Literatura infantil: dos textos à educação literária

A literatura infantil conceitualmente carece de alguma reflexão devido a sua característica etimológica polissêmica. Dessa forma a incerteza que pressupõe o entendimento da literatura infantil contribui para a sua pouca utilização fato que ocorre em muitos contextos escolares. Portanto este texto tem por objetivo discutir: os pressupostos conceituais da literatura infantil e da educação literária; a compreensão de várias interfaces dos textos e da educação literária; A metodologia deste trabalho é cunho qualitativo, sendo bibliográfico e natureza exploratória. Dentro dessas perspectivas este texto será fundamentado sob a luz de autores como: LAJOLO (1977), BAKHTIN (2000), YOPP & YOPP (2006), SOUZA (2012), entre outros. Os textos literários possuem características peculiares no que se refere aos fatos presentes no seu conteúdo o que diferencia dos outros textos não literários, essa linguagem elaborada de modo artístico e com recursos diferenciados despertando o universo imaginário sem perder a interação do mundo real. Assim a Literatura infantil e Educação Literária quando compreendida pelo docente é uma possibilidade de ensino, que desperta a imaginação, a criatividade e aprendizagem de forma significativa e lúdica na educação da infância.CIEC - Centro de Investigação em Estudos da Criança, IE, UM (UI 317 da FCT

Sophia, Mésseder e um Timor contado aos jovens

Devido a sucessivos e quase ininterruptos períodos bélicos e de ocupação, a fragilidades socioeconómicas e à ausência de um ensino sistematizado numa língua oficial, a produção literária timorense escrita revela-se bastante modesta. Apesar de a literatura timorense ter assistido ao despertar do género romanesco - sendo Luís Cardoso o mais proeminente escritor de Timor -, a invenção literária dedicada a um público mais jovem é praticamente inexistente. Ainda assim, os jovens timorenses, que têm acesso aos livros, podem ler algumas obras traduzidas para tétum e, também, esparsos contos do património oral nativo. O presente exercício visa, num primeiro momento, lançar um olhar crítico sobre a literatura infantil em Timor-Leste. Num segundo momento, utilizar-se-á a historiografia universal e as pistas culturais timorenses para compreender e divulgar a produção estética de Sophia de Mello Breyner (Anjo de Timor) e de João Pedro Mésseder (Timor Lorosa‟e – A ilha do sol nascente) à luz dos códigos nativos. É de crer que o exercício se mostre pertinente em três aspetos convergentes e complementares: na promoção de obras literárias alinhavadas com o património e a identidade timorense; na compreensão das obras sob a perspectiva da mundividência autóctone e, também, na divulgação da cultura timorense.CIEC - Centro de Investigação em Estudos da Criança, IE, UM (UI 317 da FCT

Timor Leste contado aos jovens, patrimônio e identidade: perspectivas da mundividência autóctone

Devido a sucessivos e quase ininterruptos períodos bélicos e de ocupação, a fragilidades socioeconómicas e à ausência de um ensino sistematizado numa língua oficial, a produção literária timorense escrita revela-se bastante modesta. Apesar de a literatura timorense ter assistido ao despertar do género romanesco - sendo Luís Cardoso o mais proeminente escritor de Timor -, a invenção literária dedicada a um público mais jovem é praticamente inexistente. Ainda assim, os jovens timorenses, que têm acesso aos livros, podem ler algumas obras traduzidas para tétum e, também, esparsos contos do património oral nativo. O presente exercício visa, num primeiro momento, lançar um olhar crítico sobre a literatura infantil em Timor-Leste. Num segundo momento, utilizar-se-á a historiografia universal e as pistas culturais timorenses para compreender e divulgar a produção estética de Sophia de Mello Breyner (Anjo de Timor) e de João Pedro Mésseder (Timor Lorosa‟e – A ilha do sol nascente) à luz dos códigos nativos. É de crer que o exercício se mostre pertinente em três aspetos convergentes e complementares: na promoção de obras literárias alinhavadas com o património e a identidade timorense; na compreensão das obras sob a perspectiva da mundividência autóctone e, também, na divulgação da cultura timorense.CIEC - Centro de Investigação em Estudos da Criança, IE, UM (UI 317 da FCT

Is closing the agricultural yield gap a “risky” endeavor?

