Agronomic Knowledge Research Articles

Analysis of Technical Efficiency on Orange Fleshed Sweet Potatoes Production Among the Smallholder Farmers in Migori County Kenya

Sweet potatoes (<i>Ipomea batatas </i>L.), particularly the orange fleshed variety, have become increasingly popular due to their ability to thrive in various environmental conditions with minimal inputs. Orange fleshed sweet potatoes (OFSPs) show potential for productivity, yet smallholder farmers in Kenya still struggle to maximize their yields. The factors influencing OFSP production efficiency among these farmers in Western Kenya have not been thoroughly examined. This study evaluated the technical efficiency of OFSP production among smallholder farmers in Migori County. Using a descriptive research design, a sample of 225 OFSP farmers was randomly selected by a cluster sampling technique. Data was collected through a structured questionnaire on inputs and selected socio-economic factors. The data was analyzed using the frontier stochastic model in STATA. Smallholder farmers estimated mean technical efficiency was 77.82% significant variables were; size of the land, availability of planting vines, access to hired labour, gender of the household decision maker, farming experience, household size, land ownership status, participation in training programs and extension services, and proximity to markets, indicating a need to improve OFSP production by 22.18%. These findings suggest that to reduce inefficiencies among smallholder OFSP farmers, targeted training programs are needed to enhance farmers' agronomic knowledge specific to OFSP production. Further, policy interventions should prioritize the provision of extension services to support and improve the performance of OFSP smallholder farmers.

A generalizable and interpretable model for early warning of pest-induced crop diseases using environmental data

Pest infestations and resulting crop diseases threaten global food security. Traditional pest and disease monitoring methods are time-consuming and prone to delays, thus necessitating the development of effective prediction strategies that facilitate early and timely detection. In response to this challenge, this research proposes a generalizable and interpretable machine learning model to predict two major rice pests—Green Leafhopper and Yellow Stem Borer—and a No Pest class using environmental data collected from various regions in India. The dataset, comprising factors such as temperature, humidity, and rainfall, underwent rigorous preprocessing. Key features like temperature difference, humidity difference, and vapor pressure deficit were engineered to enhance the model’s performance. Twelve baseline models were trained and their performance was evaluated using F1 scores and AUC values due to the imbalanced nature of the dataset. Through statistical analysis of baseline models, Random Forest, Balanced Random Forest, XGBoost, and CatBoost models, were selected for hyperparameter tuning via Optuna. The tuned CatBoost model demonstrated superior performance, achieving AUC values of 0.99 for Green Leafhoppers, 0.98 for Yellow Stem Borers, 1.00 for No Pest class and an overall F1 score of 0.9414 with a mean AUC value of 0.9912 across all classes. Additionally, Explainable Artificial Intelligence techniques, particularly SHAP, were employed to interpret the model’s predictions, identifying the relative importance of environmental factors in pest occurrence. This interpretability aligns the model’s predictions with established agronomic knowledge, enhancing its practical utility for early pest and disease detection. The generalizability of the proposed model suggests it can be adapted to other crops and regions, offering a valuable tool for early warning systems in agriculture, promoting sustainable practices, and reducing crop losses due to pests and diseases.

The Technopolitics of Agronomic Knowledge and Tropical(izing) Vegetables in Brazil

This article critically analyzes the social and political factors behind the advancement of technoscientific development in modern Brazilian agriculture. In the second half of the 20th century, Brazil underwent a rapid industrialization in the agricultural sector by more than doubling productivity in key global commodities and a widespread migration of people from rural to urban areas. Most observers point to the Brazilian Agricultural Research Corporation (Embrapa) as the technological engine that drove the industrialization of Brazilian agriculture. Existing approaches to analyze technoscientific development tend to overlook the role of the environment and individual scientists in enacting change. I argue that, especially in the case of agriculture, technoscientific development hinges on the extent to which the environment is disregarded or embraced by those who have the institutional support and capacity to innovate. To support my argument, I draw on two contrasting cases of crop development spearheaded by Embrapa scientists: the tropicalization of the carrot and participatory research on non-conventional vegetables. Through those two cases, the article demonstrates how the general and specific, the transnational and local, and the industrial and agroecological are all key contrasting factors for understanding technoscientific development in agriculture. This research is based on extensive interviews and participant observation at Embrapa’s vegetable research center near Brasilia, Brazil.

