yield management case study

Hotel Yield Management: Pricing and Reservation Strategies to Maximize Revenue

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• Published: 11 Mar, 2024
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The hospitality industry is highly competitive, so it’s crucial to make the most of every single room. This is where the magic – or should we say science? – of yield management comes into play. It's all about using data to maximize sales, keep your property full, and ultimately boost profits.

This article explores yield management techniques and describes various pricing and selling strategies that will help you best manage your room inventory. 

What is yield management in hospitality?

Yield management  is a strategy for maximizing revenue from room bookings by adjusting prices based on factors such as demand, seasonality, and booking patterns. It involves understanding, anticipating, and influencing consumer behavior, as well as finding the optimal balance between occupancy  and rate.

In other words, yield management is about understanding what customers want and when they want it, varying pricing and sales approaches accordingly to make as much money as possible.

Yield management originated in the aviation industry in the 1970s as a strategy to increase revenue by dynamically adjusting prices based on demand fluctuations and customer segmentation. This concept evolved over time and penetrated other industries, notably hospitality , where hotels began employing similar techniques to boost their revenue.

Today, multiple industries rely on sophisticated algorithms and data analysis to forecast demand and set prices accordingly, aiming to achieve the highest possible yield from available inventory.

Besides maximizing revenue, other benefits of yield management include

• tight control over inventory that leads to increased efficiency, better resource allocation, and minimization of both overbooking  and underbooking;
• competitive advantage over other businesses that might not be implementing such sophisticated strategies; and
• better market and customer understanding that allows for more personalized service and marketing.

Before we dive deeper into yield management intricacies, let’s first briefly clarify its place among other hospitality concepts.

Yield management vs revenue management

Yield management  and revenue management  are often used interchangeably in the hospitality industry, but they have distinct focuses, despite their overlapping goals of maximizing income.

Yield management  primarily focuses on balancing prices and availability  to maximize the revenue generated from inventory. The key here is managing the yield  or return from each room, which involves adjusting prices based on demand forecasts.

Revenue management  takes a broader approach. While it includes yield management methods, it also encompasses a wider range of activities contributing to the overall revenue. This can include not just selling rooms but also

• managing distribution channels ,
• upselling  services,
• creating additional revenue sources,
• using various marketing strategies to capture more customer segments, and
• implementing personalization techniques  to attract more guests by enhancing their experience.

In summary, while yield management focuses on maximizing the revenue from each unit of inventory  by adjusting prices and sales based on demand, revenue management takes a holistic view of all revenue streams  and strategies to increase the ultimate income.

Yield management vs traditional pricing

The main difference between yield management and traditional pricing strategies lies in the usage of advanced data analysis, forecasting techniques, and automation.

For example, traditionally, the product rate equals the costs involved plus a seller’s margin (so-called cost-based pricing ). Then the rare times when prices are changed, it’s usually because of the season or substantial market fluctuations. Meanwhile, yield management employs  dynamic pricing , constantly adjusting rates based on the analysis of multiple factors.

Also, unlike the traditional approach, yield management closely intertwines pricing decisions with inventory management, setting various booking restrictions (e.g., minimal or maximal length of stay ). In addition, traditional pricing doesn’t differentiate between customer segments as precisely as yield management does.

In our comparisons, we’ve already started describing the main yield management elements, so let’s take a closer look and discuss them in more detail.

Key components of yield management

Hotel yield management is based on  several statements  that enable revenue managers to develop and apply various pricing strategies.

• Hotels have limited capacity as there’s a fixed number of rooms to sell.
• Hotel inventory is perishable (you can’t sell the room for yesterday) .
• Different people are willing to pay different amounts for the same room under different conditions.

Given this knowledge, yield management encompasses various techniques and approaches that make its strategies possible. Here are the main ones.

Customer segmentation

Not all customers will pay the same price, so you have to figure out how to sell more expensive rooms to people willing to spend more – and find the best deals for other guest categories.

To define these categories or segments , hotels scrutinize their guests’ data and booking patterns. The main hospitality market segments are

• transient travelers – those who travel without any group and book at public, non-negotiated rates or so-called Best Available Rates (BARs) ;
• groups – multiple people who travel together and usually make reservations long in advance at discounted rates;
• corporate travelers – those who travel for business and book through their organization or travel management company , often at prenegotiated rates ;
• wholesale – intermediaries like bed banks  or tour operators  booking in bulk with a discount; and
• SMERF – Social, Military, Educational, Religious, and Fraternal travel groups that also receive special, preferential booking conditions.

You can then break all these major segments down into smaller groups for more granular analysis. Numerous factors can be taken into account, like demographics (traveler age, gender, marital status, nationality, etc.), trip purpose ( leisure  or business ), or booking channel (online or offline, direct or indirect).

Travelers in each category show similar behavior patterns such as sensitivity to pricing, amenity choices, booking window , percentage of cancelations  or no shows , etc.

Knowing all this data, it makes sense to treat each segment differently. You can tailor your services, marketing efforts, and, most importantly, pricing to meet each travel group's needs, attract more guests, and ultimately increase revenue.

After hotels segment their customers, they try to predict the demand of each group as accurately as possible.

Understanding demand

Customer demand constantly fluctuates based on various factors. Seasonality, especially for properties located at popular vacation destinations, is one such factor. So are big holidays like Christmas or important local events like sports competitions, trade shows , or festivals.

Demand forecasting  lies at the very core of yield management. Hotels use past information and knowledge about current trends and future events to predict booking patterns and adjust prices accordingly .

Adjusting prices

Just like customer demand, hotel rates aren’t static. They can be adjusted based on various incoming data, mainly related to supply and demand. Simply speaking, hotels change their prices depending on how many people want to book. If, for whatever reason, many people want a room, the price goes up. If not many people are booking, the price decreases to maximize room sales.

In addition, room rates can be negotiated so that preferred distribution channels or frequent customers get discounted rates. So revenue managers constantly play with prices to secure the best deals and attract maximum business to the property.

Implementing technology

Everything we described above will not be efficient or accurate if done manually. You simply can’t obtain granular insights into customer segments, predict demand for each group in the long term with all factors considered, or juggle thousands of room rates without sophisticated software systems.

