The study combined Jordanian crop-production and market data with published soil-carbon estimates to compare four land-management pathways: increasing soil organic carbon through measures such as cover crops, rotations, organic amendments and reduced tillage; adding biochar; introducing agroforestry; and improving cropland management. Production records covered vegetables and fruit crops from 1998 through 2023, while wheat and barley records began in 2016 because continuous, reliable data were unavailable earlier. Because the agricultural statistics were available only as national totals, the analysis compared broad crop types rather than individual regions or farms and did not include spatial or geographic modelling.

To construct Jordan-specific estimates, the researchers used global mitigation ranges for the four practices and allocated them according to each practice’s relative contribution. They then applied implementation levels of 10%, 20% and 30% of cultivated land for the low, medium and high scenarios. Jordan’s soil-carbon gains previously estimated under improved management supplied the local scale. Carbon was converted consistently between units of elemental carbon and carbon dioxide, and outputs were expressed per hectare per year. The resulting practice weights were 0.358 for increased soil organic carbon, 0.264 for biochar, 0.231 for agroforestry and 0.147 for improved cropland management, making the first pathway the largest component of the modeled strategy.

Across the scenarios, increased soil organic carbon generated the widest potential range, from 0.11 to 1.65 gigagrams of carbon per year nationally for the selected pathway. Biochar ranged from 0.08 to 1.21 GgC/year, agroforestry from 0.07 to 1.06 GgC/year, and improved cropland management from 0.04 to 0.68 GgC/year. Within individual crops, biochar reached 9.06 tCO₂/ha/year for cucumbers under the high scenario, while agroforestry reached 7.9 tCO₂/ha/year and improved cropland management topped out at 5.05 tCO₂/ha/year. The high scenario also expanded the modeled area under improvement to as much as 28,784 ha for barley and 19,360 ha for olives, whereas the smallest low-scenario areas were about 73 ha for cucumbers and 158 ha for lemons.

Uncertainty testing used 1,000 Monte Carlo simulations for each crop-practice-scenario combination, with an assumed normal-distribution uncertainty of plus or minus 15%. A separate plus or minus 20% sensitivity analysis examined changes in management coefficients. Increased soil organic carbon was the most sensitive of the four pathways, with a modeled impact range of 1.11 tCO₂/ha/year, whereas improved cropland management had the smallest response at 0.46 tCO₂/ha/year. The highest simulated mean was 13.63 tCO₂/ha/year for cucumbers under increased soil organic carbon in the high scenario; the lowest was 0.02 tCO₂/ha/year for barley under improved cropland management in the low scenario. Only 43.8% of simulated cases fell within the cited published benchmark ranges, illustrating the screening nature of the model and the need for local measurements.

The economic model evaluated farms from the producer’s perspective, adding carbon-management expenses to operating farming costs and comparing agricultural profit with adjusted profit that includes hypothetical carbon-credit revenue. It deliberately excluded possible yield gains, fixed costs, future inflation, temporary market fluctuations, future cash flows and a discount rate. Marginal abatement costs rose with implementation intensity: increased soil organic carbon and improved cropland management were assigned costs of 1, 5 and 10 US dollars per ton of carbon dioxide in the low, medium and high scenarios, while biochar and agroforestry were assigned 10, 50 and 100 dollars per ton. Estimated carbon prices ranged from 11 to 110 dollars per ton in the low scenario and from 20 to 200 dollars per ton in the high scenario.

Among the vegetables, cucumbers displayed the strongest financial results, with net profits ranging from negative 2,626 to 20,959 dollars per hectare and adjusted profits, including carbon revenue, from negative 1,976 to 21,593 dollars per hectare. Potatoes produced net-profit estimates of negative 193 to 12,946 dollars per hectare, while open-field tomatoes produced negative 574 to 18,652 dollars per hectare. Greenhouse tomatoes were a conspicuous exception: their net profit ranged from negative 12,944 to only 536 dollars per hectare, and their adjusted profit reached no more than 651 dollars per hectare. Grapes performed strongly, with maximum net and adjusted profits of 17,844 and 18,348 dollars per hectare, while lemons reached 13,086 and 14,020 dollars per hectare. Olives were less profitable, topping out at 3,819 and 3,846 dollars per hectare.

