Internal distribution of a standard Lidia farm considering the different types of animals classified by sex and age [11].
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More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
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Very important organization is thought to be the integral process of resource, transport, partitioning, metabolism, and production, which involves yield, biomass, and productivity in plants. Accordingly, it is important to obtain more information about the knowledge concerning yield, biomass, and productivity in plants. Soybean is one of the main crops largely contributing to our life, which is thought to be connected to our ecosystem through the above-mentioned integral process. This book focuses on the soybean, and reviews and research concerning the yield, biomass, and productivity of soybean are presented herein. This text updates the book published in 2017. 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In 1989, she graduated in Geological Sciences at the University of Naples 'Federico II”. In 1993, she earned a PhD degree\nin Sedimentary Geology at the University of Naples 'Federico II”,\nDepartment of Earth Sciences, Faculty of Geological Sciences. She\ncompleted a 2-year postdoctoral fellowship at the University of\nNaples 'Federico II”, a CNR-CEE fellowship, and several contracts\nat the Research Institute 'Geomare Sud”, CNR, Naples, Italy. Since 1998, she has\nbeen a full-time researcher at the Italian CNR. Dr. Aiello has 25 years’ experience in\nthe field of sedimentary geology, marine geology, and geophysics, participating in\ndifferent research projects for the Italian National Research Council (CARG, Vector,\nCentri Regionali di Competenza). 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Artificial intelligence (AI) is a branch of computer science that seeks to create intelligence. While humans have been using computers to simplify several tasks, AI provides new options to use computers. For instance, voice recognition software uses AI to transform the sounds to the equivalent text words. There are several techniques that AI includes. An artificial neural network (ANN) is one of these techniques.
Humans use their intelligence to solve complex problems and perform daily tasks. Human intelligence is provided by the brain. Small processing units called neurons are the main components of the human brain. ANNs try to imitate partially some of the human brain behavior. Thus, artificial neurons are designed to mimic the activities of biological neurons.
Humans learn by experience: they are exposed to events that encourage their brains to acquire knowledge. Similarly, ANNs extract information froma data set; this set is typically called the training set and is organized in the same way that schools design their courses’ content. ANNs provide an excellent way to understand better biological neurons. In practice, some problems may be described by a data set. For instance, an ANN is typically trained using a data set. For some problems, building a data set may be very difficult or sometimes impossible as the data set has to capture all possible cases of the experiment.
Simulated annealing (SA) is a method that can be used to solve an ample set of optimization problems. SA is a very robust technique as it is not deceived with local minima. Additionally, a mathematical model is not required to apply SA to solve most optimization problems.
This chapter explores the use of SA to train an ANN without the requirement of a data set. The chapter ends with a computer simulation where an ANN is used to drive a car. Figure 1 shows the system architecture. SA is used to provide a new set of weights to the ANN. The ANN controls the acceleration and rotation speed of the car. The car provides feedback by sending vision information to the ANN. The distance traveled along the road fromthe Start is used by the method of SA. At the beginning of the simulation the ANN does not know how to drive the car. As the experiment continues, SA is used to train the ANN. Each time the temperature decreases, the ANN improves its driving skills. By the end of the experiment, when the temperature has reached its final value, the ANN and the car have evolved to the point that they can easily navigate a maze.
System architecture.
An ANN is a computational method inspired in biological processes to solve problems that are very difficult for computers or humans. One of the key features of ANNs is that they can adapt to a broad range of situations. They are typically used where a mathematical equation or model is missing, see [4]. The purpose of an ANN is to extract, map, classify or identify some sort of information that is allegedly hidden in the input, [13].
The human brain is composed of processing units called neurons. Each neuron is connected to other neurons to form a neural network. Similarly, the basic components of an ANN are the neurons. Neurons are arranged in layers inside the ANN. Each layer has a fixed number of neurons, see [5]. For instance, the ANN shown in the Figure 2 has three layers: the input layer, the hidden layer, and the output layer.
An ANN accepts any kind of input that can be expressed as a set of numeric values; typical inputs may include: an image, a sound, a temperature value, etc. The output of an ANN is always dependent upon its input. That is, a specific input will produce a specific output. When a set of numeric values is applied to the input of an ANN, the information flows from one neuron to another until the output layer generates a set of values.
An artificial neural network.
The internal structure of an artificial neuron is shown in Figure 3(a). The output value z is given by:
where each wiis called weight. A fixed input, known as Bias, is applied to the neuron, its value is always 1 and wbis the respective weight for this input. The neuron includes also an activation function denoted by f (y) in Figure 3(a). Without the Bias, the output of the network would be f (0) when all inputs are zero. One common activation function used in multilayer ANNs is:
Artificial neuron.
The activation function of Equation 2 is plotted in Figure 3(b). The activation function (in this figure) is real, continuous, limited and has a positive derivative.
A neuron can be active or inactive, when the neuron is active its output is 1, when it is inactive its output value is 0. Some input values activate some neurons, while other values may activate other neurons. For instance, in Figure 4, the sound of the word Yeswill activate the first output neuron, while the sound of the word No will activate the second neuron at the output of the network. The structure of the ANN shown in this figure is very simple with only two neurons.
ANN activation.
Before an ANN can be used for any practical purpose, it must be trained. An ANN learns during its training. For the duration of the learning process the ANN weights are recurrently adjusted.
In some instances, ANNs may learn from a data set. This set is typically known as the training set, and it is used on a new ANN (as its name indicates) for training. The training set has two parts: the input and the target. The input contains the set of inputs that must be applied to the network. The target includes the set of desired values at the output of the ANN. In other words, each sample (case) in the training set completely specifies all inputs, as well as the outputs that are desired when those inputs are presented, see [7]. During the training, each case in the training set is presented to the network, and the output of the network is compared with the desired output. After all cases in the training set have been processed, an epoch or iteration has completed by updating the weights of the network. There are several methods for updating the weights at each epoch or iteration. All these methods update the weights in such a way that the error (measured between the actual output of the network and the desired output) is reduced at each epoch, see [7].
Some training methods are based on the gradient of the error (they are called gradient based methods). These methods quickly converge to the closest local minima. The most typical gradient based methods to train an ANN are: the variable metric method, the conjugate gradient method, and method of Levenberg-Marquardt. To make the training of an ANN robust, it is always recommended to combine gradient based methods with other optimization methods such as SA.
When an ANN is trained using a data set, typically the set includes many training cases, and the training is done at once. This kind of training is called structured learning because the knowledge is organized in the data set for the network to learn. One of the main disadvantages of structured learning is that the training set has to be prepared to describe the problem at hand. Another disadvantage of structure learning is that if more cases are added to the training set, the ANN has again to be trained starting from scratch. As ANN training is time consuming, structured learning may be inadequate for problems that require continuous adaptation.
In continuous learning, an ANN does not require a data set for training, the ANN learns by experience and is able to adapt progressively by incorporating knowledge gradually. Because some problems cannot be appropriately described by a data set, and because training using a data set can be time consuming, continuous learning is important for real-time computing (RTC) where there is a "real-time constraint".
After the ANN training has been completed, the network performance has to be validated. When using ANNs, the validation process is extremely important, as a matter of fact, the validation is as important as the training, see [7]. The purpose of the validation is to predict how well the ANN will behave in other conditions in the future. The validation process may be performed using a data set called the validation set. The validation set is similar to the training set but not equal. Under normal circumstances (when the ANN is properly used), the error obtained during training and during validation should be similar.
