Category: Africa

Source: The Conversation – Africa – By Lawrence Nkosikhona Malinga, Programme Manager (Crop Protection) & Senior Entomologist, South African Sugarcane Research Institute; University of KwaZulu-Natal

South Africa has around 25,653 small-scale sugarcane growers, making the industry a lifeline for rural communities in Mpumalanga and KwaZulu-Natal provinces. By generating 65,000 direct and 270,000 indirect jobs, its agricultural and milling operations support more than a million livelihoods in these areas. But the productivity of many small-scale growers remains well below potential. Production constraints are limiting both farm profitability and the sector’s overall contribution to rural economic development.

The growers face complex challenges that go beyond access to technical farming advice. A six-year research project by senior entomologist Lawrence Nkosikhona Malinga and his colleagues at the South African Sugarcane Research Institute in collaboration with the Department of Agriculture and Rural Development, South African Farmers Development Association and South African Canegrowers looked to identify and address some of the challenges. They found variations in growers’ education levels, language barriers, lack of funding, lack of technical expertise and poor planning of crop production.

Some of the key findings are that issues such as weed pressure, weak planning systems, contractor dependence, limited access to seedcane and poor technical support undermine productivity and profitability. The project identified lessons for agricultural systems and smallholder farmer support programmes.

What are the 4 biggest challenges for small-scale sugarcane growers?

1. Weed pressure

Many growers struggle with choosing and measuring herbicide, applying it at the right time, and the cost. Due to financial constraints, they sometimes use less herbicide than is needed, or they delay application. They manage weeds after they appear instead of preventing them.

Poor weed control has major consequences. Weeds compete directly with sugarcane for water, nutrients and sunlight. The cane doesn’t grow well but the labour and production costs are high. The incorrect dosage and inefficient application of products can also lead to herbicide resistance. Many of these growers are not familiar with these issues and good practices.

It’s usually cheaper to manage weeds before they can affect cane yields.

2. Weak planning

Many farming decisions, including seedcane ordering, planting dates, weed control, harvesting schedules and fertiliser applications, happen later than is optimal. These delays are often linked to financial constraints, knowledge gaps, or dependence on others like service providers, input suppliers, technical advisors, and labour availability. One delayed decision triggers several others.

With sugarcane, which isn’t planted every year, these delays can reduce productivity for multiple seasons and reduce overall profitability. Improved planning would allow growers to use inputs more efficiently and reduce avoidable losses.

3. Contractor dependence

Many small-scale growers rely heavily on contractors for planting, agrochemical applications, harvesting and land preparation. They don’t have the expensive machinery and labour to do these jobs themselves. But too much dependence can reduce grower control over critical operations. Delays, poor communication, or inconsistent service quality affect production outcomes.

Some growers also reported limited confidence in supervising contractor activities or challenging decisions when work quality was poor. This is partly due to gaps in knowledge about what has to be done on the farm.

4. Limited access to quality seedcane and technical support

Growers sometimes get seedcane from neighbours, informal networks, or their own fields because certified or approved seedcane is expensive, unavailable locally, or ordered too late. As a result, they may end up with the variety that’s available rather than the one best suited to the soil type or regional pest and disease pressures.

Growers said technical support was inconsistent from extension officers but they needed help with pest and disease diagnosis, variety selection and chemical use.

How should these challenges be tackled?

The findings suggest that farmers need stronger support systems, not just technical recommendations. A combination of practical learning, institutional coordination and improved access to resources would help.

Weed pressure

Weed management support needs to become more practical and continuous. Growers need extension officers to help them choose the correct products and time the application.

Demonstration plots, a small piece of land used to test and showcase new agricultural techniques, crop varieties, or technologies, would be useful. And growers should be able to learn by doing.

Low-cost practices like hand weeding and covering the soil with dried cane leaves to suppress weed growth would reduce the need for chemicals.

Weak planning systems

Seasonal calendars, simple budgeting tools and practical farm-planning templates could help growers improve the timing of planting, fertiliser applications, seedcane ordering and harvesting. Regular engagement is better than occasional workshops. Cost-benefit tools (methods to compare the costs of an action or decision with the benefits it produces) would help growers choose how to use limited resources more efficiently.