Biochar carried the highest total costs, ranging approximately from 240 to 33,856 dollars per hectare in low scenarios, 244 to 34,054 dollars in medium scenarios and 255 to 34,634 dollars in high scenarios. Increased soil organic carbon and improved cropland management had lower modeled costs, although implementing any pathway was generally more expensive when it produced a larger carbon-storage rate. The crop choice remained decisive. High-value open-field vegetables and profitable perennial fruits combined stronger agricultural returns with larger sequestration potentials, while olives, wheat and barley were more economically vulnerable and often depended on carbon revenue. The authors therefore described soil carbon as a potential complement to crop economics, not a universal substitute for crop profitability.

An economic sensitivity analysis tested an optimistic case with 10% higher yields, sequestration and carbon prices alongside implementation costs 10% below the base case, and a pessimistic case that reversed those changes. Under the optimistic case, net profit reached 28,903 dollars per hectare and adjusted profit 29,866 dollars per hectare. These modeled gains depended strongly on carbon-market and agronomic assumptions. The study did not engage farmers, policymakers or other stakeholders, did not establish actual carbon-credit demand and did not predict adoption rates; its low, medium and high implementation levels were scenario assumptions adapted from earlier work rather than observed trends.

The central result is therefore a screening-level business case rather than evidence that Jordanian soil can be converted into a verified carbon-credit asset. The analysis identifies increased soil organic carbon as the most promising of the four pathways by modeled storage potential and cost, while locating the clearest commercial opportunity in cucumbers, potatoes, open-field tomatoes, grapes and lemons. Yet the estimates depend on global coefficients, national production averages, assumed land coverage and hypothetical prices, and the 43.8% agreement rate with published ranges shows that substantial uncertainty remains. The researchers call for long-term field measurements of soil carbon, tests of the management coefficients under Jordanian conditions, spatially explicit datasets and farmer surveys. Until those steps are completed, the figures point toward where targeted research and investment may be most productive rather than demonstrating realized sequestration or guaranteed income.

Subject of Research: Scenario-based assessment of soil carbon sequestration and economic feasibility in Jordanian croplands

Article Title: Turning dirt into gold: the business case of soil carbon for agrarian pathways in arid climate

Article References: Althalaj, T., Thompson, R. M., Qtaishat, T., Bumblauskas, D., & Al-Shibli, F. (2026). Turning dirt into gold: the business case of soil carbon for agrarian pathways in arid climate. Frontiers in Sustainable Food Systems, 10, Article 1966265. https://doi.org/10.3389/fsufs.2026.1966265

Image Credits: AI Generated

DOI: 10.3389/fsufs.2026.1966265

Keywords: soil carbon sequestration, Jordan, agriculture, carbon credits, soil organic carbon, biochar, agroforestry, crop profitability, arid climates, scenario modelling, Turning, dirt

The team, led by Luis A. Gil-Alana of the University of Navarra in Spain together with OlaOluwa S. Yaya of the University of Ibadan in Nigeria, Ramiro Gil-Serrate of Universidad Antonio de Nebrija and Peter O. Ohue of Queens University, set out to answer a deceptively simple question: when a wet or dry month arrives in these drylands, how long does its influence persist? The answer matters enormously for the hundreds of millions of people whose agriculture, livestock and water supplies depend almost entirely on rain-fed systems. If rainfall shocks fade quickly, droughts are transient annoyances; if they persist, a single bad season can cascade into years of hardship.

To quantify persistence, the researchers turned to the econometric concept of fractional integration. In classical time-series analysis, a series is either stationary, meaning shocks die out quickly, or nonstationary, meaning shocks accumulate indefinitely and require full differencing to stabilise. Fractional models allow an intermediate possibility: a differencing parameter d that can take any real value. When d lies between zero and 0.5, the series displays long memory, with autocorrelations that decay hyperbolically slowly rather than exponentially fast, so disturbances echo through the record for a very long time even though the series ultimately reverts to its mean. When d reaches or exceeds 0.5, the series becomes nonstationary and shocks become effectively permanent in statistical terms.

