels somewhat above those from the previous year, while public consumption likely increased significantly. While a recovery in investment in both housing and in other buildings and structures is expected, overall investment levels in this case likely continued to be low, and gross fixed capital formation is expected to continue to show significant annual declines. Imports likely recovered to again outpace exports, though both are expected to register significant annual declines. Economic activity that outpaced projections, an increase in oil prices and other export products, and an expected increase in public spending this year account for the upward revision to the 2021 growth forecast (from 4.6% with a range between 2% and 6% in January, to 6.0% with a range between 3% and 7% in April). As a result, the output gap is expected to be smaller and to tighten more rapidly than projected in the previous report, though it is still expected to remain in negative territory on the forecast horizon. Wide forecast intervals reflect the fact that the future evolution of the COVID-19 pandemic remains a significant source of uncertainty on these projections. The delay in the recovery of economic activity as a result of the resurgence of COVID-19 in the first quarter appears to have been less significant than projected in the January report. The central forecast scenario expects this improved performance to continue in 2021 alongside increased consumer and business confidence. Low real interest rates and an active credit supply would also support this dynamic, and the overall conditions would be expected to spur a recovery in consumption and investment. Increased growth in public spending and public works based on the national government’s spending plan (Plan Financiero del Gobierno) are other factors to consider. Additionally, an expected recovery in global demand and higher projected prices for oil and coffee would further contribute to improved external revenues and would favor investment, in particular in the oil sector. Given the above, the technical staff’s 2021 growth forecast has been revised upward from 4.6% in January (range from 2% to 6%) to 6.0% in April (range from 3% to 7%). These projections account for the potential for the third wave of COVID-19 to have a larger and more persistent effect on the economy than the previous wave, while also supposing that there will not be any additional significant waves of the pandemic and that mobility restrictions will be relaxed as a result. Economic growth in 2022 is expected to be 3%, with a range between 1% and 5%. This figure would be lower than projected in the January report (3.6% with a range between 2% and 6%), due to a higher base of comparison given the upward revision to expected GDP in 2021. This forecast also takes into account the likely effects on private demand of a fiscal adjustment of the size currently being proposed by the national government, and which would come into effect in 2022. Excess in productive capacity is now expected to be lower than estimated in January but continues to be significant and affected by high levels of uncertainty, as reflected in the wide forecast intervals. The possibility of new waves of the virus (of uncertain intensity and duration) represents a significant downward risk to projected GDP growth, and is signaled by the lower limits of the ranges provided in this report. Inflation (1.51%) and inflation excluding food and regulated items (0.94%) declined in March compared to December, continuing below the 3% target. The decline in inflation in this period was below projections, explained in large part by unanticipated increases in the costs of certain foods (3.92%) and regulated items (1.52%). An increase in international food and shipping prices, increased foreign demand for beef, and specific upward pressures on perishable food supplies appear to explain a lower-than-expected deceleration in the consumer price index (CPI) for foods. An unexpected increase in regulated items prices came amid unanticipated increases in international fuel prices, on some utilities rates, and for regulated education prices. The decline in annual inflation excluding food and regulated items between December and March was in line with projections from January, though this included downward pressure from a significant reduction in telecommunications rates due to the imminent entry of a new operator. When controlling for the effects of this relative price change, inflation excluding food and regulated items exceeds levels forecast in the previous report. Within this indicator of core inflation, the CPI for goods (1.05%) accelerated due to a reversion of the effects of the VAT-free day in November, which was largely accounted for in February, and possibly by the transmission of a recent depreciation of the peso on domestic prices for certain items (electric and household appliances). For their part, services prices decelerated and showed the lowest rate of annual growth (0.89%) among the large consumer baskets in the CPI. Within the services basket, the annual change in rental prices continued to decline, while those services that continue to experience the most significant restrictions on returning to normal operations (tourism, cinemas, nightlife, etc.) continued to register significant price declines. As previously mentioned, telephone rates also fell significantly due to increased competition in the market. Total inflation is expected to continue to be affected by ample excesses in productive capacity for the remainder of 2021 and 2022, though less so than projected in January. As a result, convergence to the inflation target is now expected to be somewhat faster than estimated in the previous report, assuming the absence of significant additional outbreaks of COVID-19. The technical staff’s year-end inflation projections