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Understanding the spatiotemporal patterns and driving mechanisms of cropping patterns' evolution tailored to local conditions is crucial for the effective allocation of black soil in northeast China and the advancement of agricultural development. This study utilized the Google Earth Engine platform to extract the spatial distribution data of major grain crops in northeast China for the year 2022. Using crop classification data from 2000 to 2022, the spatial overlay analysis method identified cropping pattern types based on spatial and temporal changes. The primary cropping patterns identified were continuous maize cropping, maize–soybean rotation, mixed cropping, and continuous soybean cropping. Simultaneously, this research constructed three distinct crop periods: Period I (2000–2002), Period II (2010–2012), and Period III (2020–2022). Over three periods, these patterns covered 94.73%, 88.76%, and 86.39% of the area, respectively. The evolution of the dominant cropping pattern from Period I to Period II involved the transition from continuous soybean cropping to continuous maize cropping, while from Period II to Period III, the main shift was from continuous maize cropping to maize–soybean mixed cropping. From a spatial perspective, since Period I, maize has increasingly replaced soybean as the dominant crop, with continuous maize cropping expanding northward and continuous soybean cropping contracting. The maize–soybean rotation area also migrated northward, particularly in the core area of the Songnen Plain, evolving mostly into continuous maize cropping. Maize cropping areas exhibited significant regional characteristics, being densely distributed in the Sanjiang Plain and Liaohe Plain, and along major tributaries in northeast China. Consequently, the interplay of the natural environment, economic policies, and agricultural technologies drove these changes. The findings offer valuable insights for optimizing cropping patterns and developing rotation systems in northeast China. [ABSTRACT FROM AUTHOR]

This article explores the topic of plant-soil nitrogen management strategies and their importance in plant growth. It summarizes key findings from various studies on nitrogen management, including the effects of different nitrogen forms on soil health and crop productivity, the influence of cropping patterns on soil nitrogen transformations, and the effects of biochar and straw mulching on plant productivity. The article also discusses the negative impacts of excessive nitrogen fertilizer use and highlights the importance of understanding and enhancing soil nitrogen status sustainably. It briefly mentions a study on the decomposition of fine roots in forest ecosystems and its contribution to the release of nitrogen and phosphorus. [Extracted from the article]

Cropping patterns, including intercropping, are recognized as sustainable agricultural practices in many parts of Africa. However, there is a lack of data and information regarding their extent and specific locations. This study aims to understand the dynamics of these cropping patterns using hyperspectral satellite data. We examined the spectral reflectance of maize and intercropped maize (imaize) fields using DESIS hyperspectral satellite data during the flowering growth phase for discrimination and classification. We used mean reflectance spectra, coefficient of variation, and the Mann–Whitney U-test to characterize the cropping patterns and identify optimal spectral bands. Principal component analysis (PCA) was used to reduce the dimensionality of the hyperspectral data, followed by RF classification. Our findings reveal that the optimal spectral bands for classification fell within the 730–1000 nm range, with the spectral band at 849.8 nm being the most prominent. The RF algorithm performed well, achieving an overall accuracy of 80%. The maize class was distinguished with high precision (0.9) and recall (0.8), resulting in an F1-score of 0.9, indicating a robust ability to accurately identify and classify most of the maize class. In comparison, the imaize class exhibited lower precision (0.6) but a reasonable recall (0.7), leading to an F1-score of 0.6. These findings highlight the potential of using DESIS hyperspectral satellite data in conjunction with PCA and RF for the classification of maize cropping patterns. Additionally, our findings suggest avenues for further research using full waveform hyperspectral satellite data, such as EnMAP and PRISMA, to enhance our understanding of spectral dynamics among different cropping patterns.

Understanding the spatiotemporal patterns and driving mechanisms of cropping patterns’ evolution tailored to local conditions is crucial for the effective allocation of black soil in northeast China and the advancement of agricultural development. This study utilized the Google Earth Engine platform to extract the spatial distribution data of major grain crops in northeast China for the year 2022. Using crop classification data from 2000 to 2022, the spatial overlay analysis method identified cropping pattern types based on spatial and temporal changes. The primary cropping patterns identified were continuous maize cropping, maize–soybean rotation, mixed cropping, and continuous soybean cropping. Simultaneously, this research constructed three distinct crop periods: Period I (2000–2002), Period II (2010–2012), and Period III (2020–2022). Over three periods, these patterns covered 94.73%, 88.76%, and 86.39% of the area, respectively. The evolution of the dominant cropping pattern from Period I to Period II involved the transition from continuous soybean cropping to continuous maize cropping, while from Period II to Period III, the main shift was from continuous maize cropping to maize–soybean mixed cropping. From a spatial perspective, since Period I, maize has increasingly replaced soybean as the dominant crop, with continuous maize cropping expanding northward and continuous soybean cropping contracting. The maize–soybean rotation area also migrated northward, particularly in the core area of the Songnen Plain, evolving mostly into continuous maize cropping. Maize cropping areas exhibited significant regional characteristics, being densely distributed in the Sanjiang Plain and Liaohe Plain, and along major tributaries in northeast China. Consequently, the interplay of the natural environment, economic policies, and agricultural technologies drove these changes. The findings offer valuable insights for optimizing cropping patterns and developing rotation systems in northeast China.

