Knowledge Based Inter-Firm Collaborations: A Theoretical Review

This paper seeks to study the theoretical and empirical theories of knowledge based strategic inter-firm collaboration between firms.  Strategic alliances are innovative and interesting forms of relationships between organizations and organizations create alliances in their quest to compete against fast and nimble competitors. This paper provided some evidence to suggest that companies relying on strategic alliances are more profitable than their vertically integrated counterparts. In effect, strategic alliances provide an effective means to improve both the economies of scale and scope offered by traditional modes of organization. Consequently, there has been a dramatic increase in the number of strategic alliances. In the last two decades, alliances have become a central part of most companies’ competitive and growth strategies. Alliances help firms strengthen their competitive position by enhancing market power, increasing efficiencies, accessing new or critical resources or capabilities and entering new markets. By the turn of this century many of the world’s largest companies had over 20% of their assets, and over 30% of their annual research expenditures, tied up in such relationships. The review of related literature brought out some theories and concepts which were related to my study

Prevalence of cystic echinococcosis in livestock slaughtered in selected abattoirs of Laikipia West Sub-County, Kenya

Background: Cystic echinococcosis (CE) is a neglected, emerging and reemerging zoonotic disease caused by the larval stage of the dog tapeworm of the genus Echinococcus. It causes great public health and economic concerns wherever it occurs. CE is endemic in Kenya and most studies done in the country focused on two loci; Turkana and Maasai communities. The prevalence of CE has not been documented in Laikipia County which is located between two CE hot spot areas in Kenya.Objectives: To estimate the prevalence of CE in livestock slaughtered in abattoirs of Laikipia west Sub CountyDesign: A cross-sectional studySetting: Three selected abattoirs in Laikipia west Sub CountySubjects: All cattle, sheep and goats slaughtered in the selected abattoirs between October and December, 2015.Main outcome measures: Species, sex, CE status, and originResults: A total of 339 cattle, 1396 sheep and 478 goats were examined for presence of hydatid cysts in both the thoracic and abdominal cavities during postmortem meat inspection. Overall prevalence was 3.3% and individual species’ prevalence was 11.8%, 1.5% and 2.3% in cattle, sheep and goats respectively. Most (99.1 %) slaughter animals originated from the study area. Forty-three percent (31/72) of the CE positive animals had fertile cysts and 87.1% of them originated from the study area.Conclusion: The results show a significantly higher prevalence of CE in cattle with most slaughter animals and those with fertile cysts originating from the study area. Possible implications for public health and the livestock economy require immediate control measures

Mineralisation Patterns of Selected Organic Materials

Abstract Thirty-two standard organic materials were mixed with a sandy soil (at 40% field capacity) at a rate equivalent to 5 t ha -1 and incubated aerobically under controlled conditions at 25°C for 28 days. Sampling for mineral N determination and CO 2 evolution was conducted at 3, 7, 14 and 28 days. Released CO 2 was related to resource quality, with those materials high in N, low in lignin and low in polyphenol concentrations releasing higher percentages of their initial C. In vitro dry matter digestibility (IVDMD) was linearly correlated with carbon breakdown, with correlation coefficients of 0.91, 0.92, 0.92 and 0.84 for sampling times of 3, 7, 14 and 28 days, respectively. Initial N concentration was significantly positively correlated with C breakdown at all sampling times. Nitrogen mineralisation was influenced mainly by initial N concentration of the materials, with materials having at least 2.3% N releasing N throughout the 28-day period

Decomposition and mineralization of organic residues predicted using near infrared spectroscopy