There are numerous tools designed to streamline hospitality operations. Some are more comprehensive, e.g., property management systems  help control all the activities in the hotel, while others are responsible for a specific task, for example, booking engines  capture and process online reservations.

Today, these tools are becoming more powerful than ever as they employ advanced AI technologies. Finely tuned machine learning models allow revenue managers to process previously unimaginable amounts of data. ML-powered platforms can simulate various what-if scenarios to choose the best-performing option, incorporate numerous variables to capture the slightest demand fluctuations and customer behavior patterns, and come up with prices that will optimally match the traveler's willingness to pay.

For example, we at AltexSoft have hands-on experience in developing an ML-based price prediction tool  for a property owned by Rakuten Travel, a Japanese booking platform. To find an optimal price-demand ratio, we built two ML models: One used competitor pricing data to forecast the market ADR (average daily rate) , and the other predicted occupancy based on the price levels. This way, revenue managers could see how different rates impact occupancy and choose the most revenue-beneficial scenario.    

Yield management is mainly supported by RMS – revenue management software . It automates all the activities we discussed, calculating the best rates for predicted demand levels across segments and suggesting the optimal course of action. It also determines which booking requests comply with your current reservation policy (we’ll talk more about it in the next section) and must be approved.

In addition, RMS generates detailed reports, offering granular insights into your property performance – another essential element worth mentioning.

Performance monitoring

You can’t control what you can’t measure. So you must calculate and monitor various KPIs to understand your position in the market, the effectiveness of your strategies, and areas that require improvement.

Besides the general overview of hotel KPIs , we have a series of detailed posts on the most important ones such as occupancy rate , average daily rate (ADR) , and revenue per available room (RevPAR) , so check them out for more insights on how to approach these metrics.

Also, take a look at our case study on AltexSoft developing a business intelligence tool  that helped monitor vacation rentals’ performance and predict their occupancy rates.

Now that we’ve discussed what yield management is about, let’s finally talk about the different strategies you can apply to build up your returns.

Yield management strategies

As mentioned above, yield management's main challenge is finding an optimal balance between room rates and sales volume to achieve the maximum total yield. With this purpose, revenue managers apply various demand-based pricing  and reservation  strategies that aim to maximize occupancy – at the highest rates possible.

Yield management pricing

Obviously, pricing strategies involve managing room rates. Many hotels limit their pricing activities to developing separate rate plans  for different room types  and adjusting them seasonally. But there’s more you can do.

Note that most pricing strategies involve offering incentives to attract more guests. You must carefully calculate discounts to ensure that increased occupancy results in higher revenue despite lower margins.

Segmented pricing . You can offer different prices to different customer segments. For example, you can provide special rates for large bookings, such as group tours, weddings, conferences, or other events. You can also use other segmentation criteria such as age (e.g., discounts for seniors or students) or membership in loyalty programs (which not only drives direct bookings but also fosters customer loyalty and repeat business.)

Length of stay pricing . Prices are adjusted to the duration of a guest's visit. Longer stays might be incentivized with lower rates per night to encourage guests to extend their bookings, which ensures more stable occupancy rates.

Allotment . You can offer discounts to travel wholesalers  or resellers (e.g., bed banks, tour operators, event organizers, etc.) and sell rooms in bulk.

Package pricing . One more strategy is to package rooms with other services such as meals, spa treatments , or local tours . These packages can then be priced more attractively compared to purchasing each component separately, appealing to guests looking for convenience and added value.

Non-refundable rates.  Offering non-refundable rates at a discount can attract price-sensitive customers while providing the hotel with guaranteed revenue, even if the guest cancels.

Early bird and last-minute pricing . Offering discounts to guests who book well in advance (early bird) or close to the date of stay (last minute) can help manage occupancy rates by securing reservations early or filling up remaining rooms.

Dynamic pricing . Dynamic pricing is a more complex and technology-demanding approach. It involves constantly adjusting room rates based on various factors such as demand, time of booking, market trends, and competitor pricing. To make the price adjustment process really dynamic, hotels implement advanced data analytics systems that process massive amounts of assorted data in real time  and automatically  respond to changes in demand.

You can read more about ML-based dynamic pricing  and the role of ML in revenue management  overall in our dedicated posts.

Open pricing . Open pricing takes the previous strategy a step further. It offers an even more flexible and personalized approach by setting revenue-beneficial rates independently for different distribution channels or market segments.

For example, while a traditional approach implies closing out discounted channels (e.g., OTAs) in periods of high demand, open pricing suggests using a range of flexible price points for different room types, customer segments, booking channels, etc.  

Visit our detailed overview of the open pricing strategy  to learn more.

Yield management reservation strategies

The second category of yield management strategies doesn’t deal with price-setting. It’s about developing specific r eservation policies that encourage longer stays or increase occupancy in other ways.

What’s crucial here is to carefully track turnaway business reports to see if your restrictions actually lead to increased revenue. You must balance the potential revenue gains from longer stays against the risk of turning away potential guests seeking shorter stays.

Also, keep in mind that guests who have a reservation refused might not be too happy about it, so monitor customer sentiment  closely and pay attention to how  your policies are communicated to the public.

Minimum length of stay (MinLOS).   Consider requiring  guests to book a minimum number of nights , especially  during peak periods or special events. This ensures higher occupancy levels for a continuous period and maximizes revenue during high-demand times.

For example, you might require a minimum 3-night stay during a major holiday weekend or festival, preventing guests from booking only the most in-demand night.

Maximum length of stay (MaxLOS). Conversely, you can set a maximum stay limit to prevent long-term bookings that could block shorter, more profitable stays, particularly in periods of fluctuating demand. This restriction can also be used to limit use of discounted rates.

Closed to arrival (CTA).  Under this strategy, hotels may restrict guests from checking in on certain days. You can implement this strategy to lighten the workload of the front desk and housekeeping teams on a busy day or when  the arrival day would start a stay that conflicts with other booking rules.

For example, if your hotel is mainly busy on weekends, you can set a CTA restriction for Saturday bookings to encourage Friday to Sunday reservations. Or suppose you have an upcoming local event. In that case, you can restrict bookings on that day to promote reservations starting before the event day, avoid overload, and ensure smooth property operations.