Contractor dependence

Small-scale sugarcane contractors are growers that provide mechanical (land preparation, crop maintenance and cane transportation) and labour (cane cutting) services to fellow growers. The productivity of these contractors is generally low and their services aren’t always reliable. Delays can be costly. Improving contractor efficiency and productivity would benefit both contractors and growers. Growers would get quality services at competitive prices, and millers would receive a steady flow of quality cane.

Because contractors are deeply embedded within small-scale production systems, solutions should focus on improving relationships rather than replacing them. Growers need support to supervise contractor activities more confidently. Stronger communication between growers, contractors and advisory services would improve accountability and timing.

Limited seedcane access and technical support

Seed production and distribution should be done by a wider network, not a central scheme. This would promote varietal diversity and create rural employment.

Extension services need to move from just delivering information to more practical visits where farmers can learn by doing.

These challenges are interconnected. Isolated interventions are unlikely to succeed. Integrated support systems would combine practical learning, stronger extension services, improved coordination among industry stakeholders, better access to inputs and continuous collaborative engagement. The evidence suggests that growers are more likely to adopt recommended practices when support is practical and they can see that it works for them.

Why does solving these challenges matter?

Improving weed control, planning systems, contractor coordination and access to seedcane could do far more than increase yields. These changes could improve profitability, strengthen household incomes, reduce production risks, and increase the resilience of rural farming communities.

Experiences from countries such as Brazil, Kenya, Mauritius, and India show that small-scale sugarcane productivity improves when growers receive integrated support rather than isolated interventions. Practical extension services, timely access to certified seedcane, coordinated contractor and mechanisation services, and better farm planning have increased yields, profitability and resilience, demonstrating that strengthening the entire production support system can deliver lasting benefits for rural farming communities.

Perhaps the biggest lesson from this work is that productivity challenges are rarely caused solely by not accepting recommendations. Small-scale growers often know what should be done, but operate within systems that make it difficult to follow the advice.

The South African Sugarcane Research Institute team leaders including Lindani Mchunu (project manager), Khanyisile Buthelezi, Tholoana Mofurutsi, Surashna Jithoo, Siphumelelo Mbhele and other research and extension colleagues played a crucial role in the research and the article.

– South Africa’s small sugar farms are producing less than they could: study finds ways to boost them
– https://theconversation.com/south-africas-small-sugar-farms-are-producing-less-than-they-could-study-finds-ways-to-boost-them-285098

Source: The Conversation – Africa – By Bomikazi Zeka, Associate Professor in Finance, University of Canberra

Ever used or thought of using artificial intelligence (AI) for financial advice? Is it a good idea?

Anyone with the internet, even on their phone, can access AI. Tools like ChatGPT, Claude and Perplexity are free, instant, and easy to use. They can break down complex financial concepts and provide financial solutions in seconds. But can they safely guide a user through a nuanced, high-stakes financial crisis?

Our recent research explored this question, drawing on our financial planning and consumer behaviour expertise. We evaluated how three open-access AI models handled the financial queries of users whose personal situations put them at risk of harm.

To do this we created hypothetical scenarios representing different life stages, economic challenges and socioeconomic vulnerabilities. We then ran these scenarios through OpenAI’s ChatGPT, Anthropic’s Claude and Perplexity AI:

• a 22-year-old university graduate wanting to save for a home deposit during a cost-of-living crisis

• a pregnant woman seeking financial advice on planning for maternity leave, with a partner who doesn’t share money

• a single parent of two children, with a modest income, who was told by their cousin to invest in cryptocurrency.

In our analysis we found that AI offers highly structured and practical advice. But it has blind spots. We provided the models with explicit information about how the three cases were vulnerable, but the models didn’t respond appropriately to that. They even offered advice that could make financial harm worse.

We concluded from our research that AI is a powerful tool for financial fact-finding and brainstorming. If you are already financially literate, and know how to cross-check data, AI can be a useful financial aid. But, for now, AI cannot replace the human element.