Efficiently optimizing cropping patterns is crucial for the sustainable agriculture development, especially in arid regions like Saudi Arabia. This study specifically aimed to gradually optimize cropping patterns in Saudi Arabia over next decade. The cropping pattern of 2023 according to ministry of environment water and agriculture was assessed to identify crops with potential for optimization based on economic revenue, water consumption. Subsequently, three optimization strategies were devised: maximizing total income, minimizing total water consumption, and maximizing productivity. The strategy focused on maximizing income projected a remarkable 17.6/% increase in total agricultural income by simply increasing the current water resources by 4.2%. On the other hand, the strategy aimed at minimizing water consumption could save 13.4% of water resources, but it would result in 4.6% reduction in total income. Alternatively, the strategy prioritizing productivity maximization would lead to a significant 15.6% increase in total income while reducing water resources by 0.7%.

Intercropping – the planting of more than one crop in the same plot of land – is a prevalent agricultural management practice which can be used for risk reduction. Despite its widespread use, intercropping is not commonly reported in agricultural statistics, resulting to very limited spatially disaggregated information about its prevalence. Remote sensing-based approaches to detect and estimate the area of cropping patterns like intercropping require good understanding of the spectral response of (intercropped) crops at different crop growth phases. This study integrates field surveys, farmer interviews and temporal Sentinel-2 data from four crop growth phases and the post-harvest period of maize and intercropped maize (imaize). The goal is to identify the optimal crop growth phases, spectral regions and vegetation indices (VIs) that can accurately discriminate the two cropping patterns. We computed p-values for the spectral bands using Mann-Whitney U test and identified critical crop growth phases. Classification of maize and imaize cropping patterns was performed using random forest classifier. Our spectral analysis revealed effective discrimination between maize and imaize cropping patterns during the vegetative (in all spectral bands) and flowering-yield phases (in Blue, Green, Red, RE704, RE783, NIR833, NIR865). The most suitable VIs contained red-edge and near-infrared spectal bands. Utilizing spectral data and VIs from vegetative and flowering-yield phases, we achieved optimal discrimination during the vegetative phase (user’s accuracy of 100 % and producer’s accuracy of 100 %). However, accuracy decreased during the flowering yield phase (overall accuracy of 87 % for all spectral bands). The highest classification results using all spectral bands at the flowering yield phase resulted in 80 % producer’s accuracy for maize and 100 % for imaize. This study illustrates the utility of temporal Sentinel-2 spectral data for identifying the critical crop growth phase, spectral regions and VIs for cropping patterns classification, particularly for intercropping.

ABSTRACTMapping and monitoring the distribution of croplands and crop types support policymakers and international organizations by reducing the risks to food security, notably from climate change and, for that purpose, remote sensing is routinely used. However, identifying specific crop types, cropland, and cropping patterns using space-based observations is challenging because different crop types and cropping patterns have similarity spectral signatures. This study applied a methodology to identify cropland and specific crop types, including tobacco, wheat, barley, and gram, as well as the following cropping patterns: wheat-tobacco, wheat-gram, wheat-barley, and wheat-maize, which are common in Gujranwala District, Pakistan, the study region. The methodology consists of combining optical remote sensing images from Sentinel-2 and Landsat-8 with Machine Learning (ML) methods, namely a Decision Tree Classifier (DTC) and a Random Forest (RF) algorithm. The best time-periods for differentiating cropland from other land cover types were identified, and then Sentinel-2 and Landsat 8 NDVI-based time-series were linked to phenological parameters to determine the different crop types and cropping patterns over the study region using their temporal indices and ML algorithms. The methodology was subsequently evaluated using Landsat images, crop statistical data for 2020 and 2021, and field data on cropping patterns. The results highlight the high level of accuracy of the methodological approach presented using Sentinel-2 and Landsat-8 images, together with ML techniques, for mapping not only the distribution of cropland, but also crop types and cropping patterns when validated at the county level. These results reveal that this methodology has benefits for monitoring and evaluating food security in Pakistan, adding to the evidence base of other studies on the use of remote sensing to identify crop types and cropping patterns in other countries.

Farmers in Kuripan Village cultivate cassava plants. Some farmers plant to get the tubers and others grow to harvest the leaves (oyek). The harvest obtained has not contributed significantly to the income of the farmer's household. For cassava tubers it takes 7 months to harvest while oyek takes a month to harvest. Low prices and market opportunities through middlemen have prevented farmers from relying on their land optimally. As a village assisted by the University of Muhammadiyah Jakarta, the Community Service team provides training to optimized land use by diversifying and regulating cropping patterns to cassava farmers. The targets and outcomes, first, increase the knowledge and skills of farmers to choosing the right plants according to the physical conditions to diversification. Through crop diversification, it will minimize crop failure because more than one type of plant is planted. Second, farmers have skills to design appropriate cropping patterns so the farmers have income continuity throughout the year and loss of soil nutrients at harvest can be minimized so that soil fertility can be save.