Exploring farmer’s assessment of soil quality and root yield in cassava-based cropping systems

Farmers’ knowledge of soil quality and yield assessment were evaluated among cassava farmers in southwestern Nigeria. Data were collected on farmers’ demography, farming experience, criteria for selecting a site for cassava cultivation and preferences for those criteria, farmers’ methods of yield prediction and how it compares with the scientific approach, farmers’ agronomic knowledge and how it relates to the realities of climate change and soil fertility decline. The modal age class of the farmers is 45–55 years, and most of the farmers are male. The results show that farmers use a combination of soil and vegetation-based criteria to assess soil quality from which the decision to cultivate a given land is made. Among the soil-based criteria, soil drainage, colour, and depth rank the most and most used. Most of the farmers assessed yield before harvesting through green healthy leaves (25 %), large and shiny stems (25 %) and soil cracks (50 %). The predicted and measured cassava yields on farmer-managed farms were comparable, with an R2 of 0.63, although farmers overestimated the cassava root yield. There is a unanimous consensus among farmers that yield has declined in the last decade, with a mean of 36 %. The main cause of yield decline was attributed to declining rainfall and poor soils. Our study concluded that farmers have a good understanding of soil health and cassava agronomy through a process of trial and error and ingenuity as farmers’ local indices were consistent with conventional soil health indicators. It was suggested that, by recognising and incorporating traditional methods used by farmers to assess soil quality, we could enhance soil management strategies and raise productivity at the farm level.

Technical efficiency of teff farms in Ethiopia: Do neighborhood effects matter? A copula stochastic frontier and spatial Durbin regression

Teff is essential to most Ethiopians, but its production is hampered by farmers' characteristics and spatially related neighborhood variables. This study analyzes the neighborhood effect on the technical efficiency of teff farms in Ethiopia using panel data from the Ethiopian socioeconomic survey. The spatial Durbin regression models (SDM) and Copula stochastic frontier were used with 858 teff-growing farmers. The mean value of teff's technical efficiency was found to be 53 %, meaning that farmers had a 47 % likelihood of improving teff farm efficiency. The results from the SDM indicate a significant contribution of neighborhood effects for improving technical efficiency in teff farms. Thus, policymakers could explore implementing localized interventions and knowledge-sharing initiatives to disseminate best practices, innovative technologies, and agronomic knowledge within specific spatial clusters. By doing so, they can leverage the observed influence of neighborhood dynamics on teff farm efficiency.

Can information constraints explain the low efficiency in premium quality rice cultivation? Evidence from smallholder farmers in Bangladesh

AbstractThe integration of smallholder farmers into emerging value chains for fine‐grain and aromatic ‘premium quality rice’ (PQR) could prove to be crucial to improving rural livelihoods in Bangladesh, though efforts could be constrained by farmers' differing levels of agronomic knowledge. Based on a pre‐analysis plan, we analyse farmers' ability to efficiently allocate production enhancing inputs in PQR cultivation based on a survey of 1420 farmers in key PQR producing areas. Farmers received a hypothetical budget to allocate to six different inputs advised for efficient production of PQR, mimicking familiar production decisions made seasonally on their own farms. Our results suggest that even without budget or input access constraints farmers tend to inefficiently allocate inputs in PQR in this hypothetical setting. In particular, they tend to overspend on seeds, fertiliser and pesticides. Farmers with better access to agricultural information, such as through PQR specific extension services, conversely reach substantially higher efficiency scores and decided to spend significantly less on fertiliser. Without future adjustments such as more targeted extension services, implied higher production costs will likely lower the profitability of PQR cultivation for smallholder farmers, thereby limiting potential income gains. Besides these economic concerns, excessive input use is associated with environmental externalities. Improved efficiency is therefore desirable from both an economic and environmental standpoint.