As an expansion of CTA, you can set so-called close out  or block out  periods when you don’t accept reservations from certain distribution channels or for a specific duration of stay because you count on more profitable deals, for example, prioritizing direct bookings over indirect ones or longer stays over one-day visits.

Closed to departure (CTD) . Similar to CTA, this restriction prevents guests from checking out on specific days. It's often used to optimize room availability for incoming guests and ensure continuous occupancy.

Overbooking . To counteract the effect of no shows and cancellations, hotels might overbook rooms, anticipating that a certain percentage of guests won't arrive. This strategy requires careful management to avoid negative guest experiences if the hotel cannot accommodate all bookings.

Last room availability. To support last minute reservations for business travelers from your corporate partner, you can set aside an agreed number of rooms at a prenegotiated rate.

Other rules and restrictions . Depending on the booking patterns of your hotel, you can set other reservation requirements or incentives. For example, a Saturday-night stay may be required to receive a discounted room on Monday. Or, just like a supermarket's “buy 2, get 1 free” deals, you can offer a free stay (or other incentives) for booking a certain number of nights.

These strategies can be used alone or in combination, depending on the hotel's specific goals, market conditions, and customer segments. Again, the critical point you must keep in mind when developing your yield management strategy is that your goal is to maximize revenue by balancing price, occupancy, and, of course, a positive guest experience.

Yield or revenue?

The attitude of industry experts toward yield management has been changing over the last decades. In its early days (1980s-1990s), it was seen as an innovative and effective pricing strategy to increase property profitability.

• Boosting Your Bottom Line with Yield Management
• The Basics of Yield Management
• Yield management: an overview
• Hotel Front Office Management Third Edition

However, the development and adoption of analytical tech solutions enabled a more holistic approach aka revenue management. So in the 2000s, the terms yield management and revenue management started to be used interchangeably, and modern hospitality professionals even tend to consider yield management limited, ineffective, and largely obsolete.

So employing diverse revenue management techniques – including but not limited to yield management – offers a bigger potential to optimize the financial results of a business.

• Hotel Front Office Management Fifth Edition
•  Revenue Management for the Hospitality Industry
• Hospitality Revenue Management

As we mentioned above, beyond just selling rooms, revenue management oversees all potential revenue sources, such as food and beverage, wellness products, conferences and events, and ancillary services. By optimizing each stream, a business can significantly increase its bottom line.

Revenue management also involves close control over distribution, developing an optimal mix of booking channels and sales partners – and adjusting it depending on the market situation. In addition, revenue management implies developing various marketing strategies to attract new guests and retain existing ones.

In summary, while yield management is an important tool for administering room-generated revenue and occupancy, a holistic revenue management strategy offers a more comprehensive framework that potentially leads to higher earnings by considering all aspects of the business.

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What is yield management? +9 strategies to boost your revenue

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By Rafael Blanes

Pricing can be a constant source of uncertainty for hoteliers, and many find themselves asking questions like: Should I raise my room rates or lower them? What are my competitors doing? How will my pricing decisions affect bookings?

While there are no “right” answers to these questions, there are trusted pricing strategies you can use to guide you in the right direction. A great place to start is yield management.

What is yield management?

Yield management is a dynamic hotel pricing strategy designed to produce the maximum revenue, or yield, from a set inventory of rooms. It’s about understanding and influencing traveler booking behavior and finding the optimal balance between occupancy and rate. 

Yield management is based on several key assumptions:

• Hotels have a fixed number of rooms to sell.
• Inventory is perishable and time-limited, meaning if a room isn’t sold on a given night, that opportunity is gone forever.
• Different people are willing to pay different prices for the same room under varying conditions.

The history of yield management 

Before reaching the hospitality industry, the concept of yield management originated in the U.S. airline industry. Following deregulation in the late 1970s, airlines took greater control over airfares and developed systems and technology to manage inventory and pricing to maximize revenue for each flight.  

Robert Crandall , former chairman and CEO of American Airlines, is credited with giving yield management its name, calling it “the single most important technical development in transportation management since we entered deregulation.”

Tactics employed by the airlines included offering discounted fares for low-demand flights and increasing fares for high-demand flights, as well as implementing purchase restrictions, length-of-stay requirements, fees for changing and canceling tickets, and higher costs for premium airline seats. Other industries quickly followed suit, including rental car providers and the hotel industry, and many of these tactics are still applied today.

Yield management vs. revenue management

The main difference between yield management and revenue management is that yield management has a narrower focus and is generally regarded as maximizing a hotel’s revenue through pricing and inventory controls. 

Revenue management is a broader term that encompasses market segmentation , demand forecasting, and more in-depth data analysis . An advanced revenue management strategy goes even further, factoring total revenue, costs, and profits into revenue decisions.

For lodging operators new to hotel revenue management or with limited time, yield management is the place to focus efforts. Once you master the basics, you can look at expanding your skillset.

3 examples of yield management in action

Yield management shapes consumer behavior everywhere. Here are a few examples:

• Dining out . Want to go out for dinner but on a tight budget? Take advantage of “early-bird specials” offered by local restaurants. These specials are designed to boost revenue by attracting different customers who are more price-sensitive outside of peak hours.
• Ordering a ride . If you use Uber, Lyft, or another ridesharing service, you may notice different prices for the same route at different times of the day. To maximize revenue (and encourage more drivers to serve the area), Uber implements “surge pricing” during times of high demand.
• Going to a show . To sell as many tickets as possible at the highest average price for every performance, theaters charge premiums for preferred seats and extend discounts for matinees, advanced purchases, and same-day tickets.

Yield management formula 

Calculating yield can help guide pricing decisions. A simple yield management formula is to divide earned revenue by potential revenue and multiply by 100. For example, if a hotel has ten available rooms to sell and its maximum rate is $299, its potential revenue is $2,999. If the hotel sells nine rooms at $149, its total revenue is $1,331, and its yield is 44%.

However, if demand is strong, a better strategy might be to increase the rate to $249. Even if the hotel sells only six rooms, it will still end up further ahead, with $1,494 in revenue and a 50% yield. Not to mention the cost savings in servicing six rooms instead of nine.

What is a yield management system?