CONTEXT: Sub-Saharan Africa (SSA) has the climatic and biophysical potential to grow the crops it needs to meet rapidly growing food demand; however, agricultural productivity remains low. While potential maize yields in Zambia are 9 t per hectare (t/ha), the average farmer produces only 1–2. OBJECTIVE: We evaluate the contribution of responses to weather risk to that gap by decomposing the yield gap in maize in Zambia. While we know that improved seed and fertilizer can expand yield and profit, they may also increase the variance of yield under different weather outcomes, reducing their adoption. METHODS: We use a novel approach combining crop modeling and statistical analysis of survey data to obtain the yield gap components in Zambia driven by input cost and input risk. We use a crop model to simulate district-level marginal effects of fertilizer and seed maturity choice on the mean and variance of expected yield and profit under all-weather outcomes for each district for the past 30 years. We compare input levels that maximize expected yield to those that maximize expected profit and maximize the expected mean-variance trade-off assuming risk-aversion. To determine how much farmers' input choices are made to reduce risk, we then quantify differences in the expected riskiness of inputs by district. RESULTS AND CONCLUSIONS: We find approximately one-quarter of the yield gap can be explained by risk-reducing behavior, albeit with a substantial geographic variation. Given this finding, under present conditions, we expect that the average maximum yield that farmers can obtain without increasing risk is 6.75 t/ha compared to a potential profit-maximizing level of 8.84 t/ha. SIGNIFICANCE: The risk-related yield gap is only expected to increase with weather extremes driven by climate change. Promoting “one-size-fits all” solutions to closing the yield gap could underestimate the effect of risk mitigation on agricultural production while increasing farmers.CONTEXTO: El África subsahariana (ASS) tiene el potencial climático y biofísico para aumentar los cultivos que necesita para satisfacer la creciente demanda de alimentos; sin embargo, la productividad agrícola sigue siendo baja. Si bien los rendimientos potenciales del maíz en Zambia son de 9 t por hectárea (t/ha), el agricultor promedio produce sólo 1-2. OBJETIVO: Evaluamos la contribución de las respuestas al riesgo climático a esa brecha descomponiendo la brecha de rendimiento del maíz en Zambia. Si bien sabemos que las semillas y los fertilizantes mejorados pueden aumentar el rendimiento y las ganancias, también pueden aumentar la variación del rendimiento en diferentes condiciones climáticas, lo que reduce su adopción. MÉTODO: Utilizamos un enfoque novedoso que combina modelos de cultivos y análisis estadístico de datos de encuestas para obtener los componentes de la brecha de rendimiento en Zambia impulsados por el costo y el riesgo de los insumos. Utilizamos un modelo de cultivo para simular los efectos marginales a nivel de distrito de la elección de la madurez de las semillas y los fertilizantes sobre la media y la varianza del rendimiento y la ganancia esperados bajo resultados en cualquier condición climática para cada distrito durante los últimos 30 años. Comparamos los niveles de insumos que maximizan el rendimiento esperado con aquellos que maximizan el beneficio esperado y maximizan la compensación esperada entre media y varianza suponiendo aversión al riesgo. Para determinar en qué medida los agricultores eligen insumos para reducir el riesgo, luego cuantificamos las diferencias en el riesgo esperado de los insumos por distrito. RESULTADOS Y CONCLUSIONES: Encontramos que aproximadamente una cuarta parte de la brecha de rendimiento puede explicarse por un comportamiento de reducción de riesgos, aunque con una variación geográfica sustancial. Dado este hallazgo, en las condiciones actuales, esperamos que el rendimiento máximo promedio que los agricultores pueden obtener sin aumentar el riesgo sea de 6,75 t/ha en comparación con un nivel potencial de maximización de ganancias de 8,84 t/ha. SIGNIFICADO: Sólo se espera que la brecha de rendimiento relacionada con el riesgo aumente con los extremos climáticos impulsados por el cambio climático. Promover soluciones únicas para cerrar la brecha de rendimiento podría subestimar el efecto de la mitigación de riesgos en la producción agrícola y al mismo tiempo aumentar los agricultores.Centro de Investigación en Economía y ProspectivaFil: Gatti, Nicolás. Instituto Nacional de Tecnología Agropecuaria (INTA). Centro de Investigación en Economía y Prospectiva (CIEP); ArgentinaFil: Gatti, Nicolás. Universidad del Centro de Estudios Macroeconómicos de Argentina (UCEMA); ArgentinaFil: Cecil, Michael. Clark University. Department of Geography; Estados UnidosFil: Baylis, Kathy. University of California Santa Barbara. Department of Geography; Estados UnidosFil: Estes, Lyndon. Clark University. Department of Geography; Estados UnidosFil: Blekking, Jordan. Indiana University. Bloomington Department of Geography; Estados UnidosFil: Heckelei, Thomas. Universitaet Bonn. Institute for Food and Resource Economics; AlemaniaFil: Vergopolan, Noemi. Princeton University. Atmospheric and Oceanic Sciences Program; Estados UnidosFi: Evans, Tom. University of Arizona. School of Geography, Development & Environment; Estados Unido

Is closing the agricultural yield gap a risky endeavor?