Nurturing Crops, Enhancing Soil Health, and Sustaining Agricultural Prosperity Worldwide through Agronomy

Agronomy is the science and technology of crop and soil management for producing food, fiber, feed, and fuel. As the global population continues to rise, agronomy will play an increasingly vital role in meeting escalating food demands while sustaining natural resources. This review highlights major advancements in agronomic research and practice that enhance crop yields, soil health, and agricultural sustainability worldwide. Key topics include plant breeding for improved crop varieties, optimized plant nutrition and soil fertility management, efficient water management, integrated pest management (IPM), conservation agriculture techniques, precision agriculture technologies, and climate-smart farming approaches. Modern agronomy strategies such as drought-tolerant cultivars, micro-irrigation, reduced tillage systems, site-specific input applications, and integrated cropping systems can significantly improve productivity and resilience. However, continued innovation and diffusion of agronomic knowledge is crucial to nourish growing populations while protecting environmental quality. Collaborative efforts among researchers, educators, policy makers, and farmers will be imperative to apply advanced yet context-specific agronomic solutions that sustain agricultural prosperity into the future.

Advancements in Agronomic Practices for Sustainable Crop Production: A Review

This review provides a comprehensive analysis of the advancements in agronomic practices that contribute to sustainable crop production. In the context of escalating global food demand, climate change, and environmental concerns, sustainable agriculture has become a pivotal focus. This paper systematically examines the evolution of agronomic practices from traditional methods to contemporary innovations, highlighting the integration of technology, sustainability, and socio-economic factors in modern agriculture. The historical perspective of agronomic practices reveals a transition from rudimentary, labor-intensive methods to technologically driven, precision-based approaches. Traditional practices, while sustainable, often faced limitations in scalability and efficiency. The advent of the Green Revolution marked a significant shift, introducing high-yield crop varieties and synthetic inputs. However, the long-term ecological impacts of these methods prompted a reevaluation towards more sustainable practices. Contemporary advancements in agronomy are largely characterized by precision agriculture, which employs satellite and drone technology, sensor-based monitoring systems, and AI applications. These tools have revolutionized farming by enabling precise resource management and data-driven decision-making. In tandem, the rise of organic farming and integrated pest management reflects a growing emphasis on ecological balance and reduced chemical inputs. The paper also delves into the crucial role of education and extension services in disseminating modern agronomic knowledge, particularly in developing regions. Government policies and international regulations are analyzed for their impact on promoting sustainable practices. Additionally, the review addresses the vital aspect of environmental sustainability, focusing on strategies for carbon footprint reduction, biodiversity preservation, and ecosystem services enhancement. Technological innovations, especially in genetics and digital tools, are identified as key drivers in shaping future agronomic practices. The potential of CRISPR in crop improvement and the application of big data and IoT in farming are discussed as future trends.

Sustainability transitions in the making in agroecosystems: Changes in research scope and methods

CONTEXTThe urgency of redesigning the way food is produced and consumed has implications for the systems perspective of agronomy. An increasingly important strain of research addresses the ‘how-to’ question associated with the transformation of ‘maldesigned’ agricultural systems and produces ‘transformative knowledge’ as part of transitions in-the-making. Such knowledge production goes beyond ‘transformation knowledge’ resulting from the more classical analytical agronomic approaches and stances. Contributing to transitions in-the-making thus calls for systems agronomists to question their profession's why, what, and how. OBJECTIVEThis special issue brings together contributions by systems agronomists collaborating with other scientists and societal actors revealing the less visible stream of fine-grained work to support transitions in-the-making in agroecosystems. METHODSIn this introductory paper, changes in research scope and methods emerging from the contributions are highlighted to reinforce the engaged systems agronomist's roles in contributing to food system transitions. RESULTS AND CONCLUSIONSThree results stand out: 1) Contributing to transitions in-the-making necessitates going beyond analysis-oriented research stances to address the ‘how’ of transformative knowledge, and move beyond the more time-efficient dominant ‘what’ and ‘why’ questions; 2) Meta-methods are emerging about how to adapt and embed existing agronomic knowledge products within change processes. Reflection on the overarching transformation dynamics and on how to articulate knowledge products and change process will constitute an important extension of the systems agronomist's methods; 3) The efficacy of scientific transformative knowledge development may be enhanced if the notion of design is used more strictly in relation to engagement in societal change. SIGNIFICANCEThe need to respond to the how-to questions associated with the transformation of the way food is produced and consumed requires systems agronomists to reflect on their stance in the research process, the way they design their research inclusively, and the way they shape their methods. This Special Issue provides promising examples of the (re-)emerging strain of engaged systems agronomists.