Yield management can be time-consuming and complicated, involving a lot of data, numbers, and calculations. It’s especially difficult to execute well with limited resources. For help, lodging operators turn to tools such as revenue management systems (RMS), rate shoppers , and channel managers.

One tool that is particularly popular with smaller hotels is a yield management system like Cloudbeds’ Pricing Intelligence Engine (PIE) . PIE saves hotels time by using algorithms to automate tasks related to data collection, rate shopping, pricing, inventory management, and reporting.

 To maximize revenue and take advantage of market demand, hotels can set rules and alerts to adjust pricing and stay restrictions in response to changes in occupancy, competitor activity, and market conditions. All tools and data are displayed on an easy-to-use dashboard that is integrated with your property’s PMS and other core systems.

How Picton Harbour Inn earned an additional $60K in 6 months

Now that newer, easy-to-use solutions are accessible to all types and sizes of lodging businesses, revenue management is no longer limited to large hotel businesses that have the budget to hire a dedicated revenue manager. For example, the Picton Harbour Inn , a 3-star Prince Edward hotel, moved from pen-and-paper operations to Cloudbeds to manage its 29-room, 3-suite property. 

They started using PIE to gather market data, make better pricing decisions, and automate tasks to maximize yield. The results were felt immediately, with the owner estimating that the Inn made an additional 60K CAD in just six months. 

“We massively impacted our bottom line by reviewing occupancy reports in the offseason. For instance, we found that changing the pricing for a certain room type resulted in tens of thousands of incremental dollars.”

9 yield management strategies for boosting revenue

To increase revenue generated at your hotel, consider implementing the following yield management techniques:

1. Let data guide your decisions.

Start by setting rates for the coming year. Look at market conditions, historical data, booking pace, and unconstrained demand – the number of rooms you could sell if you had unlimited inventory. Monitor the impact of your pricing decisions and use them to guide future strategies.

2. Practice dynamic pricing .

Static room pricing is old-school. You’ll capture more revenue by using a variable pricing strategy – or increasing rates when demand is high and lowering rates when demand is low. This includes adjusting rates by time of year (season) and day of week, during conferences and events, and in response to changes in occupancy and market conditions.

3. Implement stay restrictions.

Instead of always having higher rates on busy nights, consider implementing stay restrictions such as closed-to-arrival (CTA) or a minimum length of stay (MinLOS) to boost occupancy on shoulder nights.

4. Track competitors’ rates.

Travel shoppers check out your competitors’ rates, and so should you. Decide how to price your property relative to your top competitors or compset . Use a rate shopping tool for easy comparison and a pricing intelligence tool to ensure you’re always priced where you want to be.

5. Vary your pricing.

Charge a higher price for your in-demand rooms that offer extra space, a nice view, a balcony, or a preferred bed configuration. Offer a variety of rate plans to appeal to a range of traveler types and budgets, including non-refundable rates, packages, weekend rates, and incentives for direct bookings and longer stays.

6. Understand your market mix.

Different market segments will be willing to pay different rates at different times. For example, business travelers book well in advance and expect discounted rates, whereas OTA distribution channels can be a good source of last-minute bookings. Monitor which customer segments are booking which room types and when, and adjust rates accordingly.

7. Consider an overbooking strategy. 

An overbooking strategy is when a property purposefully books its inventory beyond capacity in anticipation that a certain amount of guests will cancel or not show up. It can be a cost-effective strategy if implemented correctly but requires clear processes. 

8. Offer upsells.

Upselling is the practice of offering guests additional features or add-ons to enhance their stay for an extra cost. Not only does it add to the guest experience, but it can help hotels generate more revenue.

9. Don’t let those beautiful hotel rooms sit empty.

Move your perishable inventory and give your average rate a boost by providing guests an opportunity to upgrade their room prior to arrival or at check-in with an irresistible offer.

The benefits of using a yield management system

As effective as these strategies can be, it’s virtually impossible to do them well without the help of revenue management software. Here are just a few of the advantages of investing in a yield management system :

• Uncover more opportunities to drive bookings, revenue, and profits
• Monitor competitor rates in real-time to strategically position your pricing
• Set rules and alerts to be notified of important market changes
• Use a single, integrated dashboard to access all data, metrics, and tools
• Take advantage of better reporting, analytics, and visual data
• Integrate seamlessly with your PMS, booking engine, and channel manager

See how the Cloudbeds Platform can help with yield management. Book a demo

About Rafael Blanes

Rafael has spent over 15 years working in hospitality as a revenue manager as well as e-commerce, distribution, and sales before joining Cloudbeds as VP of sales. He lives on the west coast of Florida and enjoys spending time with his family sailing to new places on the open sea.

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Hotel Revenue Management in Italy, Case Studies by Xotels

It has been a few years since Xotels is operating in Italy now. And we wanted to share some of our successes in terms of hotel revenue management strategies with you.

Before entering into the market we were faced with the same hurdles as always. Owners and managers were concerned we did not know the market, and did not have first hand local experience.

Of course as a strategist these concerns are not valid to us, as we analyze demand patterns, performance KPI and and other statistics to optimize a business. And regardless of where it is located, the methodology and best practices of Revenue Management do not change.

So how did we do? Let me share some case studies of our hotel performance in Italy with you.

Hotel Turnaround Case Studies

Below you will find 3 case studies of our turnaround strategies of hotels and resorts in Italy.

Case Study 1

Property Type:

• 5* Luxury Resort Hotel in a historical building (monastery)
• Part of Leading Hotels of the World
• 102 rooms, located in Taormina, Sicily

We started managing this project in 2017, with the mission to turnaround the financial performance, right after the acquisition by a new investor.

Action Plan:

To drive a mixture of both occupancy and ADR increase Xotels implemented the following action steps and strategic adjustments:

• Implementation of Dynamic pricing structure for resorts
• Public rate structure review and repositioning
• Room type structure review, changes applied (new room types distributed in all channels, room type supplements changes)
• Inventory management review and new implementations (overbooking by room type when needed, ensuring stay through), groups on option, etc
• Business mix review, new structure applied
• Direct Sales stimulation activities implemented
• Review of all FIT contracts T&Cs
• Control of FIT availability offered to FIT partners
• Online content review and improvement in all distribution partners
• CRS content and rate structure review
• Reinforcement of Luxury travel agency partnerships
• Digital hotel market positioning review
• Stronger focus Online reputation review
• Groups strategy review and new definition according to new business mix defined
• Rate parity violation monitoring and correction
• Creation of new packages creation (Beach club, Gourmet activities, etc)

The hotel managed to increase RevPar year over year by 39%, as a result of occupancy increasing from 66.19% to 74.33% and ADR growing from €307.67 to €381.16.