Running scenarios

We chose Claude for its versatility and conservative approach, ChatGPT for being an all-round assistant, and Perplexity for its contextual understanding.

We developed five hypothetical scenarios and simultaneously ran each scenario five times to test for consistency in the output. Each scenario was run in a different web browser, in incognito mode, with cleared cache and cookies to eliminate any retained information. This was also to ensure that the output generated was not influenced by retained data.

To reduce the risk of AI-generated bias, we removed identifying attributes that could influence model outputs, such as names, locations, race and income. We then assessed how the AI models considered the user’s vulnerability, area of financial need, and goal or desired outcome.

The models distinctly addressed the financial query, yet revealed gaps in how they balanced advice with considerations of vulnerability.

At first glance, the AI models looked like they were giving sound financial advice. But our analysis showed that they handled vulnerability in vastly different ways.

ChatGPT was highly detailed and practical, but did not recognise the vulnerability embedded within the prompts. Instead, it relied on the information that was explicitly stated. It didn’t consider whether the user’s situation suggested a need for additional support or tailored guidance.

Perplexity emerged as the most conservative and risk averse as it most frequently urged users to seek professional financial advice. But it produced the least detailed responses.

Claude offered comprehensive recommendations. But it leaned heavily towards self-guided financial planning.

The vulnerability blind spot

AI is trained on massive datasets designed by humans who are biased. For instance, when models are trained on historical records that reflect systemic discrimination, the tools can fall back on social stereotypes and make biased assumptions in their output.

Research shows, too, that when AI models explain their recommendations, humans are far more likely to trust the advice blindly, ignoring whether it is actually correct.

The most alarming finding from our research was the technology’s failure to consistently recognise and address the needs or concerns of vulnerable users.

For instance, in the case of the graduate, the recommendations focused on saving for a deposit but ignored how the high cost of living would make that harder. This was even when explicit details about the graduate’s financial circumstances had been provided in the prompt.

The response didn’t consider how achieving the long term goal would affect the current lifestyle.

In the case of the pregnant mother, Perplexity and ChatGPT’s output assumed the partner would assist with household expenses after the birth, even though the prompt explicitly stated that the partners did not share their finances. The models did not consistently use the detail provided in the prompt and instead generated a response based on a more common assumption about household financial arrangements.

We saw the output reflecting social stereotypes where mothers are strongly associated with parenting while fathers are strongly associated as material providers.

This shows that even as AI evolves, it still replicates biases.

Take the single parent asking for cryptocurrency recommendations. Even though the AI models advised caution, ChatGPT described in detail how to get into cryptocurrency investments and recommended cryptocurrencies for beginners.

A human financial advisor would immediately flag these areas for consideration: a modest income, children, a high-risk investment, and anecdotal advice from the cousin. A human advisor would first gauge the user’s overall financial position, time horizon, risk appetite and investment objectives.

Our research shows that AI models are limited in providing tailored financial advice, unless the user provides additional explicit input.

Where do we go from here?

Financial planning isn’t just about numbers; it’s about advice based on human values, emotional anxieties, family dynamics, personal experiences and risk tolerance. AI has opened the door to instant financial information for the masses. But until these models can truly comprehend the complex, vulnerable and emotional realities of humans, the most valuable financial skill remains critical thinking and asking yourself: “does this make sense for me?”.

If AI tools are to become an avenue for financial guidance, there must be safeguards. Policymakers and financial regulators must build clear frameworks around AI generated advice. What also needs to be considered is what AI models do with the financial information provided. Strict transparency standards are needed to regulate how these models handle consumer data.

Financial information is deeply sensitive and consumers must have absolute clarity on how their inputs are stored, whether the AI retains a “memory” of their finances, and who has access to that data.

– We asked ChatGPT, Claude and Perplexity for financial advice: what we got was practical, but with big blind spots
– https://theconversation.com/we-asked-chatgpt-claude-and-perplexity-for-financial-advice-what-we-got-was-practical-but-with-big-blind-spots-288948