Status of underutilized crop production: Its potentials for mitigating food insecurity

AbstractGlobally, the gap between the production of nutritious quality crops and their consumption is widening, resulting in high rates of malnutrition and micronutrient deficiencies. Our goal is to examine the rich tapestry of studies on underutilized crops (UUCs) and consider their potential role in alleviating food and nutritional insecurity. Published research articles on UUC were retrieved from databases, such as Web of Science, Google Scholar, and Scopus. The key terms used were “UUC,” “neglected crops,” and lists of UUC, which were frequently cited in literature. A bibliometric innovative approach, such as reference co‐citation, co‐citation, and co‐word analysis, was used. We observed an annual growth rate of 6.4% in UUC research outputs with the number of annual publications increasing from less than 700 article publications in 2000–3000 article publications in 2021. It is also developing into a noticeably interdisciplinary research field with more collaborations and institutional affiliations. The most relevant affiliated countries in UUC‐related studies are developed and transition countries, such as the United States of America, China, India, the United Kingdom, South Africa, Australia, and Germany. The pressing and emerging research studies in UUC‐related studies are genetic diversity and identification, agronomic knowledge, seed improvement, and environmental stresses. There is a shift in the focus of UUC studies, which provides a deeper understanding of the global trends and directions that have evolved in UUC studies over the past two decades. This result provides researchers and policymakers in the field of UUC with a useful tool for decision‐making on UUC.

DIGITAL AGRICULTURE – BASICS AND PREREQUISITES FOR DEVELOPMENT

Nowadays, the results achieved both from an ecological and an economic point of view are extremely important for the agricultural sector. Higher environmental performance cannot be achieved if the competitiveness of agricultural sectors declines. To increase the sustainability of the sector, better economic results must be achieved, which will lead to an opportunity to increase investments to protect the environment. This is where the need comes from, in this new, developing era, to think about "new" agriculture responding to the new circumstances. Without turning our backs on the past, both with its mistakes and its achievements. Vast agronomic knowledge can be integrated into digital innovation and mobilized to workforce the r better economic and environmental performance of farms and for the benefit of citizens and consumers. The report summarizes the main technological innovations. The aim is to outline the parameters and possibilities of modern agriculture to respond to the developing digital world. Based on the definition of the review, summaries and conclusions are made about the development of digital agriculture in the world

Review of agronomic research on the milpa, the traditional polyculture system of Mesoamerica

The milpa system is the basis of traditional agriculture in Mesoamerica. It is based on a polyculture of maize (Zea mays L.), bean (Phaseolus spp.) and squash (Cucurbita spp.) and is associated with a great diversity of crops and wild species. The milpa has great cultural and historical importance but can also be highly productive and provide sufficient and healthy diets for smallholder farmers. The milpa system is practiced less and less however, mainly due to socioeconomic changes, but also due to a lack of agronomic knowledge adapted to current conditions. To provide a starting point for new agronomic research, we reviewed the scientific literature to identify the current knowledge on milpa agronomy and determine research priorities to further improve the system. Given the wide diversity of conditions under which milpa is practiced, agronomic research is lacking, but indicates that improvements to the milpa can be made in all aspects of the milpa. 176 research articles on the milpa system were found in databases, of which 61 treated agronomic research. The main agronomic topics described in literature are crop variety, soil fertility management, weed management and productivity. Most research has focused only on maize and studies reporting on the associated crops are scarce. More research on all aspects of the milpa is needed to understand and improve the agronomy of the system under the changing conditions of modern agriculture. Reducing the workload associated with the milpa, as well as soil fertility and weed management can be identified as research priorities.