Utilizing available water resources efficiently is crucial to address both our present and future requirements and plays a vital role in safeguarding food security. This current investigation deals with assessment and optimizing water footprint (WF) and virtual water flow (VWF) for primary crops in Banas River Basin (BRB) using AquaCrop model with local datasets and district-level estimates. VWF in the basins were estimated by multiplying the WF of crops with the amount exported/imported, which is determined based on the difference between production and consumption in the basin. The possibility of changing the cropping patterns was evaluated for the potential reduction of the blue WF. Annual WF from primary crops in the basin amounts to 19,255 MCM/yr (70 % green, 21 % blue and 10 % grey WF, respectively). Banas basin is a net exporter of agriculture commodities with nearly 7391 MCM/yr of water flowing out of the basin due to agricultural exports of which approximately 265 MCM/yr is virtual blue water outflow. Crops having low economic water productivity of blue water are being grown in vast areas resulting in a high blue WF. The optimizing the cropping pattern can result in a 5–42 % lower blue water footprint with 11–39 % higher economic output under different scenarios with and without considering the consumption needs. Changing the cropping pattern and making trade plan to optimize the crop import/exports can be viable option for tackling the blue water scarcity issues in the basin. WF can be managed sustainably by improving water resource allocation for better economic, social, and environmental productivity and going for less aggressive agricultural production.

We investigate how the receipt and amount of domestic or international transfers influences household decisions regarding farm investment and the selection of capital and labour‐intensive crops. We argue that, even though recipient households may use additional income to increase agricultural investment, investment can fall in the short run if labour constraints arising from the migrant member's absence are binding and capital accumulation is suboptimal. Employing a set of endogenous treatment estimates, we test this hypothesis on data from 5636 rural households in Pakistan. Our findings show a substantial difference between recipient and non‐recipient households in terms of their economic behaviour. Recipient households make 100% less agricultural investment and generate 82% less production compared to non‐recipient households. The estimates are found to be robust when tested with alternate empirical techniques (Heckman Selection and matching). The impact is stronger in the case of households that receive domestic transfers, with 100% less farm investment and 77% less production than non‐recipient households. Remittances result in a decrease in production of both capital‐ and labour‐intensive crops, reflecting a decline in overall farm activity. Similar farm investment and cropping patterns are observed relative to the amount of remittances received. The results are robust to different model specifications and estimation procedures. [ABSTRACT FROM AUTHOR]

Mapping and monitoring the distribution of croplands and crop types support policymakers and international organizations by reducing the risks to food security, notably from climate change and, for that purpose, remote sensing is routinely used. However, identifying specific crop types, cropland, and cropping patterns using space-based observations is challenging because different crop types and cropping patterns have similarity spectral signatures. This study applied a methodology to identify cropland and specific crop types, including tobacco, wheat, barley, and gram, as well as the following cropping patterns: wheat-tobacco, wheat-gram, wheat-barley, and wheat-maize, which are common in Gujranwala District, Pakistan, the study region. The methodology consists of combining optical remote sensing images from Sentinel-2 and Landsat-8 with Machine Learning (ML) methods, namely a Decision Tree Classifier (DTC) and a Random Forest (RF) algorithm. The best time-periods for differentiating cropland from other land cover types were identified, and then Sentinel-2 and Landsat 8 NDVI-based time-series were linked to phenological parameters to determine the different crop types and cropping patterns over the study region using their temporal indices and ML algorithms. The methodology was subsequently evaluated using Landsat images, crop statistical data for 2020 and 2021, and field data on cropping patterns. The results highlight the high level of accuracy of the methodological approach presented using Sentinel-2 and Landsat-8 images, together with ML techniques, for mapping not only the distribution of cropland, but also crop types and cropping patterns when validated at the county level. These results reveal that this methodology has benefits for monitoring and evaluating food security in Pakistan, adding to the evidence base of other studies on the use of remote sensing to identify crop types and cropping patterns in other countries. [ABSTRACT FROM AUTHOR]

Conventional and scientific cropping patterns are important in realizing the sustainable utilization of Black soil and promoting the high-quality development of agriculture. It also has far-reaching significance for protecting Black soil and constructing the crop rotation system to identify the cropping patterns in Northeast China and analyze their spatio-temporal dynamic change. Using the geo-information Tupu methods and transfer land matrix, this study identified the cropping patterns and their spatio-temporal change based on remote sensing data for three periods, namely 2002–2005, 2010–2013, and 2018–2021. The main results revealed that the maize continuous, mixed cropping, maize-soybean rotation, and soybean continuous cropping patterns were the main cropping patterns in Wangkui County, with the total area of the four patterns accounting for 95.28%, 94.66%, and 81.69%, respectively, in the three periods. Against the backdrop of global climate warming, the cropping patterns of continuous maize and soybean and the mixed cropping pattern in Wangkui County exhibited a trend towards evolving into a maize-soybean rotation in the northern region. Moreover, the maize-soybean rotation further evolved into a mixed cropping system of maize and soybean in the north. Furthermore, the spatio-temporal evolution of cropping patterns was significantly driven by natural and social factors. Specifically, natural factors influenced the spatio-temporal patterns of variation in cropping patterns, while social factors contributed to the transformation of farmers' cropping decision-making behavior. Accordingly, new insights, institutional policies, and solid solutions, such as exploring and understanding farmers' behavior regarding crop rotation practices and mitigating the natural and climatic factors for improving food security, are urgent in the black soil region of China. [ABSTRACT FROM AUTHOR]