SA is an optimization method that can be used to solve a broad range of problems, [11]. SA is recommended for complex optimization problems. The algorithm begins at a specifictemperature; as time passes the temperature gradually decreases following a cooling scheduleas shown in Figure 5. The solution is typically described by a set of variables, but it canbe described by other means. Once the algorithm has started, the solution approachesprogressively the global minimum that presumably exists in a complex error surface, see [16]and [15]. Because of its great robustness, SA has been used in many fields including thetraining of ANNs with structured learning, [9].
One of the key features of SA is that it always provides a solution, even though the solution may not be optimal. For some optimization problems that cannot be easily modeled, SA may provide a practical option to solve them.
Cooling: three ANNs learning.
Simulated annealing evolution includes the use of: ANNs, continuous learning and SA. In simulated annealing evolution, an ANN does not require a training set; instead the ANN gradually learns new skills or improves existing ones by experience. Figure 5 shows how SA evolution works. In this figure, a typical cooling schedule used in SA is displayed. Suppose that there is a 2D landscape with valleys and hills as shown in this figure. Suppose also that it is desired to find the deepest valley on this landscape. Each of the balls, in this figure, represents an ANN. At the beginning of the simulation, the high initial temperature produces a state of high energy; the balls shake powerfully and are able to traverse easily through the high hills of the 2D terrain. In other words, each ANN is exploring, that is, the ANN is in the initial step of learning. As the temperature decreases, the energy of the balls decreases, and the movement of the balls is more restricted than at high temperatures, see [1]. Thus, as the temperature diminishes, the ANN has less freedom to explore new information as the network has to integrate new knowledge with the previous one. By the end of the cooling schedule, it is desired that one of the balls reached the deepest valley in the landscape, in other words, that one ANN learned a set of skills.
At each temperature, an ANN (in the set) has the chance to use its knowledge to perform a specific task. As the temperature decreases, each ANN has the chance to improve its skills. If the ANNs are required to incorporate new skills, temperature cycling can be used, see [6]. Specifically, an ANN may learn by a combination of SA and some sort of hands-on experience. Thus, simulated annealing evolution is the training of an ANN using SA without a training set.
Each ANN may be represented by a set of coefficients or weights. For illustrative purposes only, suppose that an ANN may be described by a set of two weights w11 and w12. In Figure6, there are three individuals, each is represented by a small circle with its two weights: w11 and w12. At every temperature, each ANN is able to explore and use its abilities. At high temperatures, each ANN has limited skills and most of the ANNs will perform poorly at the required tasks. The gray big shadow circle in the figure indicates how the ANNs are considering different set of values w11 and w12 for testing and for learning. As temperature decreases, the ANNs get closer to each other, illustrating the fact that most of them have learned a similar set of skills. By the time the temperature has reached its final value, the skills of each ANN will be helpful to the degree that the network is able to perform the task at hand. At this moment, the simulation may end or the temperature may increase, if there are new skills that need to be incorporated.
Each ANN tries to learn the same set of skills.
To illustrate how to use simulated annealing evolution, this section presents a simple learning problem. The problem consists of using an ANN to drive a car in a simple road. Clearly, the problem has two objects: the road and the car. The road includes a Start point and a Finish point as shown in Figure 7. The road includes several straight lines and turning points. The car is initially placed at the Start. The driving is performed by an ANN that was integrated with the car. Specifically, the ANN manipulates indirectly several parameters in the car such as position, speed and direction. The purpose of the simulation is to train the ANN without a training set. At the beginning of the simulation, the ANN does not know how to drive; as the evolution continues the ANN learns and improves its skills being its goal to drive the car quickly from the Start to the Finish without hitting the bounds of the road (that is, the car must always stay inside the road). To solve this problem simulated annealing evolution was used to train the ANN.
The simulation was performed using object oriented programming (OOP); the respective UML diagrams for the simulation are shown in Figures 8 and 9. The two basis classes are shown in the diagram of Figure 8. This diagram includes two classes: Point and Bounds. The Point class in the diagram represents a point in a 2-Dimensional space, the diagram indicates that this structure includes only two floating point variables: x and y. The Bounds class, in the same UML diagram, is used to describe the bounds of an object when the object is at different positions and rotations angles. The main purpose of the Bounds class is to detect collisions when one object moves around other objects.
The car and the road.
UML diagram showing the basic classes.
Figure 9 shows the respective UML diagram for the Object class. This class represents a static object in a 2-Dimensional space. The class name is displayed in italics indicating that this class is abstract. As it can be seen the Render() method is displayed in italics, and hence, it is a virtual method and must be implemented by the non-abstract derived classes. If the experiment includes some sort of visualization, the Render() method may be used to perform custom drawing. There are many computer technologies that can be used to perform drawing, some of them are: Microsoft DirectX, Microsoft GDI, Microsoft GDI+, Java Swing and OpenGL. From the UML diagram of Figure 9, it can be seen that each object has a position, a rotation angle (theta) and a set of bounds. ThemethodIsCollision() takes another object of type Object and returns true when the bounds of one object collidewith the bounds of another object. This method was implemented using a simple version of the algorithm presented by [14].
UML diagram showing the Car and the Road classes.
This class represents a moving object in a 2-Dimensional space. Each Mobile object has a speed, an acceleration and a rotation speed as shown in the UML diagram of Figure 9. This class is derived directly from the Object class, and therefore, is also abstract as the Render() method is not implemented. The UpdateBounds() method for this class takes the number of seconds at which the object bounds will be computed. This method is extremely useful when moving an object around other objects, for instance, if the bounds of one object intersect with the bounds of another object, the object cannot move, this is implemented internally in the method IsCollision(). To update the bounds of a Mobile object, the speed of the object may be computed using Equation 3,
wherev1 is the initial velocity of the object, v2 is its final velocity, a is the acceleration, and t stands for the time for which the object moved. Similarly, the position of the object can be updated using another Kinematic equation. To compute the new position of the object Equation 4 can be used,
where the symbol d stands for the displacement of the object. In most cases, the object moves along a line described by its rotation angle and its position. Thus d has to be accordingly projected in the coordinate system as shown by Equations 5 and 6,
In some cases, the object may be rotating at a constant speed and, hence, the rotation angle has also to be updated at each period of time.
During the experiments, the methods UpdateBounds() and IsCollision() of the Mobile class are used together to prevent object collision. First, the simulator calls the UpdateBounds() method to compute the bounds of the object at some specific time, and then will call the IsCollision() method to check for potential collisions with other objects.
The simulation experiments were performed using only two classes: the Road class and the Car class. The UML diagram of the Road class is shown in Figure 9. The Road class is derived directly from the Object class; the method Render() is implemented to draw the road displayed in Figure 7 using Microsoft GDI and Scalable Vector Graphics (SVG).When the car leaves the Start, the ANN has to make its first right turn at 90°, as the car is just accelerating this turn is easy. The following turn is also to the right at 90°and the ANN should not have any trouble making this turn. Then, if the ANN wants to drive the car to point A, it has to make two turns to the left.
The straight segment from A to B should be easy to drive; unfortunately, the ANN maycontinually accelerate, and reach point B at a very high speed. The turn at point B is themost difficult of all the turns, because the car has to make a right turn at 90°at high speed.Because the simulation is over when the car hits the bounds of the road, as soon as the ANNcan see the turn of point B, it has to start reducing the speed of the car. Once the ANN hasmanaged to drive to point B, reaching the Finish should not be difficult.