Sustainable Crop Recommendation System Using Soil NPK Sensor

The effective management of nutrient resources in agricultural practices is crucial for optimizing crop yields and ensuring sustainable farming. Traditionally, farmers have relied on manual methods or expert knowledge to determine the appropriate amount and type of nutrients required by crops. However, these methods often lack precision and can lead to suboptimal fertilization, resulting in reduced productivity and environmental degradation. In recent years, advancements in sensor technology have paved the way for more accurate and efficient crop management systems. One such innovation is the NPK sensor, which enables real-time monitoring of soil nutrient levels. Our proposed system utilizes NPK sensor data to offer personalized fertilization recommendations to farmers. The system integrates sensor technology, machine learning algorithms, and agronomic expertise to provide precise and tailored nutrient recommendations based on the specific requirements of different crops and soil conditions. The system collects data from NPK sensors deployed in the field that includes soil nutrient levels. Machine learning algorithms analyze this data to identify patterns and correlation between nutrient levels and crop performance. By leveraging historical data and agronomic knowledge, the system can generate accurate and timely recommendations for nutrient application. In conclusion, the crop recommendation system presented here offers a novel approach to crop management by leveraging NPK sensor technology and machine learning. By providing accurate and personalized nutrient recommendations, the system has the potential to revolutionize modern agriculture, enhancing productivity while promoting environmental stewardship. Further research and field trials are needed to validate and refine the system’s performance and usability, but the preliminary results show promising potential for the adoption of such system in real-world agricultural settings.

Poverty, education and family health limit disease management and yields on smallholder cocoa farms in Bougainville

Abstract Decades of research and training activities have not reduced crop disease or pest losses, increased average yields, or alleviated poverty among smallholder cocoa farmers. Failure has been variously attributed to farmer ignorance or indifference, sociocultural barriers, inappropriate technologies, risk aversion, or uncertain financial incentives. However, attributing a single cause overlooks the complexity resulting from unique intersections of challenges facing smallholder cocoa farming families in different regions. We investigated smallholder farmer productivity from a transdisciplinary One-Health perspective and found that among cocoa farming families in the Autonomous Region of Bougainville (Papua New Guinea; PNG) the most significant barriers to increased productivity and higher incomes were low levels of education, financial insecurity, and poor family health. These factors interact to reduce the availability of productive labour needed to implement knowledge and technologies from extension and training programs, and it is labour availability rather than agronomic knowledge that limits the effectiveness of farmer training. If the goal is to increase productivity and thus livelihoods of smallholder cocoa farmers, One Health approaches that engage with communities to include relevant education, health, and income diversification packages are likely to be more successful than conventional agronomically-based farmer training. Healthy crops depend on healthy, educated farming families. One Health Impact Statement Conventional disciplinary-based assistance programs assume that it is technical knowledge that limits food production, incomes, and living standards in rural, low-income communities. This approach overlooks how the intersection of knowledge with incentives and human capacity constrains technology adoption. Significant global investments in disciplinary-based programs have not improved smallholder cocoa yields, incomes, or family health. Our team collected data on demography, farming activities, incomes, health, and nutrition from families in 33 cocoa-growing villages in Bougainville. Results were presented back to the communities, Government and scientific community and their perspectives incorporated. Our transdisciplinary interpretation revealed that among the diverse factors examined, education levels, income diversification, and family health have the strongest impacts on poverty. This deeper understanding of the intersecting factors in poverty informs the prioritisation and development of more effective, One Health approaches to poverty alleviation.