So the hotel room revenue increased by 655K year on year (2017 vs 2016) (even though we were open 62 days less in 2017 then in 2016).

Case Study 2

• 5* Luxury Grand Hotel in a Palace type building
• Located in Palermo, Sicily

We started managing this project in 2018, with the mission to turnaround the financial performance right after the acquisition by a new investor.

At this hotel we implemented our revenue management methodology, distribution techniques and best practices in hotel management strategies. Below an overview of some of the key action steps:

• Implementation of Dynamic pricing structure
• Public rate structure review and correction
• Hotel market positioning review
• Online reputation review and focus (recommendations on activities and structure)
• New packages creation (Special Suites, Gourmet activities, etc)

The hotel had a Room Revenue increase of 23% year on year, which equals €1.3mln. This is mainly coming from an increase in ADR, which grew from €213.33 in 2017 to €257,71 in 2018.

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Case Study 3

• 4* business hotel
• Located in the Piemonte region

We took on this property staring March 2018.

The successful room revenue results achieved in Agora Palace Hotel have been possible thanks to the implementation of the following new strategies:

• Business mix review and new actions implemented (online distribution specially)
• Rate structure review and new implementations
• Corporate contracts review and changes made (dynamic rate structure implementation)
• Rate structure review and corrections made
• Inventory management new standards implementation
• Group strategy implemented (quotes, procedures, T&Cs, etc)
• Hotel team training provided in Groups management
• Hotel team training provided in Direct Sales stimulation

The hotel had been trailing in REVPAR performance year over year by about 11%. So it was more of a long term effort to turn this property around. We went from a negative REVPAR Index at the beginning of the year to a year over year REVPAR increase of average 24% from August till the end of the year. By being more aggressive in the transient / public rate strategy, we managed to increase occupancy by 27 points.

In 2019, from January to June, we are up 23% in REVPAR year over year, surpassing 2017 results by 12%.

The Final Word

Implementing best practices in Revenue Management strategies , and training the onsite teams in industry to work methodically works in any market as we can see. It is all about a structured approach and analysing data.

Since the beginning of Xotels, I still compare Revenue Management to playing PacMan. You know the flow of the game after a while. They can change the colors of the screen, throw more cherries out there or have the ghosts speed up. Just keep calm and play your game as you always do, and you will harvest the results.

We have now recently picked up a 5 star luxury boutique hotel in Rome, where we start seeing the first results already. And I hope we will be able to assist more hotels in the near future in Italy with turning their financial performance around.

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YIELD MANAGEMENT CASE STUDY: Food & Beverage

Project Background

This company manufactures food products on behalf of a leading South African retailer. The products range from packaged snacks such as muffins and sandwiches through to pre-prepared meals for home consumption. Products of this type have a particularly short shelf life measured in terms of a few days rather than weeks or months.

The company deals with high volumes and their customer has very strict delivery and product quality requirements. Meeting these exacting product standards while maintaining an acceptable profit margin is a challenge, which is where the TransLution Yield Management software comes in.

Yield Management Functionality

The project began by focusing on solving problems experienced within the biltong manufacturing process. Each step in the production process was defined, including all raw material inputs and the by-products and waste created during production. Each crate of raw material, by-product and waste is weighed at every production stage providing yield results throughout the process, identifying problems to be addressed during production, and recording actual yield for every batch.

The TransLution Yield Management module was developed specifically for reporting yield calculations and is designed to run on Touch Screen terminals on the production floor. Direct integration to the weighing devices used at each production step improves yield accuracy and simplifies operation for the users.

Recording of lot number controls, labour and machine usage and quality testing results on these same Touch Screen monitors enhances reporting and analysis of the production process and provides documentation required for validation of quality standards and information in the event of a product recall.

TransLution Overview

The TransLution configuration utilises the same application server sharing both the TransLution and the ERP software databases. Touch Screen terminals, hand held bar-code scanners and bar-code printers are used on the production floor for recording production activity and PC’s are used for viewing reports and other management reporting.

Operators logging production data into the system are identified using fingerprint scanning built into the TransLution software.

The TransLution Touch Screen and scanner software is configured to match the process requirements for biltong at this client’s production plant. Each input screen and scale integration is tailored to match its relevant place in the production process.

Key Benefits

• Scanning and weighing materials into and out of the production process and assigning lot control numbers provides full computer-based traceability across the entire process. Reports are used to measure supplier performance and are the key to having an effective product recall process in place.
• Capturing test results on the system provides quality assurance documentation required by the customer.
• Operators log into the Yield Management system using fingerprint scanners which provides labour utilisation data on each batch that could only be guessed at previously.
• Previously, yield results were only available days, sometimes weeks, after production was completed. The biltong production manager summed it up by saying ‘We used to see historical yield data and ask why. Now we see yield results as a function of production and we know.’

Barcode Labels and Structures

Every crate is labelled with a unique barcode meaning individual product packs do not require barcodes. Scanning the crate barcode and weighing the crate is all that is required.