The car used for the simulation is shown in Figure 10. The car has a position represented byx and y in Figure 10; the car rotation is represented by θ. The speed and acceleration vectorsare represented by v and a respectively. The arrow next to the rotation speed in Figure 10indicates that the car is capable of turning. The car has several variables to store its state(position, theta, speed, acceleration, rotationSpeed and neuralNetwork) as shown in the UMLdiagram of Figure 9. The Car class derives directly from the Mobile class and implements themethod Render() to draw the car of Figure 10 usingMicrosoft GDI and SVG.When v = 0 anda = 0 the values of x and y do not change. When v ≠0 and a = 0, the values of x and ywill change while v remains constant. When a ≠0, the values of x, y and v will change. Themethod GetDistance() computes the distance that the car has traveled along the road fromthe Start, this distance is represented by s in Figure 7.
Figure 11 illustrates how the car is able to receive information about its surroundings. Thecar had seven vision points illustrated by the arrows in the figure. To prevent the ANN fromdriving backwards, no vision lines were placed in the back of the car. Each value of d1, d2,...d7, represents the distance from the center of the car to the closest object in the directionof the vision line. These values were computed using the bounds of the road. To create amore interesting environment for the ANN, the values of d1, d2,...d7 were computed at lowresolution and the car could not see objects located away from it.
In real life, a car driver is not able to modify directly the position or velocity of the car, thedriver only controls the acceleration and the turning speed. Asmention before, each car in the simulation has an ANN to do the driving. At each period of time, the ANN receives the visioninformation from the surroundings and computes the acceleration and the rotation speed ofthe car. Figure 12 shows the ANN of the car, the ANN has 8 inputs and two outputs. As itcan be seen from this figure, the speed of the car is also applied to the input of the network;this is very important because the ANN needs to react differently depending on the currentspeed of the car. For instance, if the car moves a high speeds and faces a turn, it needs toappropriately reduce its speed before turning. As a matter of fact, the ANN needs to be readyfor an unexpected turn and may regulate the speed of the car constantly.
The car.
The car vision lines.
Artificial Neural Network for driving the car.
This section explains how SA was used to train the ANN. The implementation of SA wasdivided in three steps: initialization, perturbation and error computation.
The ANN training process using SA is illustrated in Figure 13. The simulation starts byrandomly setting the network weights using a uniform probability distribution U[-30, 30].In the second block, a copy of the weights is stored in a work variable. In the third block,the temperature is set to the initial temperature. For the simulation experiments, the initialtemperature was set to 30. In the fourth block (iteration = 1), the optimization algorithmbegins the first iteration. Then, the work variable (a set of weights) is perturbed. After theperturbation is completed, the ANN weights are set to these new weights. At this moment,the ANN is allowed to drive the car and the error is computed as shown in the figure. Thetemperature decreases exponentially and the number of iterations is updated as shown inthe flow diagram. The simulation ends when the error reaches the desired goal or when thetemperature reaches its final value (a value of 0.1 was used).
The cooling schedule used in the simulations is described by Equation 7,
whereTj+1 is the next temperature value, Tjis the current temperature, and 0 <c <1. Clearly,the cooling schedule of Equation 7 is exponential and slower than a logarithmic one, thereforeSimulated Quenching (SQ) is being used for the training of the ANN, see [3].
Observe, that each time the ANN weights are perturbed, the ANN is allowed to drive the car.Then, the error is computed and the oracle makes a decision about whether the new set ofweights is accepted or rejected using the probability of acceptance of Equation 8, see [5] and[11]. Some implementations of SA accept a new solution only if the new solution is better thanthe old one, i.e. it accepts the solution only when the Error decreases; see [7] and [10]. Theprobability of acceptance is defined as
where
A uniform probability distribution was used to generate states for subsequent consideration.At high temperatures, the algorithm may frequently accept an ANN (a set of weights) evenif the ANN does not drive better than the previous one. During this phase, the algorithmexplores in a very wide range looking for a global optimal ANN, and it is not concernedtoo much with the quality of the ANN. As the temperature decreases, the algorithm is moreselective, and it accepts a new ANN only if its error is less than or very similar to the previoussolution error following the decision made by the oracle.
Flow diagram for simulated annealing evolution.
Because of the properties of the activation function of Equation 2, the output of an ANN islimited. As mentioned before, an ANN is trained by adjusting the weights that connect theneurons. The training of an ANN can be simplified, if the input applied to the ANN is limited.Specifically, if the input values are limited from −1 to 1, then the ANN weights are limited toapproximately from −30 to 30, [8] and [12]. To simplify the simulation, the input values of theANN were scaled from −1 to 1. Therefore, the SA initialization consisted in simply assigninga random value from −30 to 30 using a uniform probability distribution to each of the ANNweights as shown in the C++ code shown in Figure 14. Observe that the random numbergenerator uses the (ISO/IEC TR 19769) C++ Library Extensions TR1:\n\t\t\t\t\tdefault_random_engineand uniform_real. In this case, the ANN has two sets of weights: the hidden weights and theoutput weights. Each set of weights was stored in a matrix using the vector template fromtheStandard Template Library (STL); each matrix was built using a vector of vectors.
Implementation of SA initialization using the C++ language.
The code of Figure 15 shows the implementation of the SA perturbation using the C++language. First, each ANN weight was perturbed by adding a random value from –Tto T using a uniform probability distribution (tr1::uniform_real), where T is the currenttemperature. Second, if the perturbed weight was outside the valid range from −30 to 30,the value was clipped to ensure that the weight remained inside the valid range.
Implementation of SA perturbation using the C++ language.
In order to measure the driving performance of the ANN, an error function E was defined asshown in Equation 9,
where the variable s represents the distance along the road measured from the Start to thecurrent position of the car as shown in Figure 7. As it can be seen the value of the errordecreases as the car drives along the road. The smallest error is accomplished when the carreaches the Finish.
The code of Figure 16 illustrates the implementation of the error function. The function startsby setting the ANN weights. The variable deltaTimeSecis used to refresh the simulation,a value of 16.7 milliseconds was used; it provides a refreshing frequency of 60 Hz (so thatthe simulation could be rendered on a computer display at 60 frames per second). Next, thefunction begins a while block, at each iteration the car bounds are updated and the simulationchecks for a collision between the car and the road. If there is a collision the simulation stopsand the error is computed. If there are not collisions, the ANN computes vision informationand updates the acceleration and rotation speed of the car.
Implementation of SA error function the C++ language.
Several experimental simulations were performed using different configurations to analyzethe behavior of the ANN and the car.In the first simulation, the speed of the car was not applied at the input of the ANN, in allcases, the ANN was not able to turn at point B. At some unexpected point, the ANN was ableto see the approaching turn of point B and did not have enough time to reduce the speed ofthe car.
In the second simulation, the number of neurons in the hidden layer was varied from 0 to5. When the number of neurons in the hidden layer was zero, the ANN was able to drivethe car to the Finish in 90% of the cases. When the number of neurons in the hidden layerwas increased to one, the car was always able to get to the Finish. It was also observedthat the ANN was driving faster when using more neurons in the hidden layer, thus, the carwas getting to its destination quicker. When the number of neurons in the hidden layer wasincreased to 5, there were not any noticeable changes in the performance of the car than whenthe ANN had 4 neurons in this layer.
The third experiment consisted in varying the number of vision lines described in Figure 11.The number of vision lines was varied from 3 to 7. When using 3 vision lines, the ANN wasable to reach 50% of the times to point A, 10% of the cases to point B and it was never able toget to the Finish. When the number of vision lines was set to 4, the ANN was able to drive thecar to the Finish in 50% of the cases. When the number of vision lines was set to 5, 6 or 7, theANN was always able to drive the car to the Finish. However, the ANN was always drivingfaster when using more vision lines.