Considerable attention has been paid to the establishment of an appropriate cropping patterns for the restoration of saline-alkali lands. This study's goal was to explore changes in nitrogen-cycling (N-cycling) gene (nitrogen fixation: nifH; nitrification: AOA, AOB, and nxrB; denitrification: narG, norB, and nosZ) abundance of three cropping patterns at two soil depths in saline-alkali soils. Results showed that rotation and mixture promoted soil nutrients. N-cycling functional genes were significantly influenced by soil depths and cropping patterns. Compared with monoculture, rotation decreased the abundance of nifH, AOA, narG, and nosZ and increased the abundance of AOB; mixture decreased the abundance of AOA, narG, and nosZ and increased the abundance of AOB and nxrB in the 0–15 cm soil depth. Rotation increased all genes abundance; mixture increased nosZ abundance and decreased nxrB abundance in 15–30 cm soil depth. Soil protease, cellulase, nitrate reductase, pH, AK (available potassium), and AP (available phosphorus) were important factors influencing N-cycling gene abundance. In conclusion, rotation and mixture not only reduced soil salinity but also improved soil fertility and nitrogen cycling. These findings can provide some theories for the sustainable development of N-cycling during the restoration of saline-alkali soils. [ABSTRACT FROM AUTHOR]

Global climate change has posed serious threats to agricultural production. Reducing greenhouse gas (GHG) emissions and ensuring food security are considered the greatest challenges in this century. Climate-smart agriculture (CSA) is a concept that can provide a solution to the challenges that agricultural development faces. It can do so in a sustainable way by increasing adaptability, decreasing GHG emissions, and ensuring national food security. So far, little research has systematically reviewed the progresses in CSA in developing and in developed countries. A review on the recent advancements, challenges, and future directions of CSA will be quite timely and valuable. In this paper, the definition and development goals of CSA are identified. Then, the recent advancements of CSA in developing and in developed countries are reviewed. The existing problems and challenges in CSA are analyzed and pointed out. Finally, the proposals on prospects and directions for CSA in the future are proposed. Using advanced internet technology to ensure agricultural information security, improvement of cropping patterns, and management techniques, carrying out "internet + weather" service and improving the quality of agricultural service, and conducting agricultural weather index-based insurance are considered as the main direction of future development of CSA. This review provides new ideas and strategies for strengthening ecological environmental protection, promoting agricultural green development, and mitigating climate change. [ABSTRACT FROM AUTHOR]

Archaeobotanical studies have largely illuminated spatiotemporal differences in agricultural development across the Loess Plateau. However, the particularities of local agricultural development have not been adequately studied for complex geographical, environmental, and prehistoric contexts. Here, new archaeobotanical data and radiocarbon dating results from 27 Neolithic and Bronze Age sites in Baoji are reported. Combining these data with published archaeobotanical datasets, this study explores shifts (and underlying driving factors) in cropping patterns from the late Neolithic to Bronze Age on the southern Loess Plateau (SLP). Regional geographic, environmental, and climatic factors produced mixed millet-rice agricultural systems in the Guanzhong Plain (GZP) and western Henan Province (WHN) and foxtail and broomcorn millet dry-farming systems in the Upper Weihe River (UWR) from 5800–4500 cal BP. Wheat and barley were added to the agricultural systems of the UWR as auxiliary crops after ~4000 cal BP, while cropping patterns remained largely unchanged in GZP and WHN from 4500–3500 cal BP. Cultural exchanges and technological innovations may have influenced the formation of different agricultural patterns across the three regions (i.e., GZP, WHN, and UWR) from 4500–3500 cal BP. From 3500–2200 cal BP, wheat and barley became increasingly important crops on the SLP, although their importance varied spatially, and rice was rarely cultivated. Spatiotemporal variation in cropping patterns was driven by altered survival pressures associated with climate deterioration and population growth from 3500–2200 cal BP. This process was reinforced by internal social developments, as well as interactions with close northern neighbors, in the Shang-Zhou period. [ABSTRACT FROM AUTHOR]