Yield Statistics

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Remote sensing imagery to predict soybean yield: a case study of vegetation indices contribution

• Published: 27 July 2024

Cite this article

• Lucas R. Amaral   ORCID: orcid.org/0000-0001-8071-4449 1 ,
• Henrique Oldoni   ORCID: orcid.org/0000-0003-3862-003X 2 ,
• Gustavo M. M. Baptista   ORCID: orcid.org/0000-0002-1973-2725 3 ,
• Gustavo H. S. Ferreira   ORCID: orcid.org/0000-0001-7841-2767 4 ,
• Rodrigo G. Freitas   ORCID: orcid.org/0000-0002-9787-2678 1 ,
• Cenneya L. Martins   ORCID: orcid.org/0000-0002-4585-2739 1 ,
• Isabella A. Cunha   ORCID: orcid.org/0000-0002-4004-3406 1 &
• Adão F. Santos   ORCID: orcid.org/0000-0003-3405-5360 5  

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Mapping the spatial variability of crops is critical for precision agriculture. In this sense, remote sensing is a key technology generally dependent on the result of vegetation indices (VIs). Therefore, investigating the sensitivity of VIs and their contribution toward explaining crop variability and assisting in predicting yield is one of the pathways scientific research needs to explore. In this study, we evaluated 12 VIs with different acquisition principles in four soybean-producing fields. Using these VIs proved to be interesting to increase the performance of yield prediction models using the Randon Forest algorithm. However, simply adding VIs to the model is not enough; these VIs must aggregate information on crop variability. Some VIs are calculated based on the variation of the scene under study, and these can be an interesting option to complement the information provided by more traditional VIs, such as NDVI, assisting in predictive models, even if their direct correlation with crop yield is low in some situations. We found that using VIs groups with the same acquisition principle in isolation did not allow reaching performance of models that contained more than one principle simultaneously. In this study, the CI and TC2 indices stood out. Thus, associating VIs with different acquisition principles and, consequently, capturing different responses to variability in vegetation vigor and canopy structure is more important than the number of predictor variables itself.

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Ali, A., Martelli, R., Lupia, F., & Barbanti, L. (2019). Assessing multiple years’ spatial variability of crop yields using satellite vegetation indices. Remote Sensing , 11 , 2384. https://doi.org/10.3390/RS11202384 .

Article   Google Scholar  

Bahrami, H., McNairn, H., Mahdianpari, M., & Homayouni, S. (2022). A meta-analysis of remote sensing technologies and methodologies for crop characterization. Remote Sensing , 14 , 5633. https://doi.org/10.3390/rs14225633 .

Baptista, G. M. M. (2015). Aplicação do Índice de Vegetação por Profundidade de Feição Espectral (SFDVI - Spectral Feature Depth Vegetation Index) em dados RapidEye (Application of Spectral Feature Depth Vegetation Index (SFDVI) to RapidEye data). In: proceedings of the XVII Simpósio Brasileiro de Sensoriamento Remoto – SBSR. INPE, João Pessoa, PB, Brazil, pp 2277–2284.

Crusiol, L. G. T., Nanni, M. R., Furlanetto, R. H., Sibaldelli, R. N. R., Cezar, E., Sun, L., Foloni, J. S. S., Mertz-Henning, L. M., Nepomuceno, A. L., Neumaier, N., & Farias, J. R. B. (2021). Yield prediction in soybean crop grown under different levels of water availability using reflectance spectroscopy and partial least squares regression. Remote Sensing , 13 , 977. https://doi.org/10.3390/rs13050977 .

Demmel, M. (2013). Site-Specific Recording of yields. In H. J. Heege (Ed.), Precision in Crop Farming - Site specific concepts and sensing methods: Applications and results . Springer. https://doi.org/10.1007/978-94-007-6760-7 .

Ding, Y., Zhao, K., Zheng, X., & Jiang, T. (2014). Temporal dynamics of spatial heterogeneity over cropland quantified by time-series NDVI, near infrared and red reflectance of Landsat 8 OLI imagery. International Journal of Applied Earth Observation and Geoinformation , 30 , 139–145. https://doi.org/10.1016/j.jag.2014.01.009 .

Dray, S., & Dufour, A. (2007). The ade4 Package: Implementing the duality diagram for ecologists. Journal of Statistical Software , 22 (4), 1–20. https://doi.org/10.18637/jss.v022.i04 .

Figueiredo, G. K. D. A., Brunsell, N. A., Rocha, J. V., Lamparelli, R. A. C., & Picoli, M. C. A. (2016). Using temporal stability to estimate soya bean yield: A case study in Paraná state, Brazil. International Journal of Remote Sensing , 37 (5), 1223–1242. https://doi.org/10.1080/01431161.2016.1148280 .

Filippi, P., Jones, E. J., Wimalathunge, N. S., Somarathna, P. S. S. N., Pozza, L. E., Ugbaje, S. U., Jephcott, T. G., Paterson, S. E., Whelan, B. M., & Bishop, T. F. A. (2019). An approach to forecast grain crop yield using multi-layered, multi-farm data sets and machine learning. Precision Agriculture , 20 (5), 1115–1029. https://doi.org/10.1007/s11119-018-09628-4 .

Gebbers, R., & Adamchuk, V. I. (2010). Precision Agriculture and Food Security. Science , 327 , 828–831. https://doi.org/10.1126/science.1183899 .

Article   CAS   PubMed   Google Scholar  

Jin, S., & Sader, S. A. (2005). Comparison of time series tasseled cap wetness and the normalized difference moisture index in detecting forest disturbances. Remote Sensing of Environment , 94 , 364–372. https://doi.org/10.1016/j.rse.2004.10.012 .

Kauth, R. J., & Thomas, G. S. (1976). The Tasselled Cap - a graphic description of the spectral-temporal development of agricultural crops as seen by Landsat. In: Proceedings of Symposium on Machine Processing of Remotely Sensed Data. Purdue University: West Lafayette, IN, USA, pp 41–51.

Kayad, A., Sozzi, M., Gatto, S., Marinello, F., & Pirotti, F. (2019). Monitoring within-field variability of corn yield using Sentinel-2 and machine learning techniques. Remote Sensing , 11 , 2873. https://doi.org/10.3390/RS11232873 .

Kross, A., Znoj, E., Callegari, D., Kaur, G., Sunohara, M., Lapen, D. R., & McNairn, H. (2020). Using artificial neural networks and remotely sensed data to evaluate the relative importance of variables for prediction of within-field corn and soybean yields. Remote Sensing , 12 , 2230. https://doi.org/10.3390/rs12142230 .

Kumar, C., Mubvumba, P., Huang, Y., Dhillon, J., & Reddy, K. (2023). Multi-stage corn yield prediction using high-resolution UAV multispectral data and machine learning models. Agronomy , 13 , 1277. https://doi.org/10.3390/agronomy13051277 .

Leroux, C., Jones, H., Clenet, A., Dreux, B., Becu, M., & Tysseyre, B. (2018). A general method to filter out defective spatial observations from yield mapping datasets. Precision Agriculture , 18 , 789–808. https://doi.org/10.1007/s11119-017-9555-0 .