The SA parameters were set to be compatible with the ANN weights. The initial temperaturewas 30, the final temperature was 0.1. Some experiments were performed by using lowerfinal temperatures, but there were not any noticeable changes in the performance of the ANN.The number of temperatures was set to 10 using 20 iterations per temperature. Some testswere performed using more numbers of iterations, but there were not improvements. All thesimulations were run using an exponential cooling schedule.
To validate the training of the ANN, another road similar to the shown in Figure 7 was built.In all cases, the ANN behaved similar in both roads: the road for training and the road forvalidation.
An ANN is a method inspired in biological processes. An ANN can learn from a training set.In this case, the problem has to be described by a training set. Unfortunately, some problemscannot be easily described by a data set. This chapter proposes the use of SA to train an ANNwithout a training set. We call this method simulated annealing evolution because, the ANNlearns by experience during the simulation.
Simulated annealing evolution can be used to train an ANN in an ample set of cases.Because human beings learn by experience, simulated annealing evolution is similar to humanlearning.
An optimization problem was designed to illustrate how to use SA to train an ANN. Theproblem included a car and a road. An ANN was used to drive the car in a simple road.The road had several straight segments and turning points. The objective of the ANN was todrive the car from the Start to the Finish of the road. At the beginning of the simulation, thecar was placed at the Start and the ANN weights were set to random values. Obviously, theANN could not drive too far the car without hitting the bounds of the road, and stopping thesimulation. By the time the temperature reached its final value, the ANN was able to drivesuccessfully to the Finish of the road as it will be briefly described.
During the simulation, the car had a set of vision lines to compute the distance to the closestobjects. The distance from each vision line (measured from the car to the closest object) wasapplied to the input of an ANN. It was noticed that the ANN performed much better whenthe speed of the car was also applied to the input of the ANN.
The number of neurons in the hidden layer of the ANN was varied during the simulations. Itwas observed that when the number of neurons in the hidden layer was increased, the ANNwas able to reach quicker the Finish. It was observed also that when using 5 or more neuronsin the hidden layer, the performance of the ANN did not improve. It was noticed, however,that when using zero neurons in the hidden layer, the ANN could not always drive the car tothe Finish.
The car vision consisted in a set of lines. Experimental simulations were performed varyingthe number of vision lines form 3 to 7. The experimental results indicated that when 3 visionlines are used, the ANN does not have enough information and cannot drive successfullyto the Finish. It was observed also that when the number of vision lines was increased, thedriving of the ANN was smoother. Finally, it was noticed that when the number of visionlines is increased to 8 or more, the ANN did not improve its performance (meaning that therewere not observable changes in its driving).
Lidia’s cattle breeding has been, and continues to be, one of the most genuine animal production sectors, due to the particular ethological characteristics of this breed and the peculiarities of the production system and the product obtained, in this case suitable animals for the show [1].
\nSpain is the first Lidia cattle breeding country and has the most varied and important genetic heritage of this breed [2] that is also present in Portugal, southern France, and much of South America such as Mexico, Colombia, Venezuela, Peru, and Ecuador [3].
\nLidia cattle sector represents in Spain a socioeconomic activity of considerable importance, with a total turnover of approximately 1.5 billion euros per year, which does not only affect entrepreneurs, ranchers, and bullfighters, but also more than 200,000 jobs that depend directly or indirectly on the bullfighting activities [4], which constitute the second mass spectacle of Spain and Portugal [5]. Lidia cattle, the second pure breed in the bovine census in Spain [6], are considered the greatest exponent of an extensive breeding system, due to their ethological characteristics, the need for wide spaces, and the difficulty in handling that it presents [7]. In turn, it is a breed of great rusticity, able to adapt and take advantage of all types of terrain, including those of extreme weather conditions [8]. Many farms are located in territories of high landscape value such as natural parks, playing an important role in maintaining biodiversity and contributing to the conservation of the ecosystem [9].
\nThe characteristics of a Lidia standard farm are an average size of 253 mother cows and a total number of heads of 748 animals, including animals of other breeds or those belonging to other species, necessaries for livestock’s handling, with an annual replacement rate of 12% [10]. However, after the economic crisis of 2008, most livestock farms have decreased the number of heads. Nevertheless, the livestock internal distribution remains stable. For a Lidia cattle farm of 100 mother cows, the ideal average internal scheme, considering the different types of animals classified by sex and age, could be the one presented in Table 1 [11].
\nSires | \n3 | \n
Cows | \n100 | \n
Calf male <1 year | \n40 | \n
Males 1–2 years | \n38 | \n
Males 2–3 years | \n36 | \n
Males 3–4 years | \n35 | \n
Bulls 4–6 years | \n34 | \n
Calf female <1 year | \n40 | \n
Heifers 1–2 years | \n36 | \n
Heifers 2–3 years | \n20 | \n
Halters | \n12 | \n
Internal distribution of a standard Lidia farm considering the different types of animals classified by sex and age [11].
The standard farm has a number of hectares ranging from 586 to 721, of which 92% of the land is used as pastures [12].
\nToday, the farming system of the Lidia breed continues to be, mainly, an extensive management system that has gradually adapted to new grazing techniques and food supplementation in times of natural grass decline, such as winter and summer, in dry climates [13]. The extension of the farms is still remarkable, but of much less spacious than that of several decades ago and in terms of quality, the brave cattle have been relegated to less productive and more stepped mountain farms in favor of agriculture or other more profitable species, such as the Iberian pig in Spain and Portugal [7]. In Mexico, most farms are located in the central part of the country, with a dry climate similar to Spain, carrying out similar feeding management. On the other hand, Lidia cattle in Colombia, Venezuela, Ecuador, and Peru are in territories with a tropical climate, whose diet is based on natural grass with a concentrated supplementation during the last stage of preparing the males for the show [14].
\nLidia bull growth estimation [9].
The Lidia cow is a very rustic animal, of few requirements, since its small size also dictated its nutritional needs. Even so, adequate feeding is essential to obtain a good fertility rate, avoiding abortions and perinatal mortality, and, after a good lactation, wean the calf in an optimal state [7, 15]. Currently, the use of natural resources is maximized, preferably by grazing and the supplementation of hay or silage, and if necessary, concentrated food is used at a rate of 2–4 kg/day, depending on the richness of the grass and forage [16, 17].
\nDuring the first 3–4 months, calves are fed exclusively with cow’s milk and develop optimal growth, as long as it comes from a well-fed cow that produces milk of adequate quality and quantity.
\nAfter weaning, and when the animals are between 9 and 10 months old, they are usually supplemented in times of shortage of grass with rations whose fundamental components are fibrous products (beet and citrus pulps, dehydrated or henified alfalfa, and cereal straw), industrial by-products (gluten-feed, wheat bran, soy cake, and beet molasses), and common products in the composition of concentrates of other types of farm animals (corn, barley, wheat, and sunflower meal).
\nGalvanized iron feeders are frequently used, 5 m long by 40 cm wide approximately, which allow to guarantee half a meter of free space per animal, avoiding hierarchy problems, present in any group of this breed, which could result in some type of undernourishment particularly important in this stage of development. Likewise, several water points distributed along the fenced space must be installed, arranged around the feeder area, to favor the movement of animals across different areas and to avoid their concentration in one point.
\nWhen the animals are around 23 months of age, they are slowly provided, during 4 weeks of adaptation, an increasing proportion of the ration designed for adult animals of 3 and 4 years, in order to adapt them to the finishing feeding diet composition.
