Current Issue : October - December Volume : 2016 Issue Number : 4 Articles : 6 Articles
The postharvest quality of vegetable crops from conventional and organic production\nsystems depends on pre-harvest factors such as variety genetic potential, fertilization, and irrigation.\nThe five principles of plant nutrition (plants absorb ions, not fertilizers; Leibeigââ?¬â?¢s law of the\nminimum; nutrient application requires a source, a rate, a placement and a time of application;\nno correlation exists between total nutrient presence in the soil and availability; and plant nutrient\nconcentration and yield are related) must be followed throughout the crop growth cycle. In certified\norganic production in the United States, cover crops, manure and composts may be used together\nwith Organic Materials Review Instituteââ?¬â??approved fertilizer products. A fertilization program\nusually includes (1) soil sampling and understanding the recommendation; (2) adjusting pH if\nnecessary; (3) applying preplant fertilizer and developing a schedule for sidedressing or fertigation;\n(4) using foliar fertilization; (5) monitoring plant nutrient status; and (5) keeping fertilization records.\nThe components of an irrigation schedule are (1) determining a target irrigation volume based\non reference evapotranspiration and crop age; (2) adjusting this amount based on soil moisture\ncontent; (3) determining the contribution of rainfall; (4) developing a rule for splitting irrigation;\nand (5) keeping irrigation records. A poorly designed irrigation program can negate the benefits\nof a sound fertilization program. Challenges encountered in conventional and organic production\ninclude predicting nutrient release rates from organic materials, supplying enough N throughout\nthe cropping season, identifying rescue strategies, keeping production costs low, and meeting the\nadditional legal requirements of the food safety and best management practices programs....
Understanding the temporal and spatial variability in a crop yield is viewed as one of the key\nsteps in the implementation of precision agriculture practices. Therefore, a study on a center\npivot irrigated 23.5 ha field in Saudi Arabia was conducted to assess the variability in alfalfa\nyield using Landsat-8 imagery and a hay yield monitor data. In addition, the study was\ndesigned to also explore the potential of predicting the alfalfa yield using vegetation indices.\nA calibrated yield monitor mounted on a large rectangular hay baler was used to measure\nthe actual alfalfa yield for four alfalfa harvests performed in the period from October 2013 to\nMay 2014. A total of 18 Landsat-8 images, representing different crop growth stages, were\nused to derive different vegetation indices (VIs). Data from the yield monitor was used to\ngenerate yield maps, which illustrated a definite spatial variation in alfalfa yield across the\nexperimental field for the four studied harvests as indicated by the high spatial correlation\nvalues (0.75 to 0.97) and the low P-values (4.7E-103 to 8.9E-27). The yield monitor-measured\nalfalfa actual yield was compared to the predicted yield form the Vis. Results of the\nstudy showed that there was a correlation between actual and predicted yield. The highest\ncorrelations were observed between actual yield and the predicted using NIR reflectance,\nSAVI and NDVI with maximum correlation coefficients of 0.69, 0.68 and 0.63, respectively....
Transportation of raw milk from village to dairy requires lot of care, as there is no chilling unit at village collecting centre. Freshly collected raw milk at village co-operative societies is transported to chilling unit and the route is specified by the milk federation. In such a chilling unit, milk collected from many such routes is chilled and then transported to secondary processing unit once in a day. To maintain quality, the milk has to be chilled at village co-operative society level. The temperature of milk at the time of milking is about 37°C. It has to be quickly chilled to 4°C to check the growth of microorganisms and to maintain its quality as per international standards. With most farmers owning less than 2/3 animals, it is wellnigh impossible for these small dairy owners to have resources to invest in storing the milk at required 4°C before supplying to consumers or milk collection centres. An innovative, better solution for this problem is to use mobile bulk milk chiller while transport of raw milk. The mobile bulk milk chiller will consist of Jacketed Type Evaporator (Dimple Jacket), R410A Refrigerant with a proper layout, storage facility for milk cans and handling equipment which will reduce the effort and time required loading and unloading the milk cans....
Non-destructive, accurate, user-friendly and low-cost approaches to determining crop\nleaf area (LA) are a key tool in many agronomic and physiological studies, as well as in current\nagricultural management. Although there are models that estimate cut rose LA in the literature, they\nare generally designed for a specific stage of the crop cycle, usually harvest. This study aimed to\nestimate the LA of cut ââ?¬Å?Red Naomiââ?¬Â rose stems in several phenological phases using morphological\ndescriptors and allometric measurements derived from image processing. A statistical model was\ndeveloped based on the ââ?¬Å?multiple stepwise regressionââ?¬Â technique and considered the stem height,\nthe number of stem leaves, and the stage of the flower bud. The model, based on 26 stems (232 leaves)\ncollected at different developmental stages, explained 95% of the LA variance (R2 = 0.95, n = 26,\np < 0.0001). The mean relative difference between the observed and the estimated LA was 8.2%.\nThe methodology had a high accuracy and precision in the estimation of LA during crop development.\nIt can save time, effort, and resources in determining cut rose stem LA, enhancing its application in\nresearch and production contexts....
There is strong advocacy for agricultural machinery appropriate for smallholder farmers in South Asia.\nSuch ââ?¬Ë?scale-appropriateââ?¬â?¢ machinery can increase returns to land and labour, although the still substantial\ncapital investment required can preclude smallholder ownership. Increasing machinery demand has\nresulted in relatively well-developed markets for rental services for tillage, irrigation, and post-harvest\noperations. Many smallholders thereby access agricultural machinery that may have otherwise been\ncost prohibitive to purchase through fee-for-service arrangements, though opportunity for expansion\nremains. To more effectively facilitate the development and investment in scale-appropriate machinery,\nthere is a need to better understand the factors associated with agricultural machinery purchases and\nservice provision. This paper first reviews Bangladeshââ?¬â?¢s historical policy environment that facilitated the\ndevelopment of agricultural machinery markets. It then uses recent Bangladesh census data from\n814,058 farm households to identify variables associated with the adoption of the most common\nsmallholder agricultural machinery - irrigation pumps, threshers, and power tillers (mainly driven by\ntwo-wheel tractors). Multinomial probit model results indicate that machinery ownership is positively\nassociated with household assets, credit availability, electrification, and road density. These findings\nsuggest that donors and policy makers should focus not only on short-term projects to boost machinery\nadoption. Rather, sustained emphasis on improving physical and civil infrastructure and services, as well\nas assuring credit availability, is also necessary to create an enabling environment in which the adoption\nof scale-appropriate farm machinery is most likely....
Maize is one of the major food crops in China. Traditionally, field operations are done by\nmanual labor, where the farmers are threatened by the harsh environment and pesticides. On the\nother hand, it is difficult for large machinery to maneuver in the field due to limited space, particularly\nin the middle and late growth stage of maize. Unmanned, compact agricultural machines, therefore,\nare ideal for such field work. This paper describes a method of monocular visual recognition to\nnavigate small vehicles between narrow crop rows. Edge detection and noise elimination were used\nfor image segmentation to extract the stalks in the image. The stalk coordinates define passable\nboundaries, and a simplified radial basis function (RBF)-based algorithm was adapted for path\nplanning to improve the fault tolerance of stalk coordinate extraction. The average image processing\ntime, including network latency, is 220 ms. The average time consumption for path planning is 30 ms.\nThe fast processing ensures a top speed of 2 m/s for our prototype vehicle. When operating at the\nnormal speed (0.7 m/s), the rate of collision with stalks is under 6.4%. Additional simulations and\nfield tests further proved the feasibility and fault tolerance of our method....
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