Legumes are an excellent source of proteins and health‐promoting phytochemicals. Recognizing their importance in human nutrition and sustainable agricultural production, significant efforts are currently being made to accelerate genetic gain related to yield, stress resilience, and nutritional quality. Recent increases in genomic resources for multiple legume crops have laid a solid foundation for application of transformative breeding technologies such as genomic selection and genome editing for crop improvement. In this review, we focus on the recent plant‐specific advances in CRISPR/Cas9‐based gene editing technology and discuss the challenges and opportunities to harnessing this innovative technology for targeted improvement of traits in legume crops. Gene‐editing methods have been successfully established for soybean, cowpea, chickpea, and model legumes such as Medicago truncatula and Lotus japonicus. However, the recalcitrance of other legumes to in vitro gene transfer and regeneration has posed a serious challenge to application of gene editing. We discuss various modifications to in vitro culture methods, in terms of the choice of explant, media composition, and DNA delivery and gene‐editing detection methods that can potentially improve the rate of transformation and regeneration of whole plant in legume crops. Although gene‐editing technology can bring enormous benefits to legume breeding, regulatory hurdles are a cause for serious concern. We compare the regulatory environments existing in the European Union and the United States of America. A favorable regulatory framework and public acceptance are important factors in realizing CRISPR's potential benefits to global food security.

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Pulse crops (PC) have become an essential part of cropping systems in the northern Great Plains. Despite agronomic benefits of rotating pulses with cereal crops, knowledge of the economics of PC frequency and sequence in a rotation is limited. A field study was conducted from 2010 to 2014 at three locations in western Canada to evaluate the effects of rotating a cereal crop with a range of PC at different frequencies and sequences on the economic returns and risk of both the entire rotation and each individual crop. Crops in rotation included spring wheat (Triticum aestivum L.) (W), field pea (Pisum sativum L.) (P), chickpea (Cicer arietinum L.) (C), lentil (Lens culinaris Medik) (L), and Oriental mustard (Brassica juncea L.) (M). Thirteen 4-yr-cycle crop rotation treatments, along with a continuous wheat treatment as a baseline, were included. Treatments were arranged in a randomized complete block design with four replicates at each site-year. The net revenue (NR) was defined as the income remaining after paying all monetary, land and ownership, and labor costs. Crop rotation had a significant effect on average annual NR. The most profitable rotations were L-L-L-W and P-M-L-W. These rotations provided CAN$330 and $235 yr -1 ha -1 higher NR, respectively, than the baseline, and were most preferable by risk-averse producers. The economic ranking of the rotations remained the same with different crop price scenarios. Preceding PC also had a positive impact on the succeeding wheat crop NR.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

a large number of entries. An incomplete block design such as a lattice or an ␣-design can have smaller blocks Several spatial analyses of neighboring plots are now available for but spatial heterogeneity may persist in small blocks.improving the precision of variety trials. The objective of this study Evidently, such design-based control of the error variawas to evaluate the efficiency of three commonly used spatial analyses, a nearest neighbor adjustment (NNA), a least squares smoothing tion alone may not be sufficient to remove all spatial (LSS), and a first-order autoregressive model (AR1), in removing trends in the variety trials. field trends from 157 field pea (Pisum sativum L.) variety trials tested Different model-based analyses that exploit the inforin different growing zones across Alberta, Canada, during 1997 to mation on plot positions have been developed and ap-2001. All trials were conducted with a randomized complete block plied to estimate and correct for spatial variation within (RCB) design with three or four replications. A complete replication and among blocks. These spatial analyses include NNA (block) was planted in a single field tier. Yield data from each of the (Bartlett, 1978; Wilkinson et al., 1983; Zimmerman and 157 trials were subject to the conventional RCB analysis and the Harville, 1991), LSS (Green et al., 1985), and modeling three spatial analyses. The LSS, NNA, and AR1 analyses removed of spatial autocorrelation such as the AR1 model (Gleean average of 22, 16, and 7% residual variation compared with the son and Cullis, 1987; Gilmour et al., 1997). Efficiency RCB analysis, respectively, but the amount of removal by the three analyses varied considerably among the trials. Each spatial analysisThe 157 field pea variety trials used for this study were Food, and Rural Development,

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