The innovation in this study lies in extending that framework to seasonality. Monthly rainfall is dominated by an annual cycle, and standard seasonal unit root tests such as the HEGY procedure assume that the seasonal pattern can be removed by differencing the data twelve months apart. Preliminary HEGY tests on the African data rejected both non-seasonal and seasonal unit roots, but the authors argue that such tests are too restrictive: forcing d to equal exactly one risks overdifferencing, which can manufacture spurious conclusions. Their seasonal fractional model instead treats the operator (1 minus L to the twelfth power) raised to the power d, where L is the lag operator and d is estimated freely from the data. This allows the seasonal memory of rainfall to be measured on a continuum rather than forced into a binary stationary-or-not verdict.

The data came from the Climatic Research Unit Time Series dataset hosted on the World Bank Climate Change Knowledge Portal, a globally gridded observational product built by interpolating station measurements onto a regular 0.5-degree grid. Each country series contained 1,488 monthly observations. The descriptive statistics alone tell a story of extremes: mean monthly rainfall ranges from a mere 1.8 millimetres in Egypt and 3.4 millimetres in Libya to more than 70 millimetres in Ethiopia and 83 millimetres in South Sudan. Coefficients of variation frequently exceed 100 percent in countries such as Niger, Mauritania and Namibia, and every series is positively skewed, reflecting long stretches of near-zero rainfall punctuated by occasional torrential events.

The headline finding is that all estimated fractional parameters fall strictly between zero and one, confirming genuine fractional integration in every country examined. But the values split the continent into two hydroclimatic camps. In one group, the estimates are low enough to guarantee stationarity: Somalia records the lowest value at 0.25, followed by Algeria, Tunisia and Kenya at 0.27, Morocco and Egypt at 0.32, and Botswana, Libya, South Africa and Namibia in the 0.36 to 0.40 range. In these countries, rainfall deviations from the long-term mean are corrected relatively quickly, and the confidence intervals rule out nonstationarity. At the other extreme, Mauritania (0.54), Niger (0.56), Sudan and South Sudan (0.60), Chad (0.66) and Mali (0.67) all show estimates at or above the 0.5 threshold, meaning rainfall anomalies there are highly enduring and revert only slowly. Ethiopia sits almost exactly on the boundary at 0.49.

The geography of these results is not accidental. The high-persistence countries are concentrated in the Sahelian belt, where rainfall arrives in a single monsoonal wet season governed by the annual migration of the Intertropical Convergence Zone and the West African Monsoon. Rainfall events in these regions occur in coherent seasonal sequences, and prolonged dry seasons with relatively low intra-seasonal variability enhance temporal persistence. By contrast, the low-persistence countries experience climatically noisy regimes. Kenya and Somalia are shaped by two distinct rainy seasons, the so-called long rains and short rains, modulated strongly by the El Niño-Southern Oscillation and the Indian Ocean Dipole, while Algeria and Tunisia receive episodic precipitation from Mediterranean cyclones and mid-latitude weather systems. Multiple, interacting rainfall-generating mechanisms fragment the seasonal signal and weaken long memory.

The study also detected statistically significant positive time trends in 10 of the 17 countries, with the steepest increases in Ethiopia and Kenya, equivalent to roughly 0.20 and 0.34 millimetres per year respectively under the white-noise specification. To check robustness, the authors repeated the analysis allowing for weakly dependent errors using the non-parametric Bloomfield approach, which approximates the spectrum of autoregressive moving average processes with far fewer parameters, and by aggregating the monthly data to quarterly series. The broad picture held: under the Bloomfield specification, stationarity was confirmed in the same ten countries plus Ethiopia, with only South Sudan, Chad and Mali remaining nonstationary, while the quarterly analysis pushed estimates slightly higher, leaving nine countries in the nonstationary camp, with Chad reaching 0.89 and Mali 0.84. Across all specifications, six countries, Somalia, Algeria, Kenya, Tunisia, Egypt and Morocco, showed consistent evidence of stationary rainfall.