\nLivestock facilities used for these animals have similar characteristics to the ones described for young animals, although in case of using individual feeders, the number of feeders is usually 10% greater than the number of animals to be fed [18]. Also, the different water points are often installed at a greater separation distance from the feeders (at least 500 m), to facilitate a better distribution throughout the land surface. As in previous phases, a supplementation is necessary, which as an example could be based on the addition of 0.5 kg of alfalfa hay to the total supplementation established in the previous phase, thus leaving 2.5 kg of alfalfa hay added to 0.5 kg of concentrate per animal per day [15].
\nThe feeding systems described during the 1980s based on a final bait are still in force today. Although each farmer has its own feeding methodology, depending on the availability of grass and other types of food on the farm, the possibility of growing the forage or concentrate on the farm itself or the use of agricultural by-products such as citrus pulp or some derived from the olive oil industry.
\nThis final bait is carried out in fenced areas of small size, frequently without grass, with a daily supply of rations of high energy concentration and high digestibility [19]. This last feeding stage is called “pre-lidia bait” or “finishing,” and it can vary between 5 and 12 months and usually begins during the winter [20], adapting the amount of ration supplied to the bulls at the date on which they have to fight.
\nThe average fenced area used for these bulls is usually around 60 hectares per farm, and the average number of animals per enclosure is 20 (which is equivalent to a density of 3 hectares per bull), although each farm distributes its animals in a way different. The average daily gain (GMD) is approximately 450 g/day (Figure 1), which means that in this period, the bulls gain about 150 kg of weight, 30% of their final body weight, considering a standard bull of 500 kg of weight at 4 years of age [7].
\nThe use of long feeders is common, especially in southern Spain, compared to the classic individual and small feeder used in farms located on the center of Spain (Figure 2). The distribution of food is done during the morning and the afternoon in most of the farms [21].
\nLong feeder and single feeders.
There is a critical point in the strategy of feeding management, due to the overfeeding carried out during the last year, prior to the fight, which causes an overload of weight in the bone structure, added to the state of obesity that causes a lack of strength and mobility of the animal that limits its behavior in the arena and, therefore, the show itself.
\nThe problem lies in the overfeeding to which it is subjected in the final phase of its growth during variable periods of time (from 8 to 12 months) that generates a series of pathologies and inconveniences that negatively influence its productive aptitude: the behavior in the ring.
\nSeveral studies have been carried out on the effect of intensive bait on rumen physiology of Lidia cattle [16, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31], and all of them point to ruminal acidosis, a primary pathology that predisposes the appearance of secondary lesions such as liver abscesses, gastrointestinal ulcers, ruminal parakeratosis, laminitis, anthill, and so on. Later we will address this pathology more widely.
\nLidia females reach puberty at approximately 12 months of age but must reach the two-third of adult body weight before becoming pregnant [7] at approximately 2 years of age, and the productive lifespan time lasts for 8–10 years with a calving-gestation interval of 2–4 months [32].
\nLidia bulls begin to show sexual activity from 6 months of age reaching puberty at 10–12 months, having been necessary to separate them from females before 1-year olds [7]. At 2 or 3 years, the selected sires are tested with a small group of females, but they are not profusely used until their female offspring are tested, and the quality of their genetic is proved, once this happens, they could be 15 years contributing its genetic flow in natural mating to the cattle ranch [32].
\nAt present, in the majority of Lidia farms, the reproductive handling is very traditional with natural mating of one sire and 30–40 cows during several months. The outstanding difference with the past management is that now the fertility is greater due to a better cow’s body condition that allows them to perform a successful gestation and lactation every year [33] (Figures 3 and 4).
\nLive weight variation of a Lidia cow [23].
Calving and natural mating management based on grazing availability in seasonal countries.
In Europe, the duration of the mating period in Lidia cattle is similar to that of other extensive bovine breeds, being able to reach up to 8 months (autumn–spring) in livestock farms with longer periods, but its duration is often shorter, from the end of winter to the beginning of summer (March–July), since at this time, the best results are obtained in heat of the cows and fertility, due to both photoperiod and feeding reasons. In countries as Colombia without reproductive stations, the cycle is continuous.
\nThere are relevant anatomical differences in the reproductive system of the female of the Lidia cattle: the cervix is longer in length than other bovine breeds; they present a uterine body very short, and it seems as nonexistent during transrectal palpation [34]. It is similar to the bipartite uterus in rodents, and the ovary has a very small size compared to other breeds of similar size presenting at the oviduct level the largest infundibulum that surrounds much of the ovary [35].
\nAt the same time, there are hormonal differences because the Lidia cow reaches puberty earlier and has a shorter gestation period than other breeds: 286 days [36]. The natural mating should last long enough to guarantee a good fertility rate, with a minimum recommended period of 3–4 months (each cow has at least three opportunities to get pregnant), but there are farmers who extend it more, and there are even systems with continuous natural mating, more common in tropical countries like Colombia. Short mating periods have the advantage of being able to concentrate the calving with better control of herd management and feeding. It is done more in larger herds, in large areas, where lactation is adapted to times of pasture abundance.
\nThe utilization of techniques for semen collection and conservation for artificial insemination (AI) began to be used three decades ago in Lidia bulls. Later, embryo-transfer from high genetic merit Lidia cows to dairy cattle and cloning of one sire to preserve the excellent genetic quality was achieved. These reproductive methods, used to improve the productive characteristic of dairy and beef cattle, could be useful future tools to increase the genetic progress in Lidia cattle behavior selection [37].
\nThere are immense advantages in using cryopreservation, due to semen dilution and conservation during medium and large periods, increasing the possibilities to use it for decades through AI when the behavioral results of their offspring are well known. There is also the possibility to extract post-mortem semen from the epididymis after the fight in the bulls of extraordinary behavior [38]. In this way, each farmer begins to have his own semen bank of his own sires and bulls. In turn, this would allow the exchange of semen between breeders, to refresh the blood of their livestock, being easy to transport to farms located in the countries of America. Among the advantages of this technique are avoid risks of contagions of potential pathologies, allowing the reproduction of animals of different sizes because natural mating is not necessary, and also is not necessary to move the male, allows the collection of semen in extreme situations, and, above all, enables the possibility to use some improving individuals of a contrasted character in a large number of females [39].
\nThe biggest problems are due to the handling difficulties of these animals due to the untidy nature of this breed. Insemination implies added high-risk management both for animals and for people that seriously conditions, from a technical and economic point of view, its generalization in the Lidia cattle [37]. The introduction of other reproductive techniques such as early pregnancy diagnosis allows to discover and treat uterine pathologies, helping to detect nonpregnant cows that could be resynchronized or intended for natural mating and reducing calving pregnancy intervals. Reproduction control does not necessarily imply the hormonal treatment of all animals nor their subsequent insemination because it is possible to use mixed models in which the natural mating and AI are used in a complementary way [40].
\nOnce the AI technique will be established, the next step will be to adapt an embryo transfer program to this type of cattle. Currently, it is used to preserve the valuable genetic material of small farms and to increase the reproductive efficiency of some females. In recent years, this technique has contributed to the formation of germplasm banks as genetic reserve in cases of farms with severe health problems or encastes\n1 in danger of extinction [35].
\nAt the same time, the use sexed semen to obtain a greater number of males than females could create an opportunity, considering the superior economic value of those. However, its use could jeopardize the process of selection and breeding of the farms due to the fact that reducing the numbers of females could be a risk if the proper and strict selection pressure is not applied.