Li, F., Miao, Y., Feng, G., Yuan, F., Yue, S., Gao, X., Liu, Y., Liu, B., Ustin, S. L., & Chen, X. (2014). Improving estimation of summer maize nitrogen status with red edge-based spectral vegetation indices. Field Crops Research , 157 , 111–123. https://doi.org/10.1016/j.fcr.2013.12.018 .

Lobell, D. B. (2013). The use of satellite data for crop yield gap analysis. Field Crops Research , 143 , 56–64. https://doi.org/10.1016/j.fcr.2012.08.008 .

Maldaner, L. F., Molin, J. P., & Spekken, M. (2022). Methodology to filter out outliers in high spatial density data to improve maps reliability. Scientia Agricola , 79 , e20200178. https://doi.org/10.1590/1678-992X-2020-0178 .

Masino, A., Rugeroni, P., Borrás, L., & Rotundo, J. L. (2018). Spatial and temporal plant-to-plant variability effects on soybean yield. European Journal of Agronomy , 98 , 14–24. https://doi.org/10.1016/j.eja.2018.02.006 .

Pagano, M. C., & Miransari, M. (2016). The importance of soybean production worldwide. In: Abiotic and Biotic Stresses in Soybean Production: Soybean Production: Volume 5, pp. 1–26. Elsevier. https://doi.org/10.1016/B978-0-12-801536-0.00001-3 .

Peng, Y., Nguy-Robertson, A., Arkebauer, T., & Gitelson, A. A. (2017). Assessment of canopy chlorophyll content retrieval in maize and soybean: Implications of hysteresis on the development of generic algorithms. Remote Sensing , 9 , 226. https://doi.org/10.3390/rs9030226 .

Richardson, A. J., & Wiegand, C. L. (1977). Distinguishing vegetation from soil background information. Photogrammetric Engineering and Remote Sensing , 43 , 1541–1552.

Google Scholar  

Sishodia, R. P., Ray, R. L., & Singh, S. K. (2020). Applications of remote sensing in precision agriculture: A review. Remote Sensing , 12 , 3136. https://doi.org/10.3390/rs12193136 .

Skakun, S., Kalecinski, N. I., Brown, M. G. L., Johnson, D. M., Vermote, E. F., Roger, J. C., & Franch, B. (2021). Assessing within-field corn and soybean yield variability from WorldView-3, Planet, Sentinel-2, and Landsat 8 satellite imagery. Remote Sensing , 13 , 872. https://doi.org/10.3390/RS13050872 .

Snoek, J., Larochelle, H., Adams, R. P., Pereira, F., Burges, C. J. C., Bottou, L., & Weinberger, K. Q. (2012). Practical Bayesian optimization of machine learning algorithms. Advances in neural information processing systems, arXiv . https://doi.org/10.48550/arXiv.1206.2944 .

Tavakoli, H., Mohtasebi, S. S., Alimardani, R., & Gebbers, R. (2014). Evaluation of different sensing approaches concerning to nondestructive estimation of leaf area index (LAI) for winter wheat. International Journal on Smart Sensing and Intelligent Systems , 7 (1), 337–359. https://doi.org/10.34657/4497 .

Tesfaye, A. A., & Awoke, B. G. (2021). Evaluation of the saturation property of vegetation indices derived from Sentinel-2 in mixed crop-forest ecosystem. Spatial Information Research , 29 , 109–121. https://doi.org/10.1007/s41324-020-00339-5 .

Viña, A., Gitelson, A. A., Nguy-Robertson, A. L., & Peng, Y. (2011). Comparison of different vegetation indices for the remote assessment of green leaf area index of crops. Remote Sensing of Environment , 115 , 3468–3478. https://doi.org/10.1016/j.rse.2011.08.010 .

Wang, Q., Adiku, S., Tenhunen, J., & Granier, A. (2005). On the relationship of NDVI with leaf area index in a deciduous forest site. Remote Sensing of Environment , 94 , 244–255. https://doi.org/10.1016/j.rse.2004.10.006 .

Weiss, M., Jacob, F., & Duveiller, G. (2020). Remote sensing for agricultural applications: A meta-review. Remote Sensing of Environment , 236 , 111402. https://doi.org/10.1016/j.rse.2019.111402 .

Xue, J., & Su, B. (2017). Significant remote sensing vegetation indices: A review of developments and applications. Journal of Sensors , 2017,1353691. https://doi.org/10.1155/2017/1353691 .

Yu, L., Shang, J., Cheng, Z., Gao, Z., Wang, Z., Tian, L., et al. (2020). Assessment of cornfield LAI retrieved from multi-source satellite data using continuous field LAI measurements based on a wireless sensor network. Remote Sensing , 12 (20), 3304. https://doi.org/10.3390/rs12203304 .

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Acknowledgements

The authors would like to thank everyone who collaborated in data collection and field experiments at the four study sites.

This research was funded by São Paulo Research Foundation - FAPESP (Process number 2022/03160-8). The first author has a B-level productivity grant by CNPq – Brazil (306867/2022-2).

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Lucas R. Amaral, Rodrigo G. Freitas, Cenneya L. Martins & Isabella A. Cunha

Interdisciplinary Center of Energy Planning, Universidade Estadual de Campinas – UNICAMP, Campinas, SP, Brazil

Henrique Oldoni

Geoscience Institute, University of Brasília, Brasília, DF, Brazil

Gustavo M. M. Baptista

Department of Geography, University of Brasília, Brasília, DF, Brazil

Gustavo H. S. Ferreira

Lavras School of Agricultural Sciences, Federal University of Lavras, Lavras, MG, Brazil

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Amaral, L.R., Oldoni, H., Baptista, G.M.M. et al. Remote sensing imagery to predict soybean yield: a case study of vegetation indices contribution. Precision Agric (2024). https://doi.org/10.1007/s11119-024-10174-5

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Why the Management: Practitioner Programme worked for me

Ministry of Defence policy advisor Nazia Lodhi says she learned from peers across government while on the Civil Service Management: Practitioner Programme.

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TechBullion

Fintech management case study: wonga south africa .