Introduction: Phytoremediation is an effective strategy for saline land restoration. In the Western Songnen Plain, northeast China, soil fungal community recovery for saline phytoremediation has not been well documented among different cropping patterns. In this study, we tested how rotation, mixture, and monoculture cropping patterns impact fungal communities in saline-alkali soils to assess the variability between cropping patterns. Methods: The fungal communities of the soils of the different cropping types were determined using Illumina Miseq sequencing. Results: Mixture and rotation promoted an increase in operational taxonomic unit (OTU) richness, and OTU richness in the mixture system decreased with increasing soil depth. A principal coordinate analysis (PCoA) showed that cropping patterns and soil depths influenced the structure of fungal communities, which may be due to the impact of soil chemistry. This was reflected by soil total nitrogen (TN) and electrical conductivity (EC) being the key factors driving OTU richness, while soil available potassium (AK) and total phosphorus (TP) were significantly correlated with the relative abundance of fungal dominant genus. The relative abundance of Leptosphaerulina, Alternaria, Myrothecium, Gibberella, and Tetracladium varied significantly between cropping patterns, and Leptosphaerulina was significantly associated with soil chemistry. Soil depth caused significant differences in the relative abundance of Fusarium in rotation and mixture soils, with Fusarium more commonly active at 0–15 cm deep soil. Null-model analysis revealed that the fungal community assembly of the mixture soils in 0–15 cm deep soil was dominated by deterministic processes, unlike the other two cropping patterns. Furthermore, fungal symbiotic networks were more complex in rotation and mixture than in monoculture soils, reflected in more nodes, more module hubs, and connectors. The fungal networks in rotation and mixture soils were more stable than in monoculture soils, and mixture networks were obviously more connected than rotations. FUNGuild showed that the relative proportion of saprotroph in rotation and mixture was significantly higher than that in monocultures. The highest proportion of pathotroph and symbiotroph was exhibited in rotation and mixture soils, respectively. Discussion: Overall, mixture is superior to crop rotation and monocultures in restoring fungal communities of the saline-alkali soils of the Western Songnen Plain, northeast China. [ABSTRACT FROM AUTHOR]

In the mitigation strategies of climate change, improving soil carbon storage is considered as one of the main tasks to enhance agricultural sustainability and sequester atmospheric carbon dioxide. Agricultural patterns have been changing significantly and causing many impacts on soil organic carbon (SOC) storage in the upper Vietnamese Mekong Delta (VMD). Therefore, this study aims to evaluate how typical agricultural pattern changes are affecting SOC by estimating the SOC stock and identifying the correlation with soil physic-chemical properties. Soil samples were collected in both depths of 0-20 cm and 20-50 cm and analyzed physic-chemical properties such as soil texture, bulk density, pHKCl, EC and SOC. In topsoil layers, the SOC stock of the wetland forest was the highest in the opened depression of floodplain (24.42±1.38 kg C m

Oilseed flax is one of the most important oil crops in China. With the improvement of people's living standards and the deepening knowledge of the nutritional value of oilseed flax, the demand and economic value of oilseed flax are increasing, and the cultivated area in China is expanding. However, the grain yield of oilseed flax is lower than other oil crops. It varies significantly from year to year, combined with a lower degree of mechanization, which has greatly limited the healthy development of the flax industry. Some of the effects of agronomic measures on productivity and water use efficiency of oilseed flax are reviewed in this paper. The major agronomic strategies for the productivity of oilseed flax were presented based on fertilization, plant density, irrigation, cropping pattern and weed control. Future research should investigate the effect of silicon and potassium fertilizers on the mechanism of lodging resistance of oilseed flax, the effects of diversified cropping systems (strip intercropping and crop rotation) on high and stable productivity and efficient utilization of resources.

Conventional and scientific cropping patterns are important in realizing the sustainable utilization of Black soil and promoting the high-quality development of agriculture. It also has far-reaching significance for protecting Black soil and constructing the crop rotation system to identify the cropping patterns in Northeast China and analyze their spatio-temporal dynamic change. Using the geo-information Tupu methods and transfer land matrix, this study identified the cropping patterns and their spatio-temporal change based on remote sensing data for three periods, namely 2002–2005, 2010–2013, and 2018–2021. The main results revealed that the maize continuous, mixed cropping, maize-soybean rotation, and soybean continuous cropping patterns were the main cropping patterns in Wangkui County, with the total area of the four patterns accounting for 95.28%, 94.66%, and 81.69%, respectively, in the three periods. Against the backdrop of global climate warming, the cropping patterns of continuous maize and soybean and the mixed cropping pattern in Wangkui County exhibited a trend towards evolving into a maize-soybean rotation in the northern region. Moreover, the maize-soybean rotation further evolved into a mixed cropping system of maize and soybean in the north. Furthermore, the spatio-temporal evolution of cropping patterns was significantly driven by natural and social factors. Specifically, natural factors influenced the spatio-temporal patterns of variation in cropping patterns, while social factors contributed to the transformation of farmers’ cropping decision-making behavior. Accordingly, new insights, institutional policies, and solid solutions, such as exploring and understanding farmers’ behavior regarding crop rotation practices and mitigating the natural and climatic factors for improving food security, are urgent in the black soil region of China.