\nRegarding cloning, there are many questions about its efficacy in general and in Lidia cattle in particular. It is not known, for example, if a cloned animal can develop and interact normally with its peers in a highly hierarchical and of great rivalry environment. A cloned individual may have a poor development of the immune or cardiovascular system, and it is not known whether the libido and fertility of a future cloned breeder will be normal. At the moment, it is known that it ages quicker and has a shorter productive lifespan [41]. A cloned bull must also be tested, and in the event that his quality would be acceptable, it will be also necessary to test its offspring to see if it is able to convey his characters.
\nThe cloning of a sire, with the aim of collect semen, may be important in the case of some farms with few breeding males or if it is an individual of outstanding genetic merit and advanced age [42]. In any case, a clone might not have the same ethological characteristics as the animal from which it proceeds, since the behavior is the consequence of its genetic background, the environment in which it develops [33] its ontogenesis or sequential development.
\nTraditionally, three types of selection are made: genealogical, morphological, and functional [32]. In relation to the first, the farmer systematically records information, in his own books, the lines, or families that form the basis of the genetic heritage of his livestock, as well as the results of the offspring of each generation.
\nThis information is used to choose future breeding animals. In addition, each farm defines its morphological preferences, depending on the type it belongs to or the priorities of the owner. The selection criteria are usually higher for males than those used for females. They focus, fundamentally, on aspects related to external appearance, neck musculature conformation, bone structure and development, and so on [43]. And finally, the functional selection consists in measuring the brave character of each animal, although each farmer understands the meaning of this term in a very subjective way. A series of tests are carried out on both females and males to assess their bravery [32].
\nIn the case of females, animals of 1, 2, or 3 years are evaluated. The test is practiced in the tienta,2 under the direction of the farmer and with the participation of professional bullfighters, trying to discover the functional performance of each animal. The behavior of each individual in each phase of the test is assessed using the horse and with the muleta. There are different parameters (prompt response, attack, fixity, mobility, nobility, fierceness, aggressiveness, repetition, and so on) that are evaluated by the farmer, to achieve a final note for each animal and, subsequently, keep the best females as breeders [44].
\nIn the test of males, animals of 2–4 years of age are chosen, initially selecting the specimens that have obtained the best results in the genealogical and morphological tests. They are tested in a small bullring, and if the animal does not respond properly, the test is interrupted, and the bull is withdrawn and will be destined for normal fighting. Those animals initially selected, after testing the behavior of their offspring, will become part of the livestock as a sire or will be discarded, losing their value for a standard fight since they have developed sense during the test fight [32].
\nThere is another circumstantial and sporadic form of sire selection, performed by fans and not by the farmer, which is the case of indulto.3 It occurs in the context of a bullfight where many influential factors could alter the true criteria by which a bull must be selected. Therefore, it is the breeder who will decide, later, if the animal should be used for reproductive purpose or not.
\nCurrently, another type of selection, genetics, has been introduced by livestock associations, which has become increasingly important [9]. It consists of identifying the individuals carrying the most beneficial genes for the interest characters and using them as breeding animals to transmit them to their descendants. The way to evaluate whether or not the phenotype of an animal is a good reflection of the genes of which it is a carrier (genetic value or merit) is based on calculating the heritability of that character [45].
\nThe capacity to transfer behavioral characters is very slow because it is limited by the production of a calf per year, at the most, as well as the complexity to accurately and quickly assess the ethological response of its products in the show [46].
\nAccording to Cañón et al. [2], many of the behavioral characters manifested by the Lidia bull, such as mobility, repetition, nobility, rhythm, and fierceness, despite its complexity and subjective assessments, if scored with enough rigor, can manifest high heritability (>0.35) that makes them susceptible to be selected in one way or another, at the choice of the farm’s owner.
\nA very precise selection of the best individuals entails the maintenance of a population with high consanguinity; therefore, controlling it is an always necessary activity in a Lidia cattle ranch, preserving the necessary genetic variability within it. In general, in Lidia farms, the level of consanguinity does not seem to be very high: 0.12 and 0.13 [47]. Even so, it is possible to find bulls with a consanguinity coefficient of 0.25 [48]. However, regulated mating strategies should be followed, to avoid mating animals with common ancestors, establishing a short- or medium-term conservation program. However, we must be aware of the difficulties involved in the conservation of some minority genetic lines, cattle ranches, or “encastes” [48], because the smaller a population is and the greater the imbalance between the sexes the more difficult it is to preserve their genetic characteristics, complicating the task of avoiding mating between related animals.
\nFinally, the incorporation of the computer methods to control the productive data of the animals allows organization and best valuation of each reproductive potential. With the information reduced to informative schemes, the results can be checked immediately, which make it possible to know, through the corresponding analysis of the offspring, the racing power of the father or mother [45, 49, 50, 51].
\nThe most frequent diseases of Lidia cattle, which also affect extensive cattle, are parasitic processes (coccidiosis, ostertagiosis, dictyocaulosis, and sarcosporidiosis), infectious processes (clostridiosis, anthrax, paratuberculosis, tuberculosis, actinomycosis, actinobacillosis, and pyobacillosis), poisonings (aflatoxicosis, ochratoxicosis, aluminum phosphide, and lead poisoning), and deficiency processes as poliencefalomalacia [52].
\nIn addition, the extensive nature of this animal production system predisposes him to suffer from eye problems such as infectious keratoconjunctivitis and horn wounds due to fights between animals [53]. The latter represents a very important chapter in the economies of Lidia farming assuming losses of traumatic etiology ranging from 3 to 15% of male adult individual. Most of them require surgical treatment; some of the interventions are simple, and others are more complicated, but all have in common the septic character of the traumatic focus [54].
\nThe gorings have an etiology closely related to the age of the bulls, strength, and encaste, with an increase in frequency of incidence in 4-year-old bulls with a weight of 500 kg, and the wounds occur with a greater probability in the head and extremity regions. They are caused by external violence in which the surface of the traumatic agent is wide. We can find open or closed wounds. The closed wounds, even when not seen to affect external skin tissues, can cause internal muscular or vascular lesions. Hematomas or serous effusions (blood and lymphatic exudates) of difficult reabsorption due to their large size appear, and they require intervention. They evolve to contamination and abscess formation [55].
\nThe treatment of all types of wounds should be focused on controlling, primarily, the bleeding, either by suturing damaged vessels or by hemostatic parenteral treatments, then preventing or controlling the infection, disinfecting and cleaning the affected area, and finally achieving the rapid healing, usually by second attempt, and is always suggested to leave a drain at the trauma point even if it is small [56].
\nAnother pathology that has been observed with a high incidence in the Lidia breed is osteochondrosis [57]. It is a degenerative process of the joint surfaces, widely described in horses and in bait cattle of other breeds, with few studies in fighting bulls to know if it could influence the mobility of the animal during the show [27].
\nRA is a metabolic disease that settles in the rumen and is produced by the ruminal fermentation of large amounts of nonfibrous carbohydrates, such as starch and sugars, which lead to the production of high amounts of volatile fatty acids (VFAs) and lactate, which accumulate in the rumen and cause an abnormal reduction in rumen pH [58]. Ruminal epithelial cells, not protected by mucus, are vulnerable to chemical acid damage [59], and this decrease in ruminal pH together with high concentrations of VFAs causes ruminitis, erosions, and ulcerations of the ruminal epithelium. In turn, abnormal thickening of the stratum corneum of the mucosa occurs due to accumulation of corneal cells with perturbations in their keratinization resulting in hyper and parakeratosis, observing partially pigmented ruminal mucous membranes [60, 61].
