A stand life of 3 to 5 years is common in the Central Valley of California and other warm, long growing-season areas of the Southwest. A stand life of 5 to 7 years is common in much of the Northwest, and some alfalfa stands remain in production in excess of 10 years. As suggested by the principles outlined above, it is unwise to rely solely on glyphosate applications for weed control throughout the life of a transgenic alfalfa field. This practice would encourage weed shifts and resistance, and over time weed control would diminish in most cases. Once an herbicide is rendered ineffective as a result of resistant weeds, its usefulness as a weed control tool may be greatly diminished. After a resistant weed population has gained a foothold, it is practically impossible to eliminate it due to the presence of a weed seedbank.Most alfalfa producers apply an herbicide to alfalfa during the dormant season to control winter annual weeds that infest the first cutting. It is strongly recommended that growers not rely solely on glyphosate for their winter weed control program for the duration of the stand. They should rotate to another herbicide or tank mix at least once in the middle of the life of a stand, and perhaps twice if the stand life is over 5 years .Fortunately, cannabis growing systems all of the herbicides currently registered in alfalfa—and there are several to choose from—havea different target site of action than does glyphosate. The soil-residual herbicides applied during the dormant season to established alfalfa [such as hexazinone , diuron , metribuzin , and pendimethalin ] would be appropriate herbicides for a rotation or tank-mix partner.
The rotation herbicide or tank-mix partner of choice depends on the weeds present in the field and their relative susceptibility to the herbicides. Paraquat is another candidate for rotation, but paraquat, like glyphosate, lacks residual activity and is applied late in the dormant season. By rotating paraquat with glyphosate, growers could potentially be selecting for early-emerging weeds that may be too large to control at the typical timing for these herbicides. In addition, they could be selecting for late emerging weeds that germinate after the application. Rotate Herbicides Early in Stand Life So Glyphosate Remains Effective Weed control during the last year of an alfalfa stand is often challenging because the stand is typically less dense and competitive and also there are fewer herbicide options from which to choose. There are significant plant-back restrictions associated with many of the soil-residual herbicides used in alfalfa, so glyphosate is a good choice for controlling weeds in the final year of RR alfalfa field. The preference to use glyphosate in the final year of an alfalfa stand underscores the importance of rotating herbicides earlier so that glyphosate will remain effective and continue to control the majority of the weeds. Consider a Soil-Residual Herbicide for Summer Annual Weed Control Summer annual grass weeds such as yellow and green foxtail , barnyardgrass , cupgrass , and jungle rice , and less frequently, broadleaf weeds like pigweed or lambsquarters , can be problematic in established alfalfa. These weeds emerge over an extended time period whenever soil temperatures and moisture are adequate, typically from late winter or early spring throughout the summer.
Weeds may emerge between alfalfa cuttings, so several applications may be necessary in California’s Central Valley for a foliar herbicide without residual activity like glyphosate to provide season-long control. Multiple applications of a single herbicide during a season is cited as promoting weed resistance.Therefore, growers should not rely solely on glyphosate for summer grass control for multiple seasons. It remains to be seen how many applications of glyphosate will be required for season-long summer grass control. In some of the long growing season areas of California, as many as two to three applications per season may be needed in older, thinner stands. Rather than making multiple applications of glyphosate, a better approach may be to apply a pre-emergence soil-residual dinitroaniline herbicide like trifluralin or pendimethalin , or possibly EPTC , and follow up with glyphosate later in the season as needed for escapes. Not only is this approach more in line with management practices to avoid weed shifts and resistance, but it may be more economical as well, compared with multiple applications of glyphosate. The practice of rotating herbicides or applying tank mixtures is recommended for both dormant applications aimed at winter annual weeds and for spring/summer applications intended to control summer annual weeds. For example, rotating to hexazinone for winter annual weed control for a year does nothing to prevent weed species shifts or the evolution of resistance in the summer annual weed spectrum. Herbicides for summer annual weed control should be rotated as well. Frequency of Rotation Depends on Weed Species and Escapes There is no definitive rule on how often herbicides should be rotated. Our suggestion to rotate or tank mix at least once in the middle years of the life of a stand may need to be modified depending upon actual observations of evolving weed problems. The key point, which cannot be overemphasized, is the importance of diligent monitoring for weed escapes.