A reasonable planting crop pattern can effectively contribute to maintaining soil fertility and ensuring stable crop growth in the black soil regions of China. This study aimed to analyze the evolution of cropping patterns in Wangkui County between 2002 and 2021. By using ArcGIS and ENVI software, during which visual interpretation was applied, we interpreted three crop rotation cycles based on the classification data of remote-sensing inversion crops for 2002–2005, 2010–2013, and 2018–2021. Our results indicated that maize, rice, and soybean were the three major crops which accounted for a total of 87.02% of the farmland during our study period, while the cropping pattern was dominated by continuous maize cultivation (31.25%), mixed cultivation (29.01%), and continuous maize–soybean cultivation (24.48%). Specifically, the continuous maize cultivation was mainly distributed in the south-central and west-central parts, whereas continuous rice cultivation spread from the northwest to the southwest. In addition, the rice–soybean rotation tended to gather in the southwest and north and spread to the central part, and the rice–maize rotation presented a tendency of radiation diffusion from the county’s northwest region to the surrounding areas. This study could provide a practical basis for establishing high-yield and efficient planting models in the black soil areas of China.

Rice is the most important crop in Taiwan. Monitoring rice-growing areas is thus essential for crop management and food decision-making processes. This research aims to develop an approach for seasonally mapping rice areas from time-series Sentinel-1 data in Taiwan. The data were processed for 2019 and 2020 rice cropping seasons, following three main steps: (1) data pre-processing to construct smooth time-series satellite data, (2) rice area estimation using random forests (RF), and (3) accuracy assessment. The mapping results compared with the government's reference data showed overall accuracy and kappa coefficient higher than 87.7% and 0.76, respectively. The rice area estimates at the county level well agreed with the official statistics, with the root mean square error (RMSE) in percentage smaller than 19.7%. An examination of changes in cropping areas between 2019 and 2020 showed a noticeable reduction of rice areas in 2020, mainly attributed to severe drought conditions. [ABSTRACT FROM AUTHOR]

Climate change is a growing threat to the agricultural sector. Land surface temperature (LST), as an essential variable in the earth's climate system, describes processes such as the exchange of energy and water between the earth's surface and the atmosphere and affects the speed and time of vegetation growth. This study uses satellite images to evaluate the changing trends of climate variables and LST and their effects on cropping patterns in Hafizabad, Punjab. Two leading rice and wheat crops were analyzed from Landsat time series data for 1990, 2000, 2010, and 2018. Through spatial analysis and Iterative Self-Organizing (ISO) data analysis, Landsat satellite images were used for generating LST from 1990 to 2018. After image processing, crops were identified using ISO-data clustering, and crop change trends were calculated at time extension intervals of every 3 years (1989–2019). Regression and correlation analysis were used to assess the effect of LST on crop growth. The image classification reflects an increase in the built-up area from 147.65 to 312.83 km2 and a decline in green space from 187.12 to 21.70 km2. The mean annual LST values were 19.98°C, 24.55°C, 26.22°C, and 31.68°C for the years 1990, 2000, 2010, and 2018. The experimental results show that our technical solution of remote sensing and statistical analysis effectively monitors the impact of climate change on cropping patterns. The study results were beneficial, especially at the decision-making level for local government and for understanding the global scenario for regional planning. [ABSTRACT FROM AUTHOR]

Cropping patterns are defined as the sequence and spatial arrangement of annual crops on a piece of land. Knowledge of cropping patterns is crucial for crop production and land-use intensity. While cropping patterns are related to crop production and land use intensity, they are rarely reported in agricultural statistics, especially those relating to small farms in developing countries. Remote sensing has enabled mapping cropping patterns by monitoring crops' spatial and temporal dynamics. In this paper, we reviewed remote sensing studies of single, sequential and intercropping patterns of annual crops practiced at local and regional scales. A total of 90 studies were selected from 753 publications based on their cropping pattern types and relevance to the scope of this review. The review found that despite the increase in single cropping pattern studies due to the Sentinel missions, studies on intercropping patterns are rare, suggesting that mapping intercropping is still challenging. More so, microwave remote sensing for mapping intercropping has not been fully explored. Given the complexities in mapping intercropping, our review highlights how less frequently used vegetation indices (VIs) that benefit from red-edge and SWIR spectral bands may improve intercropping mapping. [ABSTRACT FROM AUTHOR]

Population growth and increasing food demand coupled with frequent droughts and famine have brought Ethiopia's agricultural growth to a crossroads. The Abbay River basin has untapped potential for irrigation and hydropower development. Numerous models and studies have suggested various irrigation schemes but have failed to precisely identify the irrigation potential or the cropping patterns and water withdrawal requirements of the basin. Investing in irrigation could provide a solution to meet the growing demands for food and development in Ethiopia. Using Mike Hydro model, our research goal was to map and assess the potential irrigation areas and water requirements for the entire Abbay River basin. Ethiopia's total potential irrigation area is estimated to be 7.5 million hectares (ha), and our study estimated that the potential irrigation area in the Abbay River basin is 738 183 ha, which is higher than that of the Abbay Master Plan estimation of 526,000 ha. The average irrigation efficiency in the basin was calculated to be 50%, and the cropping pattern ranged from 1 to 7 crops per year, and we found that maize, cotton, and sugarcane covered the highest proportions of the irrigation areas (22%, 16%, and 13%, respectively). We categorized the regions within the basin into six irrigation schemes and found that nearly all irrigation schemes required more water between December and March. The Megech, Beles, Abbay at Kessie, and Tis Abbay regions required notably more irrigation water than the others, and we estimated the total Abbay basin’s annual irrigation water requirement is 9 Billion Cubic Meters (BCM). Accordingly, our research suggests that the annual irrigation water requirement will increase significantly in the future. Thus, given the anticipated seasonal and interannual water flow variability due to climate change, our study suggests that more storage dams should be constructed to control water variability and satisfy irrigation water requirements in the basin.

