It was with great pleasure that I served on the editorial board of this journal for almost twenty years, gaining many insights into the developments of concepts and practices in our scientific field. Now, as I gladly hand over this editorial task to younger colleagues, I take the opportunity to present some thoughts on where we are and where we should go in animal breeding research.

The major objective of animal breeding is to generate genetic progress. Note that “progress” has a positive connotation, meaning that breeding aims at changing populations in a desired, positive direction. This is different from “selection response”, which is neutral and also can go in the wrong direction, as we often see in cases of correlated selection responses on fitness traits. An important task of animal breeding research should therefore be to enable genetic progress, although this is certainly not the only reason for research in animal breeding, which can also, for example, provide revealing insights into more general biological mechanisms.

In which research areas lies the greatest potential to further promote genetic progress in practical animal breeding? I will discuss this using two important (and rather old) concepts in breeding, namely the selection index and the breeder's equation.

The selection index (Hazel,1943, Genetics 28: 467) provides the conceptual framework for the definition of the breeding goal. The value of traits here is defined in economic terms; more specifically, a marginal utility coefficient needs to be specified for each trait. While the overall concept of the selection index has proven to be extremely powerful in practical breeding, it must be doubted that all breeding goals can be fully expressed in economic terms. By and large, nowadays' breeding goals in most livestock species are composed of two parts: roughly one half is attributed to an increase in production value, that is producing more and better milk, meat, eggs etc. and by this improving the source of production‐related income of the farmer. The other half is attributed to so‐called functional traits, which originally were defined as traits that affect the ability of an animal to be productive in the first place. Initially, these were traits related to health, fertility and production efficiency, but now are extended to traits related to animal welfare (beyond health in the medical sense) and environmental impact. These latter aspects are linked to broader societal expectations towards animal production and thus indirectly to secure markets for animal products in the longer term.

The immediate economic impact of functional traits lies in the saving of costs, for example through a reduction of expenses for vets and feed. But can the actual value of the trait “calving performance” of a dairy cow be fully appraised by a detailed account of costs saved for the vet and the drugs, working hours spent (including night‐time surcharges), reduced milk performance, increased service period and eventually the costs of the calf and/or the replacement of the cow in the case of a fatal outcome? In all traits that are related to animal health and welfare, there is an additional non‐monetary value, which consequently is hardly quantifiable in economic terms. This additional value has two aspects, which are interlinked: the suffering of the affected animals, and the societal impact. The loss of a male Holstein calf due to a difficult calving may be economically marginal, but it obviously has a serious animal welfare aspect, and society in the long term will not accept increased calf losses as a “by‐product” of an economic breeding programme, with detrimental long‐term effects on the willingness to consume animal products. A purely economic approach never will catch the full value of such traits, causing a too low weight of these traits in complex breeding goals. This in parts explains certain misdevelopments in practical breeding, such as unacceptably high piglet mortality rates in some high‐fertility breeds, or the low and frustratingly slowly increasing longevity of dairy cows.

Defining breeding goals is one of the key entrepreneurial challenges of a breeding organization, and it always has to take anticipated future production conditions, demand structures and socio‐economic context into account. Personally, I expect that in many Western countries breeding objectives will have to respond to a much greater extent to society's expectations in terms of animal welfare, but also to an increasing scepticism towards an excessive industrialization of animal production. International commitments and coordinated actions to limit global heating will also assign much greater importance to trait complexes related to nutrient efficiency, direct greenhouse gas emission and climate adaptation. In the longer term, we even should be prepared for a decline in the consumption of meat and animal products, due to their inherent “climate inefficiency” relative to plant‐based diets. A purely economic approach has its limitations here, hence alternative approaches, more in the spirit of “desired gains” indices should be revisited and considered. Such developments certainly would benefit from an intensified exchange of the animal breeding community with experts in economics, business and social sciences.

