Cultivo do cará.

Introdução; Clima; Solos; Sistema de plantio; Propagação; Época de plantio; Variedades; Adubação; Cobertura morta; Tutoramento; Doenças; Pragas; Colheita.

Vector Associative Maps: Unsupervised Real-time Error-based Learning and Control of Movement Trajectories

This article describes neural network models for adaptive control of arm movement trajectories during visually guided reaching and, more generally, a framework for unsupervised real-time error-based learning. The models clarify how a child, or untrained robot, can learn to reach for objects that it sees. Piaget has provided basic insights with his concept of a circular reaction: As an infant makes internally generated movements of its hand, the eyes automatically follow this motion. A transformation is learned between the visual representation of hand position and the motor representation of hand position. Learning of this transformation eventually enables the child to accurately reach for visually detected targets. Grossberg and Kuperstein have shown how the eye movement system can use visual error signals to correct movement parameters via cerebellar learning. Here it is shown how endogenously generated arm movements lead to adaptive tuning of arm control parameters. These movements also activate the target position representations that are used to learn the visuo-motor transformation that controls visually guided reaching. The AVITE model presented here is an adaptive neural circuit based on the Vector Integration to Endpoint (VITE) model for arm and speech trajectory generation of Bullock and Grossberg. In the VITE model, a Target Position Command (TPC) represents the location of the desired target. The Present Position Command (PPC) encodes the present hand-arm configuration. The Difference Vector (DV) population continuously.computes the difference between the PPC and the TPC. A speed-controlling GO signal multiplies DV output. The PPC integrates the (DV)·(GO) product and generates an outflow command to the arm. Integration at the PPC continues at a rate dependent on GO signal size until the DV reaches zero, at which time the PPC equals the TPC. The AVITE model explains how self-consistent TPC and PPC coordinates are autonomously generated and learned. Learning of AVITE parameters is regulated by activation of a self-regulating Endogenous Random Generator (ERG) of training vectors. Each vector is integrated at the PPC, giving rise to a movement command. The generation of each vector induces a complementary postural phase during which ERG output stops and learning occurs. Then a new vector is generated and the cycle is repeated. This cyclic, biphasic behavior is controlled by a specialized gated dipole circuit. ERG output autonomously stops in such a way that, across trials, a broad sample of workspace target positions is generated. When the ERG shuts off, a modulator gate opens, copying the PPC into the TPC. Learning of a transformation from TPC to PPC occurs using the DV as an error signal that is zeroed due to learning. This learning scheme is called a Vector Associative Map, or VAM. The VAM model is a general-purpose device for autonomous real-time error-based learning and performance of associative maps. The DV stage serves the dual function of reading out new TPCs during performance and reading in new adaptive weights during learning, without a disruption of real-time operation. YAMs thus provide an on-line unsupervised alternative to the off-line properties of supervised error-correction learning algorithms. YAMs and VAM cascades for learning motor-to-motor and spatial-to-motor maps are described. YAM models and Adaptive Resonance Theory (ART) models exhibit complementary matching, learning, and performance properties that together provide a foundation for designing a total sensory-cognitive and cognitive-motor autonomous system.

Home gardens and Dioscorea species – A case study from the climatic zones of Sri Lanka

Home gardens are considered as vital units for enhancing food security particularly in developing nations of South Asia, such as Sri Lanka. Although the yam crop Dioscorea spp. constitute a popular but still minor component in Sri Lankan home gardens, they have the potential of producing large quantities of edible material with minimal inputs. However, their real value in South Asian home gardens is not yet reported. Hence, this study was carried out to get insights into home garden characteristics, gardener demography as well as current management practices within 300 Sri Lankan home garden systems that are located along a climatic gradient. By using interviews and field observations, gardeners, who cultivated in particular Dioscorea species, were studied within 10 of the 25 administrative districts distributed in the wet, intermediate and dry climatic zone of Sri Lanka. Furthermore, current management practices of yams cultivation were analyzed on local scale and compared afterwards with management recommendations published in the year 2006 by the Department of Agriculture. Dioscorea species were found in a majority of home gardens, especially in wet and intermediate zones of Sri Lanka. D. alata was the most prominent species and was managed at a subsistence level and not as per recommendations developed by the Department of Agriculture. Our results revealed that Dioscorea alata is an essential component of Sri Lankan home gardens in rural areas and can yield substantial quantities of edible tubers with low input, especially during times of food scarcities, and has therefore the potential to enhance food security and rural development.