Producers should stay alert to the appearance of weed species shifts and evolution of resistant weeds. If the relative frequency of occurrence of a weed species increases dramatically, chances are that it is tolerant to glyphosate and immediate rotation of herbicides or a tank mix is advised. If a few weeds survive among a weed species that is normally controlled easily with glyphosate, it could be an indication of weed resistance, assuming misapplication and other factors can be eliminated as possible causes. However,in these situations, it is imperative to prevent reproduction of a potentially resistant biotype. Treat weed escapes with an alternative herbicide or other effective control measure.Reservoir operation is based on a series of rules that determine the amount of water that is stored and released under different system conditions. These reservoir operation rules determine how reservoir water is allocated during periods of droughts, normal climate, or wet climate. Methods used for the optimal operation of reservoirs can be classified into two main categories: classic algorithms, and evolutionary and metaheuristic algorithms. Although classic methods are relatively simple, they have limitations such as the possibility of not achieving global optima, convergence to local optima, and being hindered by high dimensionality . Evolutionary and metaheuristic algorithms are generally inspired by natural phenomena. One of the advantages of the latter algorithms is that they generally converge to near-global optima for any well-defined optimization problems. In addition, they can solve multiobjective problems. The main disadvantage of evolutionary and metaheuristic algorithms is the long processing time needed to converge to a solution. This has led many researchers to search for and produce newer, dry marijuana computationally more efficient, evolutionary and metaheuristic algorithms. Many classic and metaheuristic optimization techniques have been recently developed and applied in various aspects of water resources systems such as reservoir , hydrology , water-resources management , irrigation , power plants , structures , distribution networks , aquifers , infrastructures , and algorithmic developments . None of these works dealt with the application of the weed optimization algorithm in water resources systems, or, in particular, to solve reservoir optimal operation. Concerning the application of evolutionary and metaheuristic algorithms to reservoir operation, Esat and Hall resorted to the genetic algorithm to optimize reservoir operation for energy production and water for irrigation. Oliveira and Loucks employed the GA to evaluate rules concerning the operation of multi-reservoir systems. Sharif and Wardlaw implemented the GA in several multi-reservoir systems and obtained solutions very close to those calculated with dynamic programming . Ahmed and Sarma compared the GA’s performance with that of stochastic dynamic programming and reported that the GA was superior in calculating desired solutions for optimizing multiobjective reservoir operation. Tospornsampan et al. applied the simulated annealing algorithm to optimize the operation of a multi-reservoir system. Kumar and Reddy implemented the ant colony optimization metaheuristic algorithm to optimize the operation of a multiobjective reservoir.
Bozorg Haddad et al. introduced the honey-bee mating optimization metaheuristic algorithm to reservoir operation. Bozorg Haddad et al. used the HBMO and nonlinear programming for the design and operation of a single and multiple reservoir system. Wang et al. introduced the multi-tier interactive GA for long-term optimization of reservoir operation. Jothiprakash et al. used the GA and stochastic dynamic programming for the operation of a five-reservoir system in Kodaiyar, India. Ostadrahimi and et al. calculated operation rules of a multi-reservoir system using the multipopulation approach in multi-swarm particle swarm optimization algorithm. Ngoc et al. applied the constrained GA to derive optimal operation principles of multiobjective reservoirs. Bozorg Haddad et al. applied the bat algorithm to determine the optimal operation of reservoir policies for Karoon 4 and four-reservoir system in continuous domain. Bozorg Haddad et al. used the water cycle algorithm to determine the optimal operation of reservoir policies of the Karoon 4 reservoir. They also compared the results of the WCA with those obtained with the GA and NLP. The following works have implemented the WOA in a variety of engineering optimization problems, but not to reservoir operation as of yet. Mehrabian and Lucas introduced the WOA. They solved two engineering problems and compared the results with the GA, memetic algorithm , particle swarm optimization algorithm, shuffled frog leading algorithm , and the simulated annealing algorithm. Their results showed a relatively superior performance by the WOA. Mehrabian and Yousefi-Koma applied the WOA to optimize the location of piezoelectric actuators on a smart fin. Mallahzadeh et al. tested the flexibility, effectiveness, and efficiency of the WOA in optimizing a linear array of antenna and compared the computed results with those of the PSO algorithm. Sahraei-Ardakani et al. used WOA to optimize the generation of electricity. Roshanaei et al. applied the WOA to optimize uniform linear array used in wireless networks, such as commercial cellular systems, and compared their results with those from the GA and least mean square . Mallahzadeh et al. used the WOA to design vertical antenna elements with maximal efficiency. Krishnanand and Nayak compared the effectiveness of the WOA, GA, PSO algorithm, artificial bee colony , and artificial immune by solving five basic standard mathematical problems with multivariate functions. Zhang et al. used heuristic algorithm concepts for developing the WOA. They introduced the WOA with crossover function and tested the new algorithm on standard mathematical problems and compared the results of the developed WOA with those of the standard WOA and PSO. Sharma et al. used the WOA to solve dynamic economic dispatch . Their results showed that the WOA algorithms reduced production costs relative to those obtained with the PSO and AI algorithms and differential evolution . Jayabarathi et al. implemented the WOA for solving economic dispatch problems. Kostrzewa and Josiński introduced a new version of the WOA and tested their algorithm on several standard mathematical problems. Abu-Al-Nadi et al. applied the WOA for model order reduction in linear multiple-input-multiple-output systems . Sang and Pan introduced the effective discrete WOA to solve the problem of flow shop scheduling with average stored buffers, and compared their results with the hybrid GA , hybrid PSO algorithm , and the hybrid discrete differential evolution algorithm . Saravanan et al. applied the WOA to solve the unit commitment problem for minimizing the total costs of generating electricity. They compared their results with those calculated with the GA, SFLA, PSO algorithms, Lagrangian relaxation , and the bacterial foraging algorithm. Barisal and Prusty used the WOA to solve economic problems on a large scale with the aim of minimizing the costs of production and transfer of goods subject to restrictions on production, market demand, the damage caused to goods during transportation, and to alleviate other calamities. The reviewed literature established that the WOA has not been applied to optimize reservoir operation. This study introduces the WOA to the field of reservoir operation and compares its results with those obtained with the GA, linear programming , and NLP. Several comparative examples are solved to measure the performance of the WOA against those of well-established optimization methods. Establishing hedgerows of native perennial grasses, shrubs, or trees around farms requires long-term planning and care to ensure success. This effort includes developing a farm plan; selecting, analyzing, designing, and preparing the site for planting; choosing appropriate plants; and initiating a plan for weed and rodent control.