While breeding goals determine where to go, breeding programmes describe, how to get there. The conceptual core of breeding programmes is the breeders' equation, which dates back to Lush's seminal book “Animal Breeding Plans” in 1937. From this simple equation it is apparent, that animal breeding basically has three main levers to accelerate genetic progress: increased selection intensity, more accurate breeding values and shorter generation intervals. The fourth factor in the equation, usable genetic variation, plays a role mainly in maintaining the possibility of genetic progress by avoiding erosion of genetic variance through inbreeding, while the possibilities of effectively increasing genetic variation, and through this genetic progress, are limited in many cases. An additional lever primarily affecting breeding efficiency (i.e. genetic progress over breeding costs) are the economics of performance testing. In reviewing the scientific literature of recent decades, my impression (which admittedly may be subject to a personal bias) is that a disproportionate amount of research has been devoted to a single factor, namely increasing the accuracy of breeding value estimation, while the other levers were considerably under‐researched. Since the introduction of the basic BLUP concept through Henderson in the mid 1970s, there were a few significant methodological steps, like establishing animal models, random regression models and—most remarkably—the introduction of genomic breeding value estimation. In addition, we have seen extensive research on countless variations of the established evaluation methods, which often were found to have miniscule effects on the ranking of selection candidates in a practical context. This was especially so in the genomic era, where whole methodological “alphabets” were developed without ever leading to a sustainable advantage over simple and robust basic methods like GBLUP.

Other efforts were aimed at identifying single genes or QTL that could eventually help to make better genetic predictions. In the context of complex traits, this approach, originally developed in the era of “marker‐assisted selection” in the 1990s and carried over into the era of genomic selection, has not, with few exceptions, significantly improved the accuracy of breeding values that contribute to improved genetic progress. Although there are excellent arguments for conducting research aimed at better understanding the genetic basis of complex traits, directly increasing genetic progress in such traits is not the most convincing among them.

While a strong focus has been placed on research aiming at the increase of the accuracy of breeding values, the other two major levers affecting genetic progress, that is selection intensity and generation interval, have received comparatively little research attention. Selection intensity in particular is also directly linked to breeding costs, making it the most crucial factor in the profitability of breeding programmes. Both are major elements of breeding programme design and thus less technical but more organizational. This may be one of the reason why research has much less focused on such topics in the last years or even decades. Another reason for this shortcoming is a lack of suitable methodology to design and optimize modern breeding programmes, which can no longer be represented by the breeder's equation in its simple form, but are complex in nature and therefore need to be adequately designed. In the first article of this issue, we present our ideas for a unifying concept of animal breeding programmes based on a modular approach that allows a comprehensive representation of breeding programmes of any complexity. Based on this, it will be possible to evaluate and ultimately optimize breeding programmes with different objective functions.

Using more advanced methods for genetic evaluation may increase the accuracy of breeding values by a few per cent, which directly translates into an increase of the genetic progress by the same margin. I would argue that in many practical breeding programmes it will be relatively simple to achieve a similar or even higher proportional increase in selection intensity or a reduction in generation interval through a more stringent organization of the breeding process. However, we see relatively little research on innovative breeding programmes based on conceptual alternatives, such as a more nucleus type of breeding in dairy cattle, or programmes making much more systematic use of innovative reproductive technologies like semen and embryo sexing or cloning, but also of novel approaches to automated phenotyping.

This being said, we should of course do one without leaving the other, that is combine the most accurate genetic evaluation methods with the most efficient design and stringent implementation of breeding programmes to achieve breeding goals that reflect the challenges of our time.