Mapping the spatial variability of Botrytis bunch rot risk in vineyards using UAV multispectral imagery

The fungus Botrytis cinerea causes severe diseases in many crops. In grapevines, it causes Botrytis bunch rot (BBR), one of the most reported diseases worldwide. It affects all herbaceous organs of the vine, especially the ripe berries, causing significant reductions in yield and wine quality. Botrytis detection models traditionally focus on temporal analysis at a specific spatial location, ignoring the study of the spatial variability of the crop. Unmanned aerial vehicles (UAVs) equipped with multispectral cameras can provide high-resolution images that can be valuable information to develop a tool for aerial pest detection. This paper proposes an algorithm to assess the risk of Botrytis development in a vineyard in Spain, using as input products generated by UAV imagery: DTM (Digital Terrain Model), NDVI (Normalised Difference Vegetation Index), CHM (Canopy Height Model) and LAI (Leaf Area Index). They represent the height and architecture of the canopy, the topography and the plant status. Healthy vines were significantly different from vines affected by Botrytis (p < 0.05) in each of these variables, supporting the consistency of using these inputs for the model. This methodology combines photogrammetric, spatial analysis techniques, and machine learning classification methods with deep vineyard-related agronomic knowledge to produce heatmaps with acceptable accuracy (R² > 0.7) that may support vineyard managers in understanding the spatial variability of the disease, allowing the spatial 2D visualisation of the risk of BBR disease development and, potentially, resulting in higher operational efficiency and reducing phytosanitary treatments, as well as economic costs. Furthermore, the present work takes advantage of imaging technologies that provide information about any location in the field, not only about specific points in the vineyard, suggesting that UAV imagery is appropriate to measure the likelihood of BBR development within the vineyard, highlighting the importance of efficient disease management based on spatial variability.

Getting the message out: Information and communication technologies and agricultural extension

AbstractThere has been much optimism about the potential of information and communication technologies (ICTs) to provide agricultural extension services to remote households. Yet, little is known about how different communication methods fare, and, moreover, whether different segments of the population adopt information communicated via different means equally. We conduct a randomized controlled trial comparing the effectiveness of three ICTs—radio, voice response messages, and a smartphone app—with traditional extension training in communicating fertilizer management practices across four districts in rural Nepal. We find that farmers in the smartphone app and the extension training programs are on average 8.4 and 13 percentage points more likely to adopt top dressing fertilizer practices compared to control farmers, statistically significant at the 1% and 5% levels, respectively. Farmers in the smartphone app treatment achieve the highest agronomic literacy test scores, 7.8 percentage points higher than the control, statistically significant at the 1% level. In contrast, farmers receiving radio or voice response messages were not more likely to adopt the same fertilizer recommendations nor show improved specific or general agronomic knowledge relative to control farmers. Our results suggest that smartphone apps are more cost effective at inducing farmer knowledge and technology adoption than extension trainings. Heterogeneous treatment effects, however, reveal that a targeted ICT approach may be more effective in disseminating extension advice.

Interactive soybean variable‐rate seeding simulator for farmers

AbstractSeed expense is a critical component affecting soybean (Glycine max L.) production cost. Achieving potential benefits of adjusting seeding rates by yield environment requires translation from theory to practice through decision support tools. This article introduces the soybean variable‐rate seeding (VRS) simulator, an interactive web‐tool designed to facilitate VRS decisions. A group of agronomists and farmers provided input and feedback to the tool's original design, usability, interactive features, and tool outputs. The simulator generates variable rate seeding recommendations by (a) aggregating and classifying 3 yr of historical soybean yield maps using either yield level (low, medium, or high) or temporal yield stability (stable or unstable) classes, and (b) estimating the difference between the farmers current uniform seeding rate and the optimal seeding rates calibrated for different yield environments of the U.S. Midwest region. For each yield environment, the tool suggests a seeding rate change accompanied by an expected yield increase. Marginal economic and break‐even price analyses of each VRS prescription choice are also provided considering soybean plant survival rate, seed cost, market price, and crop production costs. A step‐by‐step description is provided using a dataset of real, historical, on‐farm soybean yield maps to illustrate the tool's features and limitations. Simulated results indicate that compared to typical fixed seeding rate choices, following the tool's recommendations could improve profits between US$5 and 57 ha–1. The development of this tool contributes to a growing trend of user‐friendly interfaces that assist in translating core agronomic knowledge into practical solutions.