This paper proposes scenarios to achieve more crop per drop and irrigation for all in water-scarce irrigation systems, with a particular reference to India. It uses economic water productivity (EWP) and water cost curve for EWP as tools to reallocate irrigation consumptive water use (CWU) and identify economically viable cropping patterns. Assessed in the water-scarce Sina irrigation system in Maharashtra, India, the method shows that drought-tolerant annual crops such as fruits and/or fodder should be the preferred option in irrigated cropping patterns. Cropping patterns with orchard or fodder as permanent fixtures will provide sustainable income in low rainfall years. Orchards in combination with other crops will increase EWP and value of output in moderate to good rainfall years. Governments should create an enabling environment for conjunctive water use and allocation of CWU to achieve a gradual shift to high-value annual/perennial crops as permanent fixtures in cropping patterns. Highlights Economic water productivity is a critical performance indicator when water has an opportunity cost.; Water cost curve assesses the financial trade-off of different cropping patterns.; Adapting to weather variability is imperative for water-scarce irrigation systems.; Drought-tolerant annual crops should be a permanent feature in cropping patterns.; Enabling a policy environment for conjunctive water use is necessary for change.;

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Archaeobotanical studies have largely illuminated spatiotemporal differences in agricultural development across the Loess Plateau. However, the particularities of local agricultural development have not been adequately studied for complex geographical, environmental, and prehistoric contexts. Here, new archaeobotanical data and radiocarbon dating results from 27 Neolithic and Bronze Age sites in Baoji are reported. Combining these data with published archaeobotanical datasets, this study explores shifts (and underlying driving factors) in cropping patterns from the late Neolithic to Bronze Age on the southern Loess Plateau (SLP). Regional geographic, environmental, and climatic factors produced mixed millet-rice agricultural systems in the Guanzhong Plain (GZP) and western Henan Province (WHN) and foxtail and broomcorn millet dry-farming systems in the Upper Weihe River (UWR) from 5800–4500 cal BP. Wheat and barley were added to the agricultural systems of the UWR as auxiliary crops after ~4000 cal BP, while cropping patterns remained largely unchanged in GZP and WHN from 4500–3500 cal BP. Cultural exchanges and technological innovations may have influenced the formation of different agricultural patterns across the three regions (i.e., GZP, WHN, and UWR) from 4500–3500 cal BP. From 3500–2200 cal BP, wheat and barley became increasingly important crops on the SLP, although their importance varied spatially, and rice was rarely cultivated. Spatiotemporal variation in cropping patterns was driven by altered survival pressures associated with climate deterioration and population growth from 3500–2200 cal BP. This process was reinforced by internal social developments, as well as interactions with close northern neighbors, in the Shang-Zhou period.

Descriptive Statistics and Net Farm Income model was used to analyze data collected from 120 Arable Farmers who adopted various cropping patterns in Niger State, Nigeria. The study specifically examined the socio-economic characteristics of arable farmers, profile the cropping patterns adopted, examined the profitability and highlighted the constraints to crop production among arable farmers in the study area. Results obtained from the study show that crop farming in the area is a male dominated. The mean age of farmers was 33years, 98.3% were married, 80.8% had one form of education or the other and 68.4% adopted a three-crop mix pattern in their crop production. Two and three crop mixes enterprise were profitable than sole cropping when gross income per ha was used as an index of profitability. Profitability was higher in single crop enterprise when returns/man day was used as an index but was higher in a two and three crop mix enterprise when net returns per ha was used as a measure of profitability. Bad roads, drought, theft of farm produce, poor extension per farm advisory services and lack of credit facilities respectively were the constraint to crop production. The study concludes that mixed cropping enterprises was more profitable than sole cropping. We recommend the promotion of mixed cropping among arable farmers for increased profitability and income to farm households, that the constraints identified be addressed by all concerned authorities so as to sustain crop production, reduce food insecurity and eradicate hunger and poverty among arable farmers in the area and Nigeria as a whole.