Characterization of decomposition characteristics is important for sound management of organic residues for both soils and livestock, but routine residue quality analysis is hindered by slow and costly laboratory methods. This study tested the accuracy and repeatability of near-infrared spectroscopy (NIR) for direct prediction of in vitro dry matter digestibility (IVDMD) and C and N mineralization for a diverse range of organic materials (mostly crop and tree residues) of varying quality (n = 32). The residue samples were aerobically incubated in a sandy soil and amounts of C and N mineralized determined after 28 days. IVDMD and quality attributes were determined using wet chemistry methods. Repeatability was higher with NIR than the original wet chemistry methods: on average NIR halved the measurement standard deviation. NIR predicted IVDMD and C and N mineralization more accurately than models based on wet chemical analysis of residue quality attributes: reduction in root mean square error of prediction with NIR, compared with using quality attributes, was IVDMD, 6%; C mineralization after 28 days, 8%; and N mineralization after 28 days, 8%. Cross-validated r 2 values for measured wet chemistry vs. NIR-predicted values were: IVDMD, 0.88; C mineralization, 0.82; and N mineralization, 0.87. Direct prediction of decomposition and mineralization from NIR is faster, more accurate and more repeatable than prediction from residue quality attributes determined using wet chemistry. Further research should be directed towards establishment of diverse NIR calibration libraries under controlled conditions and direct calibration of soil quality, crop and livestock responses in the field to NIR characteristics of residues

Rapid characterization of organic resource quality for soil and livestock management in tropical agroecosystems using near-infrared spectroscopy

Organic resources constitute a major source of nutrient inputs to both soils and livestock in smallholder tropical production systems. Determination of resource quality attributes using current laboratory methods is both timely and costly. This study tested visible and near-infrared (wavelengths from 0.35 2.50 ?m) reflectance spectroscopy (NIRS) for rapid prediction of quality attributes for a diverse range of organic resources. A spectral library was constructed for 319 samples of oven-dried, ground plant material originating from green leaf (186 samples), litter (33), root (25), and stem (21) samples from 83 species including tropical crops and trees used for agroforestry and manure samples (39). Organic resource attributes were calibrated to first-derivative reflectance using regression trees with stochastic gradient boosting, and screening tests were developed for separating various organic resource quality classes using classification trees. Validation r 2 values for actual vs. predicted values using a 25% holdout sample were 0.91 for N, 0.90 for total soluble polyphenol, and 0.64 for lignin concentration. Screening tests gave validation prediction efficiencies of 96% for detecting samples with high N concentration, 91% for low total soluble polyphenol, and 86% for low lignin concentration. The spectral screening tests were robust even at small (n = 48) calibrations sample sizes. Screening tests for detecting samples with low or high levels of P, K, Ca, and Mg gave prediction efficiencies of 74 to 92%. Near-infrared reflectance spectroscopy can be used to rapidly screen organic resource quality. Global spectral calibration libraries should be established for a range of resource quality attributes

Organic inputs for soil fertility management in tropical agroecosystems: Application of an organic resource database

Organic resources play a critical role in both short-term nutrient availability and longer-term maintenance of soil organic matter in most smaller holder farming systems in the tropics. Despite this importance, there is little predictive understanding for the management of organic inputs in tropical agroecosystems. In this paper, an organic resource database (ORD) is introduced that contains information on organic resource quality parameters including macronutrient, lignin and polyphenol contents of fresh leaves, litter, stems and/or roots from almost 300 species found in tropical agroecosystems. Data on the soil and climate from where the material was collected are also included, as are decomposition and nutrient release rates of many of the organic inputs. Examples of uses of ORD are provided in the paper: (1) nutrient contents (including median values and ranges) and other resource quality parameters of farmyard manure and crop residues are compared to that of alternative nutrient sources such as different plant parts and plant types; (2) nutrient stocks found in farm boundary hedges are estimated and evaluated as a source of nutrients for soil fertility management; (3) hypotheses regarding the indices and critical values of N, lignin, and polyphenol contents for predicting N release rates are tested; (4) organic materials for soil fertility management experiments are selected. This database, when coupled with models and decision support tools, will help advance organic matter management for soil fertility improvement from an empirical to a predictive practice