For drought managers and agricultural planners, the implications are stark. In Mali, Chad and South Sudan, a drought year is not simply a bad year that the climate forgets; the statistical signature suggests its effects linger, compounding food insecurity and water stress over successive seasons. Conversely, in the Mediterranean-influenced north and the dual-season east, rainfall anomalies wash out faster, but the very variability that produces this rapid mean reversion also makes rainfall harder to forecast from one season to the next. The authors note that countries with higher seasonal persistence may require fundamentally different drought-monitoring and agricultural-planning strategies than those where anomalies revert quickly, and the findings underscore sharply differing levels of hydroclimatic resilience across the continent.

The authors are candid about limitations. Country-level averages can mask enormous sub-national diversity, particularly in climatically heterogeneous nations such as Kenya, Ethiopia, South Africa and Sudan, where distinct regional rainfall regimes coexist within national borders. Future work, they suggest, will examine monthly rainfall totals, anomalies relative to the 1991 to 2020 climate baseline, standardised precipitation indices and log-transformed series, as well as non-linear trends built from Chebyshev polynomials, Fourier functions or neural networks, and the possible role of structural breaks in creating spurious long memory. Even with these caveats, the study delivers a rare century-scale, statistically rigorous portrait of rainfall behaviour in some of the world’s most vulnerable drylands, and it offers a quantitative foundation for anticipating which regions will recover from climate shocks and which will carry them forward.

Subject of Research: Seasonal persistence and long-memory dynamics of rainfall in arid and semi-arid African climatic zones

Article Title: Testing seasonal persistence of rainfall in arid and semi-arid african climatic zones using a seasonal fractional model

Article References: Gil-Alana, L. A., Yaya, O. S., Gil-Serrate, R., & Ohue, P. O. (2026). Testing seasonal persistence of rainfall in arid and semi-arid african climatic zones using a seasonal fractional model. Theoretical and Applied Climatology, 157(10), Article 628. https://doi.org/10.1007/s00704-026-06551-x

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06551-x

Keywords: rainfall persistence, fractional integration, long memory, Sahel, drought, arid climates, seasonality, Africa, time series analysis, HEGY test, Intertropical Convergence Zone, hydroclimatology

The research, led by Colin Gleason, a hydrologist in the Riccio College of Engineering at UMass Amherst, and published in AGU’s Geophysical Research Letters, harnesses data from the Surface Water and Ocean Topography satellite, known as SWOT. Launched by NASA in collaboration with the French space agency CNES, SWOT represents a generational leap in how humanity observes freshwater. Instead of inferring river behavior from sparse ground measurements, the satellite uses wide-swath radar altimetry to directly measure the height, width, and slope of rivers across nearly the entire globe, repeated over time as it orbits. For the first time, scientists can check global river models against actual observations rather than assumptions.

The motivation for the work is deceptively simple. River models are built on mathematical representations of how water moves through landscapes, and increasingly those models incorporate machine learning trained on whatever data exist. “Is there enough water? What’s happening with climate change? How do I operate my hydro plant? In a world of water problems, there are problems that we need a good model to solve,” Gleason explains. The trouble is that, short of measuring every river on Earth, there was historically no way to verify the underlying math at a global scale. A model might perform beautifully in the well-instrumented rivers of Europe or North America and fail catastrophically elsewhere, and no one would know.

SWOT changed that equation. By comparing the satellite’s direct measurements of river water storage against the estimates produced by state-of-the-art machine learning models, Gleason’s team produced something like a global error map for hydrology. The picture that emerges is one of broad competence punctuated by troubling blind spots. The majority of the world’s river reaches are modeled reasonably well, but a meaningful fraction—concentrated in some of the most water-critical places on the planet—are not.