for 2021 and 2022 have increased, suggesting figures around 3% due largely to variation in food and regulated items prices. The projection for inflation excluding food and regulated items also increased, but remains below 3%. Price relief measures on indirect taxes implemented in 2020 are expected to lapse in the second quarter of 2021, generating a one-off effect on prices and temporarily affecting inflation excluding food and regulated items. However, indexation to low levels of past inflation, weak demand, and ample excess productive capacity are expected to keep core inflation below the target, near 2.3% at the end of 2021 (previously 2.1%). The reversion in 2021 of the effects of some price relief measures on utility rates from 2020 should lead to an increase in the CPI for regulated items in the second half of this year. Annual price changes are now expected to be higher than estimated in the January report due to an increased expected path for fuel prices and unanticipated increases in regulated education prices. The projection for the CPI for foods has increased compared to the previous report, taking into account certain factors that were not anticipated in January (a less favorable agricultural cycle, increased pressure from international prices, and transport costs). Given the above, year-end annual inflation for 2021 and 2022 is now expected to be 3% and 2.8%, respectively, which would be above projections from January (2.3% and 2,7%). For its part, expected inflation based on analyst surveys suggests year-end inflation in 2021 and 2022 of 2.8% and 3.1%, respectively. There remains significant uncertainty surrounding the inflation forecasts included in this report due to several factors: 1) the evolution of the pandemic; 2) the difficulty in evaluating the size and persistence of excess productive capacity; 3) the timing and manner in which price relief measures will lapse; and 4) the future behavior of food prices. Projected 2021 growth in foreign demand (4.4% to 5.2%) and the supposed average oil price (USD 53 to USD 61 per Brent benchmark barrel) were both revised upward. An increase in long-term international interest rates has been reflected in a depreciation of the peso and could result in relatively tighter external financial conditions for emerging market economies, including Colombia. Average growth among Colombia’s trade partners was greater than expected in the fourth quarter of 2020. This, together with a sizable fiscal stimulus approved in the United States and the onset of a massive global vaccination campaign, largely explains the projected increase in foreign demand growth in 2021. The resilience of the goods market in the face of global crisis and an expected normalization in international trade are additional factors. These considerations and the expected continuation of a gradual reduction of mobility restrictions abroad suggest that Colombia’s trade partners could grow on average by 5.2% in 2021 and around 3.4% in 2022. The improved prospects for global economic growth have led to an increase in current and expected oil prices. Production interruptions due to a heavy winter, reduced inventories, and increased supply restrictions instituted by producing countries have also contributed to the increase. Meanwhile, market forecasts and recent Federal Reserve pronouncements suggest that the benchmark interest rate in the U.S. will remain stable for the next two years. Nevertheless, a significant increase in public spending in the country has fostered expectations for greater growth and inflation, as well as increased uncertainty over the moment in which a normalization of monetary policy might begin. This has been reflected in an increase in long-term interest rates. In this context, emerging market economies in the region, including Colombia, have registered increases in sovereign risk premiums and long-term domestic interest rates, and a depreciation of local currencies against the dollar. Recent outbreaks of COVID-19 in several of these economies; limits on vaccine supply and the slow pace of immunization campaigns in some countries; a significant increase in public debt; and tensions between the United States and China, among other factors, all add to a high level of uncertainty surrounding interest rate spreads, external financing conditions, and the future performance of risk premiums. The impact that this environment could have on the exchange rate and on domestic financing conditions represent risks to the macroeconomic and monetary policy forecasts. Domestic financial conditions continue to favor recovery in economic activity. The transmission of reductions to the policy interest rate on credit rates has been significant. The banking portfolio continues to recover amid circumstances that have affected both the supply and demand for loans, and in which some credit risks have materialized. Preferential and ordinary commercial interest rates have fallen to a similar degree as the benchmark interest rate. As is generally the case, this transmission has come at a slower pace for consumer credit rates, and has been further delayed in the case of mortgage rates. Commercial credit levels stabilized above pre-pandemic levels in March, following an increase resulting from significant liquidity requirements for businesses in the second quarter of 2020. The consumer credit portfolio continued to recover and has now surpassed February 2020 levels, though overall growth in the portfolio remains low. At the same time, portfolio projections and default indicators have increased, and credit establishment earnings have come down. Despite this, credit disbursements continue to recover and solvency indicators remain well above regulatory minimums. 1.2 Monetary policy decision In its meetings in March and April the BDBR left the benchmark interest rate unchanged at 1.75%.