The Spread and Regional Development of Wheat Farming in the Yellow River Valley under the Han Empire

We synthesize recent archaeological discoveries on the spread of Han wheat farming, including archaeobotanical evidence, artefacts related to wheat farming and wheat flour processing, and discovered texts such as wooden slips. We cross-examine the archaeological data with transmitted historical records within the wider social and cultural contexts of the Yellow River valley and adjacent regions. We conclude that the spread of wheat farming in the Middle Yellow River region was slower than that of the Lower Yellow River region due to environmental and social reasons. After Emperor Wu's era, wheat farming began to take off in both regions, which was characterized by its expanding geographic distributions, its increasing importance in the imperial agricultural economies and its growing recognition by the society. The beneficial factors, including favorable climate-environmental conditions, accumulating agronomic knowledge, technological innovations and other factors, and changing dietary traditions played diverse roles in the regional development of wheat farming in these regions.

Detection of Irrigated Permanent Grasslands with Sentinel-2 Based on Temporal Patterns of the Leaf Area Index (LAI)

Conventional methods of crop mapping need ground truth information to train the classifier. Thanks to the frequent acquisition allowed by recent satellite missions (Sentinel 2), we can identify temporal patterns that depend on both phenology and crop management. Some of these patterns are specific to a given crop and thus can be used to map it. Thus, we can substitute ground truth information used in conventional methods with agronomic knowledge. This approach was applied to identify irrigated permanent grasslands (IPG) in the Crau area (Southern France), which play a crucial role in groundwater recharge. The grassland is managed by making three mows during the May–October period, which leads to a specific temporal pattern of leaf area index (LAI). The mowing detection algorithm was designed using the temporal LAI signal derived from Sentinel 2 observations. The algorithm includes some filtering to remove noise in the signal that might lead to false mowing detection. A pixel is considered a grassland if the number of detected mows is greater than 1. A data set covering five years (2016–2020) was used. The detection mowing number was conducted at the pixel level, and then the results were aggregated at the plot level. An evaluation data set including 780 plots was used to assess the performances of the classification. We obtained a Kappa index ranging between 0.94 and 0.99 according to the year. These results were better than other supervised classification methods that include training data sets. The analysis of land-use changes shows that misclassified plots concern grasslands managed less intensively with strong intra-parcel heterogeneity due to irrigation defects or year-round grazing. Time series analysis, therefore, allows us to understand different management practices. Real land-use change in use can be observed, but long time series are needed to confirm the change and remove ambiguities with heterogeneous grasslands.

Diagnostic Appraisal of the Sorghum Farming System and Breeding Priorities in Sierra Leone

Understanding demographic structures, production constraints, and trait preferences is essential for setting up crop breeding goals and enhancing adoption strategies for new varieties. The objective of this study was to document the sorghum (Sorghum bicolor L. Moench) production constraints and preferred sorghum traits to guide breeding in Sierra Leone. A participatory rural appraisal was used to collect data from 210 farmers across seven districts in Sierra Leone in 2019. Results showed that all sorghum varieties in cultivation are landraces. Poor access to fertilizer (91%), lack of suitable varieties (85%), poor agronomic knowledge (79%), low yielding varieties (78%), storage pests (75%), field pests (67%), low soil fertility (52%), lack of market (49%), sorghum disease (43%), drought (16%), and heavy rainfall (12%) are key production constraints limiting sorghum production. Farmers expressed interest in adopting new varieties with high yield (99%), disease (84%) and pest (81%) resistance, drought tolerance (50%), white grain (59%), and short height (53%). The prioritized traits will form the basis for farmer-oriented sorghum breeding.