\nAmong the works carried out on the feeding management of the Lidia bull, the one carried out by Bartolomé [26] stands out because he observes 66.2% of the animals studied with ruminal pH values compatible with RA, of which 41.5% chronically (pH = 6.2–5.6) according to the classification of González et al. [62]. In addition, 70.7% of animals presented parakeratosis in the mucosa, and in 26.9% of bulls sampled, liver lesions were detected. In the same line, Lomillos et al. [27] reported a 43% reduction in the length of the ruminal papilla of bulls subjected to the finishing bait, added to an increase in the thickness of their mucosa, which approximately doubled the value obtained in the group of animals considered control, and maintained in pure extensive regime (Figures 5 and 6).
\nNormal papilla of extensive animal.
Thickened and shortened papilla of finished bulls [27].
In this context, the decrease in rumen pH predisposes the epithelium to become fragile and loses its ability to act as a barrier between the ruminal environment and the blood, which predisposes the appearance of continuity solutions, which allow the passage of microorganisms toward the bloodstream and the consequent risk of suffering sepsis for the animal [60]. Among others, Fusobacterium necrophorum and Corynebacterium pyogenes, are bacteria often carried to the liver through the portal vein, and there they begin infection and abscess formation, which compromise their metabolic capacity [5]. From the liver, they can go to the peritoneum, generating peritonitis, and sometimes they can go to the lung, heart valves, kidneys, joints, and so on [63]. In this sense, García et al. [12] recorded abscesses at the liver level in 4% of the studied bulls and hepatic-diaphragmatic adhesions in 21% of cases that extended to the pulmonary pleura, confirming, after culture, Fusobacterium necrophorum as the main causative agent of lesions.
\nAt the same time, the intense finishing feeding management based on the use of high amounts of carbohydrates is a predisposing cause of hoof lesions such as the lameness by diffuse aseptic pododermatitis observed in the animals as an excessive growth of the hoof [60] widely described in Lidia cattle [25, 29] and detected with a prevalence of 28% in the fought animals [12].
\nAccording to Nocek [64], the relationship between RA and laminitis seems to be associated with hemodynamic alterations of peripheral microcirculation. During acidosis, as a consequence of the decrease in ruminal pH, a process of bacteriolysis takes place in the rumen, releasing vasoactive substances (histamine and endotoxins), which are absorbed through the damaged rumen wall and cause vasoconstriction and dilation, which destroy microcirculation at the level of synovial joints and chorionic tissue of the hoof [65, 66]. The combination of high concentrations of histamine in areas of terminal circulation [67], the increase in digital blood flow and high blood osmolarity induce an increase in blood pressure inside the animal’s hoof, producing a serum exudate, which results in edema, internal hemorrhages from thrombosis, and finally, the expansion of the chorion, causes intense pain [60, 64]. The disease presents with signs of lameness, excessive growth of the hooves, and the appearance of dark lines or bands on the surface of the hooves, a consequence of the ischemia generated by vascular damage and edema [68]. At present, lameness is treated with anti-inflammatories, and the hoof overgrowth is usually remedied in livestock by a functional cut of the hoof, using the cattle crush facilities to immobilize the animal.
\nIt seems clear that the RA generated after the intensive bait and the pathological processes to which it predisposes or directly causes, affects the performance of the bull in the arena in the form of physical decline of the animal that hinders its ethological and physical performance [12, 20, 26, 69]. Therefore, it is of great importance to explore possible solutions or prevention strategies by designing a new food management.
\nTo control the process, in principle, it would be enough to reduce the amount of nonfibrous carbohydrates provided with the diet, but this measure would lead to a decrease in the rations’ energy level, with the consequent delay in the fattening of the bull and the consequent economic losses.
\nIn the case of the final bull bait, improved rationing and feeding management could have a considerable impact on rumen pH stability. Adapting the ruminal environment by slowly and gradually changing from one forage feed ration to another concentrate would stimulate the development of the rumen papillae and the growth of the lactic acid transforming flora [5], in such a way so that a greater amount is metabolized and the mucosa of the rumen can absorb a greater amount of generated VFAs. This adaptation of the mucosa to concentrated rations takes approximately 4–6 weeks [64] and changes in microflora about 3 weeks [70].
\nThe adoption of the mixed total ration type feeding system, better known as “unifeed” carriage (Figure 7), widely used in dairy cattle, ensures a balanced consumption of concentrate and forage, which is a very important advantage. In this way, it is possible to increase the energy density of the rations by reducing the risk of digestive problems [71]. In fact, in recent years, this type of food management has begun to be incorporated into the Lidia farms, mainly in farms located in the south of the peninsula, later extending through Madrid and Salamanca [72].
\nSmall format “unifeed” mixer truck, adapted to Lidia cattle (BIGA).
In this sense, the contribution of compensated and high fiber rations through the use of “unifeed” mixer cart during the bull bait does not generate a pH decrease below the physiological limits, as shown in Graph 1 that describes the pH ruminal of bulls fed following this pattern of food management for a month [31]. However, it is not clear that this handling is the solution to the RA of the bull since the use of these mixing machines during the entire bait period, which usually lasts between 3 and 9 months, can generate lesions in the morphology of the papilla ruminal (decrease in length and thickening of the mucosa) similar to those found in animals fed through traditional feeding management. In addition, the feeding time generates a negative effect on the severity of the lesions, with the animals fed for more than 6 months being the ones with the greatest lesions at the level of the rumen mucosa [27].
\nAnother strategy to prevent RA is the use of additives both chemical and microbial. Among the first are buffer substances such as bicarbonate, alkalizing agents such as magnesium oxide, or adjuvants such as bentonite, which can help fight RA because it absorbs part of the volatile fatty acids at the ruminal level [5, 65, 73, 74]. The most commonly used microbial additives to combat RA are yeast extracts and live yeasts. These microorganisms help maintain ruminal pH by stimulating the growth of cellulolytic bacteria and lactic acid users, preventing their accumulation in the rumen [75].
\nMuscle weakness syndrome, which involves motor incoordination and transient loss of standing and balance, all encompassed under the common term of “falling syndrome,” has been worrying different authors for almost a century [76]. The frequency with which this problem occurs in the arena had not become worrisome until the beginning of the last century, from the being of 1930 when the manifestation of the problem became general and the falls were more frequent and alarming [77], reaching incidence percentages in the most critical decades close to 99% [78] or 98% [26] of the sampled animals. It affects both males and females and specimens of all ages: bulls, calves, and cows [79, 80]. It is observed in individuals of different livestock farms, regardless of their weight, the category of the arena where they fought, the distance from its farm of origin [77], and, additionally, within the same livestock, the incidence of this problem can be very diverse.
\nDespite recent research work done in this regard, the falling syndrome of the brave bull is an issue in which consensus is not yet perceived. The theories that have come to light in order to explain the etiology of the syndrome have been very numerous and varied, without any of them providing definitive conclusions to date. The simplest attributes the problem to physical reasons such as transport trauma and intentional fraud, while others, more complex, consider that the origin of the syndrome is genetic, due to the inheritance of a gene that determines the fall [81]. However, given the appearance of the problem in cattle ranches whose original genetic distance is very wide, it is logical to assume that the appearance of this syndrome must be influenced by the action of the environment, within which food management, in addition to other factors, such as the health status of the livestock itself would play a very important role.
\nNowadays, in view of the different studies carried out, it is possible to think that the falling syndrome is a multicausal problem, where we can observe some predisposing causes, such as the genetic endowment, the characteristics of the transport, the physical demands of the fight, the effect of the puya and the banderillas, the lack of functional gymnastics, nutritional deficiencies, and other more determinants, such as the possible pathological, circulatory, nervous, metabolic, endocrine, genetic, or ethological causes [76].