Duration from vine emergence to flowering suggests a long-day or rate of change of photoperiod response in white yam (Dioscorea rotundata Poir.)

The objective of this study was to quantify the effect of photoperiod on the duration from vine (shoot) emergence to flowering in white or Guinea yam (Dioscorea rotundata). The duration from vine emergence to flowering in two clonal varieties of yam (TDr 131 and TDr 99-9) was recorded at 10 different sowing dates/locations in Nigeria. Durations to flowering varied from 40 to > 88 days. Mean daily temperature and photoperiod between vine emergence and flowering varied from 25 to 27 degrees C and 13.1 to 13.4 h day(-1), respectively. Both clones had similar responses to temperature, with base and optimum temperatures of 12 and 25-27 degrees C, respectively. Thermal durations to flowering were strongly related (r(2) > 0.75-0.83) to absolute photoperiod (h) at vine emergence as well as to rate of change of photoperiod (s day(-1)) at vine emergence. The response to absolute photoperiod suggests that white yams are quantitative LDPs, flowering sooner in long than short days. Yams also flowered earlier when the rate of change of photoperiod was positive but small, or was negative. It is suggested that yams may use a combination of photoperiod and rate of change in order to fine tune flowering time. (c) 2006 Elsevier B.V. All rights reserved.

Effect of harvest date on the dormancy period of yam (Dioscorea rotundata)

Tuber dormancy enables yams to survive in the ground during the dry season and post-harvest storage. Three clones of Dioscorea rotundata were harvested after five intervals and then stored in a cooler (20.6°C) or at ambient temperature (27.8°C). The time from harvest to sprouting was shorter as harvest was delayed. The period from sowing to sprouting for each clone was similar for tubers harvested from 140 days after planting, but tubers harvested earlier took longer to sprout. The cooler temperature delayed sprouting. Tubers of two clones sprouted after only 70 days of crop growth. If the dormancy period of these young tubers can be broken, the generation time of yam crop improvement programmes could be considerably reduced.

Duration from vine emergence to flowering suggests a long-day or rate of change of photoperiod response in white yam (Dioscorea rotundata Poir.)

The objective of this study was to quantify the effect of photoperiod on the duration from vine (shoot) emergence to flowering in white or Guinea yam (Dioscorea rotundata). The duration from vine emergence to flowering in two clonal varieties of yam (TDr 131 and TDr 99-9) was recorded at 10 different sowing dates/locations in Nigeria. Durations to flowering varied from 40 to > 88 days. Mean daily temperature and photoperiod between vine emergence and flowering varied from 25 to 27 degrees C and 13.1 to 13.4 h day(-1), respectively. Both clones had similar responses to temperature, with base and optimum temperatures of 12 and 25-27 degrees C, respectively. Thermal durations to flowering were strongly related (r(2) > 0.75-0.83) to absolute photoperiod (h) at vine emergence as well as to rate of change of photoperiod (s day(-1)) at vine emergence. The response to absolute photoperiod suggests that white yams are quantitative LDPs, flowering sooner in long than short days. Yams also flowered earlier when the rate of change of photoperiod was positive but small, or was negative. It is suggested that yams may use a combination of photoperiod and rate of change in order to fine tune flowering time. (c) 2006 Elsevier B.V. All rights reserved.

Climate change impacts on crop productivity in Africa and South Asia

Climate change is a serious threat to crop productivity in regions that are already food insecure. We assessed the projected impacts of climate change on the yield of eight major crops in Africa and South Asia using a systematic review and meta-analysis of data in 52 original publications from an initial screen of 1144 studies. Here we show that the projected mean change in yield of all crops is − 8% by the 2050s in both regions. Across Africa, mean yield changes of − 17% (wheat), − 5% (maize), − 15% (sorghum) and − 10% (millet) and across South Asia of − 16% (maize) and − 11% (sorghum) were estimated. No mean change in yield was detected for rice. The limited number of studies identified for cassava, sugarcane and yams precluded any opportunity to conduct a meta-analysis for these crops. Variation about the projected mean yield change for all crops was smaller in studies that used an ensemble of > 3 climate (GCM) models. Conversely, complex simulation studies that used biophysical crop models showed the greatest variation in mean yield changes. Evidence of crop yield impact in Africa and South Asia is robust for wheat, maize, sorghum and millet, and either inconclusive, absent or contradictory for rice, cassava and sugarcane.