CONTEXT: Sub-Saharan Africa (SSA) has the climatic and biophysical potential to grow the crops it needs to meet rapidly growing food demand; however, agricultural productivity remains low. While potential maize yields in Zambia are 9 t per hectare (t/ha), the average farmer produces only 1–2. OBJECTIVE: We evaluate the contribution of responses to weather risk to that gap by decomposing the yield gap in maize in Zambia. While we know that improved seed and fertilizer can expand yield and profit, they may also increase the variance of yield under different weather outcomes, reducing their adoption. METHODS: We use a novel approach combining crop modeling and statistical analysis of survey data to obtain the yield gap components in Zambia driven by input cost and input risk. We use a crop model to simulate district-level marginal effects of fertilizer and seed maturity choice on the mean and variance of expected yield and profit under all-weather outcomes for each district for the past 30 years. We compare input levels that maximize expected yield to those that maximize expected profit and maximize the expected mean-variance trade-off assuming risk-aversion. To determine how much farmers\u27 input choices are made to reduce risk, we then quantify differences in the expected riskiness of inputs by district. RESULTS AND CONCLUSIONS: We find approximately one-quarter of the yield gap can be explained by risk-reducing behavior, albeit with a substantial geographic variation. Given this finding, under present conditions, we expect that the average maximum yield that farmers can obtain without increasing risk is 6.75 t/ha compared to a potential profit-maximizing level of 8.84 t/ha. SIGNIFICANCE: The risk-related yield gap is only expected to increase with weather extremes driven by climate change. Promoting “one-size-fits all” solutions to closing the yield gap could underestimate the effect of risk mitigation on agricultural production while increasing farmers\u27 risk exposure. © 2023 The Author

A global near-real-time soil moisture index monitor for food security using integrated SMOS and SMAP

Soil Moisture (SM) is a direct measure of agricultural drought. While there are several global SM indices, none of them directly use SM observations in a near-real-time capacity and as an operational tool. This paper presents a near-real-time global SM index monitor based on integrated SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture and Ocean Salinity) remote sensing data. We make use of the short period (2015–2018) of SMAP datasets in combination with two approaches—Cumulative Distribution Function Mapping (CDFM) and Bayesian conditional process—and integrate them with SMOS data in a way that SMOS data is consistent with SMAP. The integrated SMOS and SMAP (SMOS/SMAP) has an increased global revisit frequency and a period of record from 2010 to the present. A four-parameter Beta distribution was fitted to the SMOS/SMAP dataset for each calendar month of each grid cell at ~36 km resolution for the period from 2010 to 2018. We used an asymptotic method that guarantees the values of the bounding parameters of the Beta distribution will envelop both the smallest and largest observed values. The Kolmogorov-Smirnov (KS) test showed that more grids globally will pass if the integrated dataset is from the Bayesian conditional approach. A daily global SM index map is generated and posted online based on translating each grid's integrated SM value for that day to a corresponding probability percentile relevant to the particular calendar month from 2010 to 2018. For validation, we use the Canadian Prairies Ecozone (CPE). We compare the integrated SM with the SMAP core validation and RISMA sites from ISMN, compare our indices with other models (VIC, ESA's CCI SM v04.4 integrated satellite data, and SPI-1), and make a two-by-two comparison of candidate indices using heat maps and summary CDF statistics. Furthermore, we visually compare our global SM-based index maps with those produced by other organizations. Our Global SM Index Monitor (GSMIM) performed, in many tests, similarly to the CCI's product SM index but with the advantage of being a near-real-time tool, which has applications for identifying evolving drought for food security conditions, insurance, policymaking, and crop planning especially for the remote parts of the globe

Cognitive Biases about Climate Variability in Smallholder Farming Systems in Zambia

Given the varying manifestations of climate change over time and the influence of climate perceptions on adaptation, it is important to understand whether farmer perceptions match patterns of environmental change from observational data. We use a combination of social and environmental data to understand farmer perceptions related to rainy season onset. Household surveys were conducted with 1171 farmers across Zambia at the end of the 2015/16 growing season eliciting their perceptions of historic changes in rainy season onset and their heuristics about when rain onset occurs. We compare farmers' perceptions with satellite-gauge-derived rainfall data from the Climate Hazards Group Infrared Precipitation with Station dataset and hyper-resolution soil moisture estimates from the HydroBlocks land surface model. We find evidence of a cognitive bias, where farmers perceive the rains to be arriving later, although the physical data do not wholly support this. We also find that farmers' heuristics about rainy season onset influence maize planting dates, a key determinant of maize yield and food security in sub-Saharan Africa. Our findings suggest that policy makers should focus more on current climate variability than future climate change.National Science Foundation [SES-1360463, BCS-1115009, BCS-1026776]6 month embargo; published online: 29 March 2019This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling

Abstract. We undertook a comprehensive evaluation of 22 gridded (quasi-)global (sub-)daily precipitation (P) datasets for the period 2000–2016. Thirteen non-gauge-corrected P datasets were evaluated using daily P gauge observations from 76 086 gauges worldwide. Another nine gauge-corrected datasets were evaluated using hydrological modeling, by calibrating the HBV conceptual model against streamflow records for each of 9053 small to medium-sized ( <  50 000 km2) catchments worldwide, and comparing the resulting performance. Marked differences in spatio-temporal patterns and accuracy were found among the datasets. Among the uncorrected P datasets, the satellite- and reanalysis-based MSWEP-ng V1.2 and V2.0 datasets generally showed the best temporal correlations with the gauge observations, followed by the reanalyses (ERA-Interim, JRA-55, and NCEP-CFSR) and the satellite- and reanalysis-based CHIRP V2.0 dataset, the estimates based primarily on passive microwave remote sensing of rainfall (CMORPH V1.0, GSMaP V5/6, and TMPA 3B42RT V7) or near-surface soil moisture (SM2RAIN-ASCAT), and finally, estimates based primarily on thermal infrared imagery (GridSat V1.0, PERSIANN, and PERSIANN-CCS). Two of the three reanalyses (ERA-Interim and JRA-55) unexpectedly obtained lower trend errors than the satellite datasets. Among the corrected P datasets, the ones directly incorporating daily gauge data (CPC Unified, and MSWEP V1.2 and V2.0) generally provided the best calibration scores, although the good performance of the fully gauge-based CPC Unified is unlikely to translate to sparsely or ungauged regions. Next best results were obtained with P estimates directly incorporating temporally coarser gauge data (CHIRPS V2.0, GPCP-1DD V1.2, TMPA 3B42 V7, and WFDEI-CRU), which in turn outperformed the one indirectly incorporating gauge data through another multi-source dataset (PERSIANN-CDR V1R1). Our results highlight large differences in estimation accuracy, and hence the importance of P dataset selection in both research and operational applications. The good performance of MSWEP emphasizes that careful data merging can exploit the complementary strengths of gauge-, satellite-, and reanalysis-based P estimates

Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors

Information about the spatiotemporal variability of soil moisture is critical for many purposes, including monitoring of hydrologic extremes, irrigation scheduling, and prediction of agricultural yields. We evaluated the temporal dynamics of 18 state-of-the-art (quasi-)global near-surface soil moisture products, including six based on satellite retrievals, six based on models without satellite data assimilation (referred to hereafter as "open-loop" models), and six based on models that assimilate satellite soil moisture or brightness temperature data. Seven of the products are introduced for the first time in this study: one multi-sensor merged satellite product called MeMo (Merged soil Moisture) and six estimates from the HBV (Hydrologiska Byrans Vattenbalansavdelning) model with three precipitation inputs (ERA5, IMERG, and MSWEP) with and without assimilation of SMAPL3E satellite retrievals, respectively. As reference, we used in situ soil moisture measurements between 2015 and 2019 at 5 cm depth from 826 sensors, located primarily in the USA and Europe. The 3-hourly Pearson correlation (R) was chosen as the primary performance metric. We found that application of the Soil Wetness Index (SWI) smoothing filter resulted in improved performance for all satellite products. The best-to-worst performance ranking of the four single-sensor satellite products was SMAPL3E(SWI), SMOSSWI, AMSR2(SWI), and ASCAT(SWI), with the L-band-based SMAPL3ESWI (median R of 0.72) outperforming the others at 50% of the sites. Among the two multi-sensor satellite products (MeMo and ESA-CCISWI), MeMo performed better on average (median R of 0.72 versus 0.67), probably due to the inclusion of SMAPL3ESWI. The best-to-worst performance ranking of the six openloop models was HBV-MSWEP, HBV-ERA5, ERA5-Land, HBV-IMERG, VIC-PGF, and GLDAS-Noah. This ranking largely reflects the quality of the precipitation forcing. HBV-MSWEP (median R of 0.78) performed best not just among the open-loop models but among all products. The calibration of HBV improved the median R by C0 :12 on average compared to random parameters, highlighting the importance of model calibration. The best-to-worst performance ranking of the six models with satellite data assimilation was HBV-MSWEP+SMAPL3E, HBV-ERA5+SMAPL3E, GLEAM, SMAPL4, HBV-IMERG+SMAPL3E, and ERA5. The assimilation of SMAPL3E retrievals into HBV-IMERG improved the median R by C0:06, suggesting that data assimilation yields significant benefits at the global scale