\nOn the other hand, the bull is by nature a sedentary animal. In the last year of life, he is transferred to small enclosures where his chances of exercising naturally are limited and the energetic components in his diet are increased. Although cattle are not considered an athletic species, the bull is subjected to tremendous exercise in the arena, lasting approximately 20 min, maintaining a physical and metabolic effort of great intensity to which it is not accustomed [82]. These circumstances mean a lack of physical condition for the show.
\nThis muscle weakness, manifested in the falling syndrome, is projected in various acute muscle injuries associated with intense physical exercise and in chronic muscle injuries that may result from nutrient deficiencies of selenium and vitamin E [83]. On the other hand, Aceña et al. [84] demostrated the existence of a reduction in glycogen stored and very high concentrations of lactic acid in the muscles at the end of the fight, results that indicate the existence of muscle fatigue due to physical exercise in an anaerobic situationss. Similarly, a high correlation has been observed between the main parameters indicative of metabolic acidosis (HCO3−, lactate, and low blood pH) and respiratory acidosis (PCO2) with the falling syndrome [69].
\nTherefore, it is essential to subject the animals to a physical preparation and adaptation to the fight. In fact, in recent years, the number of farmers who seek to achieve adequate physical condition in their animals has increased, through an empirical training program along a running track or by moving them in the same enclosure where they normally live.
\nThere are few studies on the effect of training on the physiology of the bull [85, 86, 87]; however, we can state that training potentially increases athletic performance, as can be deduced from muscular and blood metabolic adaptations [88, 89]. It has been observed that training favors the β-oxidative metabolic pathway of fatty acids (oxidative metabolism) prevailing over the glycolytic pathway, requiring a protocol of at least 6 months to increase its antioxidant capacity [89, 90].
\nIn addition, this training would increase the muscle mass of the animal favoring physical performance [91]. To train, and for the result to be effective, great care of the diet should be taken into account since, in the finishing phase of the bulls, it is intended that the animal’s body weight increases and that the training will serve to increase muscles and adapt the cardiovascular system to an aerobic exercise. With this training management, it is being pursued that the bull endures the fight better, increasing its mobility while achieving greater lung capacity and, therefore, a greater chance of recovery, after efforts made in the first moments of fight.
\nWith training, physical capacity is enhanced, stimulating the body’s level of work above normal. These animals have a great capacity for adaptation and although at the beginning of the training they show signs of fatigue and body loss, this is followed by a phase of recovery/adaptation and maintenance of body weight.
\nA basic training program would consist of three sessions per week, within a total period of 5–6 months, depending on the date scheduled for the fight. A group of animals, with a variable number of bulls, around 12, are forced to move for approximately 3 km, accompanied by horsemen.
\nIt usually begins with a weekly session, increasing the pace until reaching 3 sessions/week in the second month. The intensity is progressive, each session begins with the first minute to the step, to warm the animals, increasing the pace until they are trotted or lightly galloped, to return to the initial point in a progressive cooling. The orography of the land is usually flat, but there are farmers who prefer to exercise the cattle on sloping terrain to increase the intensity of the session. This training is interrupted approximately 15 days before the fight [92].
\nEach breeder has been carrying out a particular training protocol, adapted to their availability of time and cowboys, the number of animals they intend to prepare, and the date of their fight. Generally, a more intense preparation is usually carried out with bulls whose destiny is first or second category arena. In turn, the orographic characteristics of the farm, its distribution of fenced areas, and its extension will have an important influence on the programmed exercise.
\nConsidering the high economic value of the Lidia breed animals, the number of farmers who establish a health management program in their livestock as a control system against infectious or parasitic diseases, and to increase fertility and pregnancy rates as well as to decrease mortality rates in new-born calves, is rising in recent years.
\nProblems related to infectious and contagious diseases represent the main source of economic losses. The pathogens that have tropism for the reproductive, respiratory, or digestive system stand out. Therefore, reproductive and respiratory alterations and neonatal diarrhea are the main problems we find in these cattle [93].
\nCurrently, there are several emerging diseases that could affect these animals during the last decade such as blue tongue, foot and mouth disease, or bovine spongiform encephalopathy, which have joined those that already have an eradication program in our country (brucellosis and tuberculosis), which require periodic official livestock checking on farms (Order DES/6/2011). On many occasions, the health problem itself is linked to a cumbersome legislation that hinders the transit of animals through the various communities of the national territory and between intracommunity countries such as Spain, France, and Portugal (Royal Decree 186/2011).
\nThe official campaigns of eradication of brucellosis and tuberculosis are based on hard controls of the herds and on the application of a legal regulation on these aspects that makes, in certain cases, the movement of animals from the infected cattle ranches, including sales for bullfighting, impossible [94]. It is essential to consider the peculiar factors of this cattle production system. One of them is the level of consanguinity within some farms with a very small number of individuals, which works against disease resistance. It is also necessary to consider the complexity of handling these animals, which coexist in extensive systems with species of different sanitary categories (hunting and/or wild) that could be reservoir for numerous diseases.
\nIn addition, cross-reactions with paratuberculosis (a widespread disease in the Spanish countryside) compromise the reliability of diagnostic analytical tests, posing serious problems when addressing eradication plans [95]. The fight against diseases, both endemic (tuberculosis and brucellosis) and emerging (bluetongue), to achieve eradication and control, will be one of the workhorses for the Lidia sector. This should not entail, in any case, any risk to the maintenance of the diversity of encastes and genetic lines that characterize this breed. Important and unique farms for their genealogy are being decimated by this cause, to the point of endangering the survival of certain encastes.
\nOne of the most valued and delicate body parts of the bull is its horns. They suffer a risk of deterioration, mainly in the last year of life, as a result of potential fights, friction, contacts, or blows with the ground, with trees, fencing, feeders, or the walls of the handling facilities [96]. Therefore, to protect the horns during the last year of animal live, a fiberglass bandage is placed on the horns, easy to handle, porous and that hardens quickly by polymerization with water, providing good consistency (Figure 8). The technique consists of immobilizing the animal in the restraining facilities and wrapping the horn with this bandage to protect it from any aggression or friction. The distal part of the horn is reinforced in many cases with metal tubes or similar hard materials, in order to reduce the wear of the apical zone [97, 98].
\nLidia bull with protected horns.
The horn is increased in thickness by the sheath, and the end of the horn is blunt, which decreases the effect of the lesions of horns between animals by 90% and, in addition, improves their handling for vaccinations, deworming, and other treatments, due to the risks of deterioration of the defenses when the animals pass through the handling facilities minimized [99]. In spite of the obvious advantages of the sheathing mentioned above, and the answer to many questions about the influence of this management practice on the structure and corneal anatomy and the ethological performance of the animal in the arena provided by Alonso et al. [100], there is still some controversy about its usefulness.
\nLidia cattle production presents unique characteristic that requires farmer and veterinary knowledge about the particularities of these animals and its management. The Lidia production sector, from its origins, has been adapting to the new times making use of the most current technological advances. In this way, the feeding system, selection criteria, and reproductive techniques have been modified, driving the need for a modernization of the medical and management practices. However, there are difficulties associated with the breeding, either because of the temperament of animals that increase the difficulty in handling, as well as to the predisposition to present diseases that greatly affect the animal, such as the ruminal acidosis, the falling syndrome, and some health problems that it shares with other extensive bovine cattle.
\nWe would like to thank Mr. Logan Scott for his revision and edit of the English translation.
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