The short-term loan disruptor ‘Wonga’ began UK trading over 17 years ago, and back in 2007, the online loan provider was truly ahead of its time. The original team behind the pioneering loan platform was actually comprised of staff with roots in the gaming industry; their lack of deeper knowledge into the credit industry would prove problematic.

The business approach at this time focused on revenue optimization as priority number one, contrasting sharply with sustainable credit industry goals. This misalignment proved critical and was a major factor in the UK’s eventual collapse into administration. A leadership overhaul at this time ushered in Brett Van Aswegen to take over the South African division of the personal loan business. 

What follows is Brett’s insight into the myriad of challenges he and his team faced to pull Wonga out of the fire in the face of imminent closure from financial administrators. 

Brett is quick to acknowledge both the previous successes and missteps of the brand’s legacy. The original team at Wonga made notable errors that damaged the brand’s reputation in the UK. However, they also pioneered a revolutionary paperless, real-time loan application system, establishing Wonga as one of the earliest fintech innovators. 

Embracing Regulators as Teammates 

Despite Wonga South Africa’s compliance with the 2007 National Credit Act, political pressures and negative associations with the UK division overshadowed its local operations. The new CEO was acutely aware of the existential threat to the South African branch. Brett requested six months to turn things around.

On his first day, an inspector from the National Credit Regulator arrived. Working under parliamentary pressure, they were intent on shutting down Wonga internationally. Van Aswegen immediately engaged with them, presenting the company’s compliance and commitment to ethical practices at rapid pace. 

He proposed retrospective loan affordability checks, an unprecedented move in the industry, to demonstrate Wonga’s intent to make things right. This willingness to integrate with regulators and absorb them into the team fabric helped to build credibility and trust. Within three weeks, all required changes were implemented, significantly reducing Wonga’s revenue by a staggering two-thirds: the equivalent of chopping off two legs (and an arm) to save the rest.

Reinventing Customer Engagement & Core Values

With immediate regulatory closure abated, Wonga South Africa focused on extensive product surveys and engaged in focus groups to better understand their customers. This research was crucial in shaping their strategy and redefining the brand’s purpose. Uniting the team was crucial; Brett involved them in developing this purpose statement and integrated the ‘old guard’ into the transformation process.

The motivation was not driven by shareholder profits but by creating value for South Africa and the broader community. The aim was to solve real problems for customers, not just to be another micro-lender.

A purpose statement was crafted that aligned with the positive aspects of Wonga’s disruptive origins while addressing current market needs. The old company model relied heavily on high advertising spend, operating on a ‘churn-and-burn’ method. The new strategy focused on creating a relevant product, transitioning from payday to short-term lending. This allowed customers more time and greater flexibility with loan repayments. This change aimed to reduce dependency on Wonga’s product and foster sustainable lending practices.

Reflecting this pivot in customer relationship, Wonga shifted away from heavy out-bound marketing spend to laser focused digital campaigns, prioritising a more relevant and sustainable product for consumers when it was urgently needed.

Successful Rebirth & New Challenges

To thrive, Wonga had to redefine its system’s strengths and identify areas for partnership enhancement. This re-engineering required a complete platform rebuild, coinciding with a significant shift within the Wonga group. 

The Dublin engineering team was disbanded, necessitating the assembly of a South African team to redesign the platform and launch new products within two years. By the end of 2017, the new product and platform were in testing. By March 2018, the first campaign with the revamped offering launched, tripling application volumes in the first month. This marked Wonga’s significant progression from mere survival to active growth.

However that summer, in the face of severe regulatory issues, the UK group entered administration, cutting off funding entirely. This sudden liquidity crisis, just as the new South African product gained traction, presented a significant challenge. Over the next 16 months, Brett navigated a complex process to save the business and initiated a local management buyout.

Bringing in partners who played a pivotal role in this acquisition was crucial to success. Fortunately, the team managed to acquire the business in December 2019 and began trading independently as Wonga Online (Pty) Ltd.

Positioning Wonga for the Future

Having led Wonga South Africa for eight years, Van Aswegen found the journey challenging yet rewarding. His goal is to help the business realise its full potential. He recently completed a vision board to clarify future goals, including thinking about how to mould  talented team members to lead. He thrives on building and overcoming challenges, not maintenance, and his role at Wonga will evolve as the company continues to grow.

Financial inclusion presents a significant future opportunity for Wonga. In South Africa, Approximately 60% of adults have access to formal credit, while 40% rely on informal credit markets i.e. the loan shark industry pervasive at the most vulnerable socio-economic level in South Africa.

Wonga aims to help this excluded segment gain access to safer and more sustainable credit by using alternative data sources and machine learning. These tools can make credit decisions for those without established credit records, providing them with their first access to regulated credit and building a bridge towards greater financial inclusion.

Wonga’s transformation has been about more than survival – It created a sustainable, customer-centric business that balances the needs of all stakeholders. By focusing on purpose before profit, building strong partnerships, and driving financial inclusion, Wonga is positioned for long-term success. This journey reaffirms the company’s commitment to providing valuable financial services and maintaining leadership in the fintech space.

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• DOI: 10.1007/s10668-024-05220-1
• Corpus ID: 271522220

An accuracy assessment of satellite-based cotton yield estimation using panel data regression: a case study of Uzbekistan

• Shovkat Khodjaev , I. Bobojonov , +1 author T. Glauben
• Published in Environment, Development and… 27 July 2024
• Agricultural and Food Sciences, Environmental Science

110 References

Integrating environmental and satellite data to estimate county-level cotton yield in xinjiang province, assessing the severity of cotton verticillium wilt disease from in situ canopy images and spectra using convolutional neural networks, fuzzy clustering for the within-season estimation of cotton phenology, cotton yield estimation based on vegetation indices and texture features derived from rgb image, implications of farmer’s adaptation strategies to climate change in agricultural sector of gujarat: experience from farm level data, an end-effector for robotic cotton harvesting, yield estimation of high-density cotton fields using low-altitude uav imaging and deep learning, cotton yield estimation model based on machine learning using time series uav remote sensing data, usa crop yield estimation with modis ndvi: are remotely sensed models better than simple trend analyses, suitability of satellite remote sensing data for yield estimation in northeast germany, related papers.

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