Nonlinear Feed Formulation For Broiler: Modeling And Optimization

The current scenario requires the application of new computational tools for the feed formulation strategy that uses mathematical modeling in decision making. Noteworthy is the nonlinear programming, which aims not only to formulate a diet that meets the needs of the animal, but also the minimum cost and the maximum profit margin. Thus, the work aimed to validate the use of the nonlinear model (NLM), with maximization of the economic return, through estimates of animal performance and feed costs, according to the price variation of the kg of the broiler (price historical average of 2009 and 2010), the phases of creation and sex. For this purpose, 480 broiler broiler chickens, 240 males and 240 females of the same strain (Cobb 500) were used, from 1 to 56 days of age. The experimental design was entirely randomized, totaling 6 treatments (increasing or decreasing the average historical price of live chicken by 25% or 50%), with 4 replicates and 10 broiler chickens per experimental plot. Performance (weight gain and feed consumption), total energy consumption and profit margin were evaluated. Regarding the formulation principle (Linear and Nonlinear), the performance was very similar in relation to the studied parameters. However, when simulated values of 50% below the historical average, performance was significantly impaired in this specific condition. However, due to the profit margin, it demonstrated that the principle of nonlinear formulation allows to significantly reduce losses (P <0.05), mainly in unfavorable conditions of the price of chicken in the market. It is concluded that the nonlinear principle is more appropriate, since the requirements of all nutrients are automatically adjusted by the mathematical model and with the premise of increasing profitability, different from the linear one, which is to achieve maximum performance and not is directly related to the economic factor

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Uso de paclobutrazol como estratégia para redução do porte e da brotação lateral de plantas de tomateiro The use of paclobutrazol as a strategy for controlling height and side shooting of tomato plants

Objetivou-se, no trabalho, avaliar o efeito da aplicação de diferentes concentrações (0; 50; 100 e 150 mg L-1) de paclobutrazol (PBZ) sobre o crescimento, a emissão de brotos laterais e a produtividade de dois híbridos de tomateiro (Jennifer e AF 7631), em campo aberto e em ambiente protegido. O trabalho foi desenvolvido de abril a outubro de 2007, na UNESP - Campus de Ilha Solteira, estado de São Paulo. O PBZ foi aplicado às mudas, aos 15 dias após a semeadura, avaliando-se seu efeito, nas concentrações testadas, em plantas de tomateiro após o seu transplantio. Adotou-se o delineamento em blocos casualizados, em esquema fatorial, com análise para grupo de experimentos, modelo fixo, com 4 blocos por ambiente e 9 plantas por parcela. Como resultado, o híbrido Jennifer apresentou maiores taxas de crescimento absoluto, resultando em plantas com maior altura que o AF 7631, aos 60 dias após o transplantio, sem que houvesse diferenças entre eles, no que se refere à brotação lateral e produtividade. Em ambiente protegido, foram obtidas plantas mais vigorosas, com maior altura, brotação lateral e maior produtividade que em campo aberto. O uso de concentrações crescentes de paclobutrazol reduziu a taxa de crescimento e a altura de plantas, bem como reduziu a brotação lateral e a produtividade da cultura.The objective of this study was to evaluate the effect of different concentrations (0; 50; 100 and 150 mg L-1) of paclobutrazol (PBZ) on growth, side shoot emission and yield of two tomato hybrids (Jennifer and AF 7631), cultivated in open field and in protected environment. The work was conducted from April to October of 2007, at UNESP - Campus of Ilha Solteira, State of São Paulo. The PBZ was applied to seedlings 15 days after sowing, and its effects were evaluated, considering the tested concentrations, for tomato plants after the transplant. A randomized blocks design was used, in a factorial scheme, with analysis for series of experiments, in a fixed model, consisting of 4 replicates for environment and 9 plants per plot. As results, the hybrid Jennifer showed higher rates of absolute growth, resulting in higher plants than AF 7631, on the 60th day after the transplantation, but, with no differences between them regarding side shooting and crop yield. In the greenhouse, higher plants were obtained, with higher side shooting and higher yield than that observed in the open field. The use of increasing concentrations of paclobutrazol reduced the height of plants and their growth rate, decreased side shooting, and reduced the crop yield