Agroforestry systems promise a high multifunctionality providing cash and subsistence yields as well as other ecosystem services. Such land systems may be particularly promising for smallholders in tropical landscapes due to high labour intensity and productivity on limited land. Focusing on Madagascar, we here describe the history of agroforestry in the country and review the current literature on agroforestry outcomes as well as factors promoting and hindering agroforest establishment and maintenance. From this, we discuss the potential future of agroforestry in Madagascar. Historically, many crops farmed today in agroforestry systems were originally introduced as plantation crops, mostly in the nineteenth century. Since then, people co-opted these crops into mixed agroforestry systems, often focusing on clove, vanilla, coffee, or cocoa in combination with fruit trees or, for clove, with livestock. Other crops are also integrated, but shares are comparatively low. Overall, 27.4% of Malagasy exports are crops typically farmed in agroforestry systems, providing income for at least 500,000 farmers. Outcomes of agroforestry for biodiversity and ecosystem services are commonly researched, showing benefits over annual crops and monocultures. Social-economic outcomes, including yields, are more scarcely researched, but findings point towards financial benefits for smallholder farmers and a sense of community and collective memory. However, findings emphasize that research gaps remain in terms of geographic and crop coverage, also for ecological outcomes. Looking to the future, we highlight the need to overcome hurdles such as land tenure insecurity, financial barriers to implementation, and unstable value chains to scale agroforestry in Madagascar to the benefit of multifunctional land systems and human wellbeing.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s10457-024-00975-y.

UNASSIGNED: With increasing labor shortage and production costs, water scarcity and climate change, there is increased interest in ratooning as a green, resource-efficient technology to boost sustainable rice production, especially in China. Since the performance of ratoon rice (regenerating a second crop from the stubble left in the fields after the main harvest) and the impact of agronomic practices on its yield have shown mixed results across the world, a better understanding is needed to determine under which conditions ratoon rice performs well.
UNASSIGNED: The objectives are (i) to quantify variation in rice yield of main and ratoon crops, (ii) to assess genetic variation in and impact of agronomic practices on rice yield, focusing on the yield of ratoon crop and total yield (main and ratoon crops), and (iii) review of economic and environmental benefits of ratoon rice in comparison with single and double rice cropping.
UNASSIGNED: In researching ratoon rice, we compiled a database from 68 studies published from 2000 to 2023. Descriptive data analysis was performed.
UNASSIGNED: Studies from non-tropical regions account for about 70%. Large variation exists in the yield of ratoon crop across the studies, with lower yield from the tropics than non-tropics. The ratio of yield of ratoon crop to that of main crop also varied widely from 0.13 to 0.67 with 0.36 and 0.5 in tropics and non-tropics, respectively. The yield of ratoon crop was positively related to the yield of main crop, crop duration and nitrogen fertilizer application rate, which were generally higher in non-tropics. Hybrid varieties out-yielded inbred varieties in both main and ratoon crops in non-tropical regions. Direct seeding and AWD had a positive impact on the yield of ratoon crop. The impact of stubble cutting height was mixed. While agronomic nitrogen use efficiency (AEN) during entire ratoon rice cropping was similar to that reported for single rice cropping in previous studies, AEN for ratoon crop in tropical regions tended to be lower than those from previous studies on single rice cropping. Ratoon rice cropping reduced labor input and production cost and increased net economic return compared with double rice cropping.
UNASSIGNED: We propose a research agenda, with the focus on improvement of genetic and agronomic practices to explore the potential of ratoon rice cropping, especially in the tropics.
UNASSIGNED: This study provides insight into the progress in ratoon rice research over the past two decades globally, and specifically in the tropics.

Plant pathogens can decimate crops and render the local cultivation of a species unprofitable. In extreme cases this has caused famine and economic collapse. Timing is vital in treating crop diseases, and the use of computer vision for precise disease detection and timing of pesticide application is gaining popularity. Computer vision can reduce labour costs, prevent misdiagnosis of disease, and prevent misapplication of pesticides. Pesticide misapplication is both financially costly and can exacerbate pesticide resistance and pollution. Here, we review the application and development of computer vision and machine learning methods for the detection of plant disease. This review goes beyond the scope of previous works to discuss important technical concepts and considerations when applying computer vision to plant pathology. We present new case studies on adapting standard computer vision methods and review techniques for acquiring training data, the use of diagnostic tools from biology, and the inspection of informative features. In addition to an in-depth discussion of convolutional neural networks (CNNs) and transformers, we also highlight the strengths of methods such as support vector machines and evolved neural networks. We discuss the benefits of carefully curating training data and consider situations where less computationally expensive techniques are advantageous. This includes a comparison of popular model architectures and a guide to their implementation.