我非常高兴地在该杂志的编辑委员会任职近 20 年，对我们科学领域的概念和实践的发展有很多见解。现在，当我很高兴地将这份编辑任务交给年轻的同事时，我借此机会提出一些关于我们在动物育种研究中的现状和应该去向的想法。

动物育种的主要目标是产生遗传进步。请注意，“进步”具有积极的含义，这意味着育种旨在朝着理想的积极方向改变种群。这与“选择反应”不同，后者是中性的，也可能走向错误的方向，正如我们在适应度特征的相关选择反应的情况下经常看到的那样。因此，动物育种研究的一项重要任务应该是促进遗传进步，尽管这当然不是动物育种研究的唯一原因，例如，动物育种研究还可以提供对更一般生物学机制的揭示性见解。

哪些研究领域最有潜力进一步推动实际动物育种的遗传进展？我将使用育种中两个重要（而且相当古老）的概念来讨论这个问题，即选择指数和育种者方程。

选择指数 (Hazel, 1943, Genetics 28: 467) 为育种目标的定义提供了概念框架。这里特征的价值是用经济术语定义的；更具体地说，需要为每个性状指定一个边际效用系数。虽然选择指数的整体概念在实际育种中被证明是极其强大的，但必须怀疑所有育种目标都可以用经济术语来充分表达。总的来说，当今大多数牲畜品种的育种目标由两部分组成：大约一半归因于产值的增加，即生产更多更好的奶、肉、蛋等，并由此改善了产值的来源。农民与生产有关的收入。另一半归因于所谓的功能特征，最初被定义为影响动物生产能力的特征。最初，这些是与健康、生育力和生产效率相关的特征，但现在扩展到与动物福利（超出医学意义上的健康）和环境影响相关的特征。后面的这些方面与更广泛的社会对动物生产的期望有关，从而间接地确保了动物产品的长期市场。

功能性状的直接经济影响在于节省成本，例如通过减少兽医和饲料费用。但奶牛“产犊性能”性状的实际价值能否通过详细说明为兽医和药物节省的成本、花费的工作时间（包括夜间附加费）、产奶性能下降、服务增加等因素得到充分评估？在发生致命后果的情况下，小牛和/或更换母牛的最终费用是多少？在与动物健康和福利相关的所有特征中，都有一个额外的非货币价值，因此很难用经济术语来量化。这种附加价值有两个相互关联的方面：受影响动物的痛苦和社会影响。由于产犊困难而损失一头雄性荷斯坦小牛在经济上可能是微不足道的，但这显然对动物福利有严重影响，从长远来看，社会不会接受将小牛损失增加作为经济育种的“副产品”计划，对消费动物产品的意愿产生不利的长期影响。纯粹的经济方法永远无法捕捉到这些性状的全部价值，导致这些性状在复杂育种目标中的权重过低。这在一定程度上解释了实际育种中的某些错误发展，例如某些高生育力品种的仔猪死亡率高得令人无法接受，或者奶牛的寿命低且令人沮丧地缓慢增加。从长远来看，社会不会接受增加的犊牛损失作为经济育种计划的“副产品”，这会对消费动物产品的意愿产生不利的长期影响。纯粹的经济方法永远无法捕捉到这些性状的全部价值，导致这些性状在复杂育种目标中的权重过低。这在一定程度上解释了实际育种中的某些错误发展，例如某些高生育力品种的仔猪死亡率高得令人无法接受，或者奶牛的寿命低且令人沮丧地缓慢增加。从长远来看，社会不会接受增加的犊牛损失作为经济育种计划的“副产品”，这会对消费动物产品的意愿产生不利的长期影响。纯粹的经济方法永远无法捕捉到这些性状的全部价值，导致这些性状在复杂育种目标中的权重过低。这在一定程度上解释了实际育种中的某些错误发展，例如某些高生育力品种的仔猪死亡率高得令人无法接受，或者奶牛的寿命低且令人沮丧地缓慢增加。

