Including predator presence in a refined model for assessing resistance of alfalfa cultivar to aphids

The aphid quantity ratio（AQR） is defined as the number of aphids on each cultivar divided by the number of aphids on all cultivars. AQR is based on the correlation between aphid populations and their host plants and is an important tool that has been utilized in evaluating Medicago sativa（alfalfa） cultivar resistance to aphids. However, assessment of alfalfa resistance to aphids can be confused by the presence of aphid predators, causing the assessment of plant resistance to aphids to be based on incorrect aphid population data. To refine the AQR and account for the effect of predators on aphid population assessments, we introduced a parameter ‘α＇, corresponding to the predator quantity ratio, and used αAQR as the ratio to quantify aphid populations. Populations of both aphids（4 species） and their predators（12 species） occurring in 28 M. sativa cultivars were sampled over two years at a research station near Cangzhou, Hebei Province, China. Results showed that the most suitable evaluation period was from May to June, as the aphid population was stable during this period. Compared with the AQR method, the predator population numbers based on the αAQR had a significant inverse relationship with aphid population numbers and the 28 cultivars were clustered into three classes： the resistant class, tolerant class, and susceptible class. In addition, 17 cultivars were reassigned when evaluated using αAQR. All numerical values calculated by αAQR were displayed as a Gaussian distribution, which showed that the 28 cultivars could be clustered into nine groups using a median value（±SE） of 1±0.1. Hence, ongoing alfalfa breeding trials will be assessed using the αAQR to establish a robust system that includes agronomic performance parameters in order to generalize the new method for further studies.

Improving a method for evaluating alfalfa cultivar resistance to thrips

The thrips quantity ratio（TQR） model is an important tool for evaluating crop resistance to thrips based on the correlation between thrips quantities and cultivars. Unfortunately, it is inaccurate, and the results appear significantly inconsistent when analysing the same cultivars in the same field study. To improve this model, we first studied the resistance of 28 alfalfa cultivars to thrips in Cangzhou, Hebei Province, north China. The results showed that the most suitable evaluation period was from May to June, as the thrips population was stable during this period. Second, we found that the natural enemy population was significantly positively correlated with the thrips population density（R=0.7275, P〈0.0001）, which might influence resistance estimation. Hence, we introduced a parameter ‘α＇, corresponding to the natural enemy quantity ratio, to eliminate the effect of the natural enemy using ＂αTQR＂. Using the improved method, 28 cultivars were clustered into three classes： the resistant class, sensitive class, and median class. All numerical values were calculated for αTQR displayed as a Gaussian distribution. This information showed that all data should be divided into nine groups using a median value of 1±0.1 with an equal difference of 0.1. Based on the new standard cultivars, Gongnong 1, Alfaking, Cangzhou and Algonquin were classified as highly resistant cultivars; Zhongmu 3, Gongnong 2, Zhongmu 1 and Zhongmu 2 were classified in the resistant group; Queen was classified in the moderately resistant group; Derby, WL354HQ, KRIMA, Apex, 53 HR, SARDI 5 and Farmers Treasure were classified in the median class; WL319HQ, WL343HQ and Sitel were classified as the low sensitive group; WL440 HQ and SARDI7 as the moderately sensitive group; WL168HQ and Sanditi as the sensitive group; and SARDI 10, WL363HQ, FD4, WL323 and SOCA as the highly sensitive group.

Simultaneous settings of order quantity,wholesale price,production run length,process mean,and warranty period

In this article,the authors propose a modified version of S.L.Chen and Liu’s model with a two-stage production system.Assume that the retailer’s order quantity is concerned with the manufacturer’s selling price and the warranty period of product.The used cost of the customer is measured under the Taguchi’s quadratic quality loss function and concluded in the retailer’s profit function.The quality of the lot for the manufacturer is determined by adopting a two-stage single sampling rectifying inspection plan.The modified economic manufacturing quantity(EMQ)model is addressed in formulating the manufacturer’s expected profit.The retailer’s order quantity,manufacturer’s wholesale price,production run length,process mean,and warranty period of product will be jointly determined by maximizing the total expected profit of the supply chain system including the manufacturer and the retailer.Finally,the quality investment policy is introduced to illustrate the profit improvement for the supply chain system.

EMISSION ABATEMENT FOR PRODUCTION SYSTEM ACROSS LOCATIONS WITH HETEROGENEOUS EMISSION PROFLES

This paper investigates a production system in which the desired system state is a reduction of emissions and the controlling action plan is altering carbon price. From the operations perspective, we develop a model to study how increasing carbon emission costs may affect the joint production and location decisions for a manufacturer across different locations. Specifically, our model incorporates economies of scale and explicitly links product demand, production costs and carbon emission levels to location decisions. The firm＇s decisions in production batch size and locations are then optimised. The system state of emission are analysed under different carbon prices. Finally we check the alignment of objectives in costs and emissions for the system. The results show that for a production system with economies of scale, the production allocation is transformed to a location choice problem after optimising the costs. Raising the carbon price reduces the carbon emissions but may not be able to induce the production to be placed in an emission efficient place. We propose a hybrid policy combining carbon price and free emission allowance to fully align the cost efficiency and emission efficiency and characterise the link between the emission target and the carbon price.