Each new generation of grassland managers could benefit from an improved understanding of how modification of nitrogen application and harvest dates in response to different weather and soil conditions will affect grass yields and quality. The purpose of this study was to develop a freely available grass yield simulation model, validated for England and Wales, and to examine its strengths and weaknesses as a teaching tool for improving grass management. The model, called LINGRA-N-Plus, was implemented in a Microsoft Excel spreadsheet and iteratively evaluated by students and practitioners (farmers, consultants, and researchers) in a series of workshops across the UK over 2 years. The iterative feedback led to the addition of new algorithms, an improved user interface, and the development of a teaching guide. The students and practitioners identified the ease of use and the capacity to understand, visualize and evaluate how decisions, such as variation of cutting intervals, affect grass yields as strengths of the model. We propose that an effective teaching tool must achieve an appropriate balance between being sufficiently detailed to demonstrate the major relationships (e.g., the effect of nitrogen on grass yields) whilst not becoming so complex that the relationships become incomprehensible. We observed that improving the user-interface allowed us to extend the scope of the model without reducing the level of comprehension. The students appeared to be interested in the explanatory nature of the model whilst the practitioners were more interested in the application of a validated model to enhance their decision making.

Rice (Oryza sativa L.) is a staple cereal in the diet of more than half of the world\'s population. Within the European Union, Spain is a leader in rice production due to its climate and tradition, accounting for 26% of total EU production in 2020. The Valencian rice area covers around 15,000 hectares and is strongly influenced by biotic and abiotic factors. An important biotic factor affecting rice production is weeds, which compete with rice for sunlight, water and nutrients. The dominant weed in Spain is Echinochloa spp., although wild rice is becoming increasingly important. Rice cultivation in Valencia takes place in the area of L\'Albufera de Valencia, which is a natural park, i.e., a special protection area. In this natural area, the use of phytosanitary products is limited, so it is necessary to use the minimum amount possible. Therefore, the objective of this work is to evaluate the possibility of using remote sensing effectively to determine the effectiveness of the application of the herbicide cyhalofop-butyl by drone for the control of Echinochloa spp. in rice crops in Valencia. The results will be compared with those obtained by using sterilisation machines (electric backpack sprayers) to apply the herbicide. To evaluate the effectiveness of the application, the reflectance obtained by the satellite sensors in the red and near infrared (NIR) wavelengths, as well as the normalised difference vegetation index (NDVI), were used. The remote sensing results were analysed and complemented by the number of rice plants and weeds per area, plant dry weight, leaf area, BBCH phenological state, SPAD index values, chlorophyll content and relative growth rate. Remote sensing is validated as an effective tool for determining the efficacy of an herbicide in controlling weeds applied by both the drone and the electric backpack sprayer. The weeds slowed down their development after the treatment. Depending on the phenological state of the crop and the active ingredient of the herbicide, these results are applicable to other areas with different climatic and environmental conditions.

Long-term application of organic and mineral fertilizers can lead to changes in the elemental composition of agroecosystem components. Both the levels of nutrients and potentially toxic elements can change, as can the potential for these elements to be available to plants through changes in soil properties. Soil and plant samples of two species (pea Pisum sativum L. and oat Avena sativa L.) were collected from plots of a long-term field experiment on the application of mineral and organic fertilizers and their combinations to gray forest soils in the Vladimir region, Russia. Soil samples from the 0-20 and 20-40 cm layers were subjected to acid digestion to determine total element content. Mobile forms of elements were extracted from topsoil samples using acetate-ammonium buffer (pH 4.8). Sample preparation of pea and oat plant organs (stems, leaves, pods/ears) included sample digestion in a microwave sample digestion system ETHOS EASY (Milestone, Italy). The elemental composition of the samples was determined by inductively coupled plasma optical emission spectrometry (ICP-OES) using Agilent 5800 ICP-OES (Agilent Technologies, USA). The dataset includes concentration data for 34 elements, including rare earth elements, in these samples collected in 2021. The dataset also contains general agrochemical characteristics of soils of the experimental groups: pH of water and salt suspension, organic carbon content, mobile forms of phosphorus. The data can be valuable to researchers developing fertilizer application systems and modeling changes in the elemental composition of agroecosystems.

