Questões de Língua Inglesa do ano 2025

   Many studies reveal the contributions of plant breeding and agronomy to farm productivity and their role in reshaping global diets. However, historical accounts also implicate these sciences in the creation of new problems, from novel disease vulnerabilities propagated through industrial monocrops to the negative ecological and public health consequences of crops dependent on chemical inputs and industrialized food systems more generally.

   Increasingly, historical analyses also highlight the expertise variously usurped, overlooked, abandoned, or suppressed in the pursuit of “modern” agricultural science. Experiment stations and “improved” plants were instruments of colonialism, means of controlling lands and lives of peoples typically labeled as “primitive” and “backward” by imperial authorities. In many cases, the assumptions of colonial improvers persisted in the international development programs that have sought since the mid-20th century to deliver “modern” science to farming communities in the Global South. 

   Awareness of these issues has brought alternative domains of crop science such as agroecology to the fore in recent decades, as researchers reconcile the need for robust crop knowledge and know-how with the imperatives of addressing social and environmental injustice.

Helen Anne Curry; Ryan Nehring. The history of crop science and the future of food. Internet: (adapted)

     In the 20th century, we made tremendous advances in discovering fundamental principles in different scientific disciplines that created major breakthroughs in management and technology for agricultural systems, mostly by empirical means. However, as we enter the 21st century, agricultural research has more difficult and complex problems to solve.

      The environmental consciousness of the general public is requiring us to modify farm management to protect water, air, and soil quality, while staying economically profitable. At the same time, market-based global competition in agricultural products is challenging economic viability of the traditional agricultural systems, and requires the development of new and dynamic production systems. Fortunately, the new electronic technologies can provide us a vast amount of real-time information about crop conditions and near-term weather via remote sensing by satellites or ground-based instruments and the Internet, that can be utilized to develop a whole new level of management. However, we need the means to capture and make sense of this vast amount of site-specific data.

        Our customers, the agricultural producers, are asking for a quicker transfer of research results in an integrated usable form for site-specific management. Such a request can only be met with system models, because system models are indeed the integration and quantification of current knowledge based on fundamental principles and laws. Models enhance understanding of data taken under certain conditions and help extrapolate their applications to other conditions and locations. 

        Many studies reveal the contributions of plant breeding and agronomy to farm productivity and their role in reshaping global diets. However, historical accounts also implicate these sciences in the creation of new problems, from novel disease vulnerabilities propagated through industrial monocrops to the negative ecological and public health consequences of crops dependent on chemical inputs and industrialized food systems more generally.

       Increasingly, historical analyses also highlight the expertise variously usurped, overlooked, abandoned, or suppressed in the pursuit of “modern” agricultural science. Experiment stations and “improved” plants were instruments of colonialism, means of controlling lands and lives of peoples typically labeled as “primitive” and “backward” by imperial authorities. In many cases, the assumptions of colonial improvers persisted in the international development programs that have sought since the mid-20th century to deliver “modern” science to farming communities in the Global South.

         Awareness of these issues has brought alternative domains of crop science such as agroecology to the fore in recent decades, as researchers reconcile the need for robust crop knowledge and know-how with the imperatives of addressing social and environmental injustice. 

        Many studies reveal the contributions of plant breeding and agronomy to farm productivity and their role in reshaping global diets. However, historical accounts also implicate these sciences in the creation of new problems, from novel disease vulnerabilities propagated through industrial monocrops to the negative ecological and public health consequences of crops dependent on chemical inputs and industrialized food systems more generally.

       Increasingly, historical analyses also highlight the expertise variously usurped, overlooked, abandoned, or suppressed in the pursuit of “modern” agricultural science. Experiment stations and “improved” plants were instruments of colonialism, means of controlling lands and lives of peoples typically labeled as “primitive” and “backward” by imperial authorities. In many cases, the assumptions of colonial improvers persisted in the international development programs that have sought since the mid-20th century to deliver “modern” science to farming communities in the Global South.

         Awareness of these issues has brought alternative domains of crop science such as agroecology to the fore in recent decades, as researchers reconcile the need for robust crop knowledge and know-how with the imperatives of addressing social and environmental injustice. 

        The business intelligence (BI) industry has been challenged with poor user adoption for years. Yet, many CIOs continue to push BI as a core initiative. Billions of dollars have been spent on traditional BI, but adoption rates are below 30%. Why? Successfully levering the full capabilities of business intelligence is still difficult to achieve, and product managers are searching for more. These individuals are looking for ways to expand the impact and value of their BI tools but are lost about where to start.

        The overall goal of BI is to provide business teams with the proper data and information at the right time to create insightful, data-driven decision-making. However, these solutions fall short and continually fail the industry through inefficiency, hefty costs, and an overall lack of value and insightful data production.

        Currently, traditional BI solutions force users to exit their current workflow to even attempt and secure any valuable data. When your team is operating in the middle of their workflow and needs data to inform a decision, they shouldn’t have to exit the application to enter yet another application, gather data and then jump back in. The likelihood of delays in report deliverability also factors into this headache. This process dramatically slows down any workflow and causes frustration for employees, especially when the data secured isn’t always useful.

        Additionally, many BI tools are not designed for business users but instead for more technical individuals within the organization. Traditional vendors often try to cover the complexity of their solution with self-service options and features, but users continue to feel like they need an advanced engineering or computer science degree to navigate them. This sucks up valuable time for non-technical users as they work to navigate a difficult platform to get the information they need.

Internet:: <rtinsights.com>  (adapted).