确定育种目标是育种组织面临的主要创业挑战之一，它始终必须考虑预期的未来生产条件、需求结构和社会经济背景。就我个人而言，我预计在许多西方国家，育种目标必须在更大程度上响应社会对动物福利的期望，但也要响应对动物生产过度工业化的日益增长的怀疑。限制全球变暖的国际承诺和协调行动也将更加重视与养分效率、直接温室气体排放和气候适应相关的性状复合体。从长远来看，我们甚至应该为肉类和动物产品消费量的下降做好准备，由于与植物性饮食相比，它们固有的“气候效率低下”。纯粹的经济方法在这里有其局限性，因此应重新审视和考虑更多本着“预期收益”精神的替代方法。这种发展肯定会受益于动物育种界与经济学、商业和社会科学专家的加强交流。

育种目标决定了去哪里，育种计划描述了如何到达那里。育种计划的概念核心是育种者方程，它可以追溯到 1937 年 Lush 的开创性著作《动物育种计划》。从这个简单的方程可以看出，动物育种基本上具有三个主要杠杆来加速遗传进步： 增加选择强度、更准确的育种值和更短的世代间隔。等式中的第四个因素，可用遗传变异，主要通过避免近亲繁殖对遗传变异的侵蚀来维持遗传进步的可能性，而有效增加遗传变异的可能性，并通过这种遗传进步，在许多方面受到限制。案件。主要影响育种效率的额外杠杆（即 遗传进步超过育种成本）是性能测试的经济学。在回顾近几十年来的科学文献时，我的印象（诚然可能受个人偏见的影响）是，不成比例的研究致力于单一因素，即提高育种价值估计的准确性，而其他杠杆被大大低估了。自 1970 年代中期通过 Henderson 引入基本 BLUP 概念以来，出现了一些重要的方法论步骤，例如建立动物模型、随机回归模型以及——最显着的——引入基因组育种价值估计。此外，我们已经看到了对既定评估方法的无数变化的广泛研究，在实际情况下，这通常被发现对选择候选人的排名产生微乎其微的影响。在基因组时代尤其如此，在整个方法论“字母表”的开发过程中，与 GBLUP 等简单而强大的基本方法相比，它从未带来可持续的优势。

其他努力旨在识别最终有助于做出更好遗传预测的单个基因或 QTL。在复杂性状的背景下，这种最初在 1990 年代“标记辅助选择”时代发展起来并延续到基因组选择时代的方法，除了少数例外，并没有显着提高育种值的准确性。有助于改善遗传进展。尽管开展旨在更好地了解复杂性状的遗传基础的研究有很好的论据，但直接增加这些性状的遗传进展并不是其中最有说服力的。

虽然研究重点一直放在提高育种值准确性的研究上，但影响遗传进展的另外两个主要杠杆，即选择强度和世代间隔，相对较少受到研究关注。特别是选择强度也与育种成本直接相关，使其成为育种计划盈利能力的最关键因素。两者都是育种计划设计的主要元素，因此技术性较低，但组织性较强。这可能是过去几年甚至几十年研究很少关注此类主题的原因之一。这个缺点的另一个原因是缺乏合适的方法来设计和优化现代育种计划，不能再用简单形式的育种者方程来表示，但本质上很复杂，因此需要进行充分的设计。在本期的第一篇文章中，我们提出了基于模块化方法的动物育种计划的统一概念的想法，该方法可以全面表示任何复杂性的育种计划。在此基础上，可以评估并最终优化具有不同目标函数的育种计划。

使用更先进的遗传评估方法可以将育种值的准确性提高几个百分点，这直接转化为相同幅度的遗传进步。我认为，在许多实际的育种计划中，通过更严格的育种过程组织来实现类似甚至更高比例的选择强度增加或世代间隔的减少将相对简单。然而，我们很少看到基于概念替代方案的创新育种计划的研究，例如更核心的奶牛育种，或者更系统地使用创新生殖技术的计划，如精液和胚胎性别鉴定或克隆，但也包括自动表型分析的新方法。

话虽如此，我们当然应该做一个不遗余力的，那就是将最准确的遗传评估方法与最有效的育种计划设计和严格执行相结合，以实现反映我们时代挑战的育种目标。