Proper irrigation practice consists of applying the optimum amount of water to the soil at the right time. The porous characteristics of the soil determine the capacity of the soil to absorb, infiltrate, and store water. In irrigation, it is not sufficient to only determine the water content of the soil; it is also necessary to determine the availability of water for plants: water potential. In this paper, a comprehensive laboratory evaluation-accuracy and variability-of the world\'s leading commercial water potential sensors is carried out. No such comprehensive and exhaustive comparative evaluation of these devices has been carried out to date. Ten pairs of representative commercial sensors from four different families were selected according to their principle of operation (tensiometers, capacitive sensors, heat dissipation sensors, and resistance blocks). The accuracy of the readings (0 kPa-200 kPa) was determined in two soils of contrasting textures. The variability in the recordings-repeatability and reproducibility-was carried out in a homogeneous and inert material (sand) in the same suction range. The response in terms of accuracy and value dispersion of the different sensor families was different according to the suction range considered. In the suction range of agronomic interest (0-100 kPa), the heat dissipation sensor and the capacitive sensors were the most accurate. In both families, registrations could be extended up to 150-200 kPa. The scatter in the readings across the different sensors was due to approximately 80% of the repeatability or intrinsic variability in the sensor unit and 20% of the reproducibility. Some sensors would significantly improve their performance with ad hoc calibrations.

In-field data were collected in Costa Rica between 2018-2021 on newly planted grafted and non-grafted coffee plants grown under artificial shade nets and across an elevation gradient (1050, 1250 and 1450 m.a.s.l). The coffee plants consisted of Coffea arabica F1 hybrid plants (\'H3 i.e. Caturra cv. X Ethiopian 531\'), which were derived from a somatic embryogenesis clonal propagation process, an American C. arabica pure line (\'Villa Sarchi\') and C. canephora \'Nemaya\' (the latter two both being produced by seed). Data from eight different coffee types (including these three genotypes) and different grafting combinations (including reverse and auto-grafting) were collected. Data concerned plant traits such as grafting compatibility (plant collar diameters above and below graft union), agronomic characteristics (aerial and root traits), leaf ecophysiology (leaf gas-exchange and chlorophyll fluorescence), yield and quality attributes (bean size, peaberry percentage, WB100 and SCA note). Climate data were also included for comparison on the farm plots along the elevation gradient. Linear mixed models were used to test for effects of elevation (test sites), coffee types (grafted or non-grafted combinations) and interaction between coffee types and elevations. Least square mean estimates were calculated for significant fixed effects and Tukey tests applied for pairwise tests. A tangential hyperbola curve was used to analyse leaf gas-exchange data. These datasets and R scripts can be re-used as a guide for future analyses concerning coffee agronomy or eco-physiological interactions for other plant species. Other potential re-uses could be meta-analyses aimed at comparing coffee yield, quality, or other agronomic traits across different environmental conditions (such as under shade of an agroforestry system or across different elevation sites).

The global food system must meet the increasing demand for food, fiber, and energy while reducing environmental impacts. The UN Food System Summit (UNFSS) has made a clear call to action for a global food systems transformation. We argue that three major discrepancies remain, potentially delaying the urgent implementation of the call to action. First, Nature-based Solutions (NbS) are not sufficiently focused on agriculture, leading to funding allocation issues. Second, a mismatch of agroecology with technology innovations may slow scaling agroecological farming. Lastly, agricultural diversification must move beyond organic landscapes and into conventional agriculture. As a solution, principles of NbS should be clear on agricultural integration. Moreover, stakeholder awareness must increase that agroecology does not necessarily conflict with agricultural technologies. Future agricultural models must apply measures such as agricultural diversification in conjunction with technology innovations to then ascertain an overall timely and successful implementation of the UNFSS call to action.

A growing urban population and dietary changes increased wheat import bills in Africa to 9% per year. Though wheat production in the continent has been increasing over the past decades, to varying degrees depending on regions, this has not been commensurate with the rapidly increasing demand for wheat. Analyses of wheat yield gaps show that there is ample opportunity to increase wheat production in Africa through improved genetics and agronomic practices. Doing so would reduce import dependency and increase wheat self-sufficiency at national level in many African countries. In view of the uncertainties revealed by the global COVID-19 pandemic, extreme weather events, and world security issues, national policies in Africa should re-consider the value of self-sufficiency in production of staple food crops, specifically wheat. This is particularly so for areas where water-limited wheat yield gaps can be narrowed through intensification on existing cropland and judicious expansion of rainfed and irrigated wheat areas. Increasing the production of other sources of calories (and proteins) should also be considered to reduce dependency on wheat imports.