The study identifies three categories of rivers where models consistently struggle: dammed rivers, rivers in arid climates, and Arctic rivers. While less than ten percent of the world’s river reaches fall into the category of serious error, the researchers emphasize that these reaches are among the most sensitive and important for water resources. That combination poses a genuine dilemma, because one of the primary uses of river modeling is to generate accurate predictions precisely in regions with limited or no ground data—places where hydropower is being developed, where agricultural and municipal water use must be planned, and where communities are preparing for a changing climate with little else to go on.

The failure of dammed rivers is perhaps the most intuitive, yet the new study is the first to definitively demonstrate the scope of the problem. Gleason illustrates the challenge with the Connecticut River, which contains a pumped hydro reservoir capable of changing the river’s depth by more than a meter per day—fluctuations far beyond anything the natural water cycle would produce. To accurately predict the amount of water in that river, a model would need to know not only that the pumped hydropower plant exists, but also the price of electricity and the cost threshold that governs the utility’s pumping schedule. In other words, the river’s hydrology is entangled with human economics, and no purely physical model of rainfall and runoff can capture that. Wherever dams and reservoirs dominate, the river is, in a real sense, an engineered artifact.

Arid regions present a different and subtler failure mode. The study found that models perform poorly in places like Australia, Central Asia, the southwestern United States, and Mexico. The likely culprit is groundwater. In dry landscapes, rivers depend heavily on subsurface water, and decades of pumping have altered those aquifers in ways that affect surface flows non-obviously and with delayed responses. A machine learning model trained on historical records may simply have no way to represent an aquifer that has been drawn down beyond anything in its training data, or to anticipate the lag between pumping decisions and their eventual expression in river levels. The result is systematic error in exactly the regions where water scarcity makes accurate forecasting most valuable.

The Arctic, meanwhile, fails for a different reason: a shortage of data itself. Machine learning excels at finding and replicating patterns, but only if patterns exist in the training data. Gleason points to Iceland as a case study. The island sits at higher elevation than much of the Arctic, is influenced by an ocean current, is highly geologically active, is covered in snow, ice, and glaciers, and possesses very little hydrological data. “If you’re a machine learning model, you’d ask yourself: what other places on the planet are like Iceland that I can learn from? Just parts of New Zealand. That’s pretty much it,” he says. Where the data are absent, the model cannot learn, and its predictions become extrapolations dressed up as forecasts.

Beyond cataloguing failures, the study carries a more philosophical punch. Models perform well on what might be called normal rivers—single-thread channels, not fed by glaciers, without estuaries, without dams. But it turns out that only about eleven percent of the rivers on Earth fit that definition. “A weird river is the norm. A dammed river is the norm. A multichannel, complicated planform river is the norm, not the exception,” Gleason says. The finding challenges the field’s basic mental image of what a river is. The tidy, free-flowing channel that dominates textbooks and training datasets is the statistical outlier, and the braided, dammed, glacially influenced, human-altered systems that dominate the real world are precisely the ones our models handle worst.

For Gleason, the study is ultimately a map of where to focus attention: identifying the areas where global hydrology cannot yet provide a reliable starting point for water information. But he also sees it as justification for a more radical possibility—abandoning models altogether in some places in favor of trusting SWOT’s direct measurements. “In many ways, we’re thinking of SWOT kind of like an early microscope,” he says. “We want to return to that way of thinking about rivers: Let’s measure them first, and let’s trust the measurements rather than the models.” If that shift takes hold, the era of inferring the world’s rivers from imperfect math may be giving way to an era of simply watching them from orbit—an empirical revolution in a field that has, until now, been flying partly blind.

Subject of Research: Validation of global river discharge models using SWOT satellite observations

Article Title: Newly available satellite data shows just how flawed some river models can be

Article References: Newly available satellite data shows just how flawed some river models can be. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: SWOT satellite, river models, hydrology, machine learning, water resources, dams, arid climates, Arctic rivers, climate change, hydropower, groundwater, UMass Amherst