Increasing food demands are causing rapid transitions in farming systems, often involving intensified land and resource use. While transitioning has benefits regarding poverty alleviation and food outputs, it also causes environmental and social issues over time. This study aims to understand the transitions in farming systems in a region in Telangana, from 1997 to 2015, and their effect on livestock rearing and smallholder livelihoods. We also examine the impact of the transitions on lower caste groups and women in particular. We collected data using a combination of methods, i.e., a household survey, focus group discussions, and secondary data sources, to build a comprehensive picture of the transitions in the region. We found that subsistence mixed farming systems transitioned to market-orientated specialized systems over a short time span. As the transition process gained momentum, households either intensified their production or got marginalized. Technological interventions, development programs with integrated approaches, and market demand for certain agricultural produce triggered increased regional production but also led to the scarcity of water, land, and labor. The transitions marginalized some of the households, changed the role of livestock in farming, and have been inclusive of both lower caste groups and women in terms of increased ownership of large ruminants and access to technologies. However, for women specifically, further increase in workload in the context of farming is also found. [ABSTRACT FROM AUTHOR]

Long-term historical information on national-scale grain production is critical for ensuring food security but often limited by the lack of geospatial data. This study aims to conduct the first systematic investigation of grain Cropping Patterns (CP) in China over the past two decades, shedding light on the roles of grain expansion and intensification in sustainable agriculture. This study proposes a framework to fully characterize grain production patterns considering crop types, cropping intensity and patterns based on spatiotemporal continuous ChinaCP datasets (2005–2020). Four indicators were developed for measuring the Reality to Capability Ratio (RCR) of grain production regarding the total yield and sow area, the cropland extent and cropping intensity. The capability of grain production was derived based on grain cultivation history. There was a huge gap between the reality and capability of grain production in China, which varied with grain crop types and cropping patterns. At national level, a vast majority (96%) of cropland was capable of grain production, and two fifths of cropland quantified for double grain cropping. However, only 46.65% and 24.89% of the capability was implemented for grain or double-grain cropping in 2020. Maize, rice, and wheat was ever cultivated in 76.88%, 57.05%, and 25.18% of national cropland, respectively. Winter wheat plays an important role in stabilizing grain production by double grain cropping, accounting for 7/8 continuously grain-cultivated areas. However, the RCR of double rice was only 7% in 2020. Bridging these gaps could potentially triple grain production, however, achieving this increase poses challenges due to a series of constraints related to cropland fraction, topographic conditions and lack of agricultural labors along with rapid urbanization. This study found that there was a continuous Northeastward movement & countryside shift in grain production. Continuous support for long-term active agricultural systems is crucial to ensure sustainable grain production in China, with a special emphasis on key grain productive regions, considering targeted cropping patterns and regional disparities. This study enhances our understanding of grain production systems in China based on long-term cultivation histories. Findings can inform the development of more geographic-targeted policies concerning grain cropping intensifications to ensure food security and environmental sustainability in developing countries. The long term spatiotemporal continuous CPChina datasets during 2005-2020 was are publicly accessed at: https://doi.org/10.6084/m9.figshare.25106948. [Display omitted] • Maize, rice, and wheat was ever cultivated in 77%, 57%, and 25% of national cropland, respectively. • Only 46.65% of grain and 1/4 double-grain quantified cropland was implemented in 2020. • Winter wheat dominated the continuously grain cultivated areas (around 7% cropland) during 2005–2020. • Bridging cultivation gaps could potentially triple grain production, but it is hard to achieve this goal. • Grain production showed continuous reductions in peri-urbans versus expansions in remote regions. [ABSTRACT FROM AUTHOR]

Efficiently optimizing cropping patterns is crucial for the sustainable agriculture development, especially in arid regions like Saudi Arabia. This study specifically aimed to gradually optimize cropping patterns in Saudi Arabia over next decade. The cropping pattern of 2023 according to ministry of environment water and agriculture was assessed to identify crops with potential for optimization based on economic revenue, water consumption. Subsequently, three optimization strategies were devised: maximizing total income, minimizing total water consumption, and maximizing productivity. The strategy focused on maximizing income projected a remarkable 17.6/% increase in total agricultural income by simply increasing the current water resources by 4.2%. On the other hand, the strategy aimed at minimizing water consumption could save 13.4% of water resources, but it would result in 4.6% reduction in total income. Alternatively, the strategy prioritizing productivity maximization would lead to a significant 15.6% increase in total income while reducing water resources by 0.7%. [ABSTRACT FROM AUTHOR]

Cropping patterns are defined as the sequence and spatial arrangement of annual crops on a piece of land. Knowledge of cropping patterns is crucial for crop production and land-use intensity. While cropping patterns are related to crop production and land use intensity, they are rarely reported in agricultural statistics, especially those relating to small farms in developing countries. Remote sensing has enabled mapping cropping patterns by monitoring crops’ spatial and temporal dynamics. In this paper, we reviewed remote sensing studies of single, sequential and intercropping patterns of annual crops practiced at local and regional scales. A total of 90 studies were selected from 753 publications based on their cropping pattern types and relevance to the scope of this review. The review found that despite the increase in single cropping pattern studies due to the Sentinel missions, studies on intercropping patterns are rare, suggesting that mapping intercropping is still challenging. More so, microwave remote sensing for mapping intercropping has not been fully explored. Given the complexities in mapping intercropping, our review highlights how less frequently used vegetation indices (VIs) that benefit from red-edge and SWIR spectral bands may improve intercropping mapping.

The Gansu–Qinghai region lies in the key position for trans-Eurasian cultural exchange, and hence investigations of the history of agricultural development in this region are significant for understanding the spatiotemporal evolution of prehistoric crop dispersal in Eurasia. However, systematic archaeobotanical studies concerning the history of the development of prehistoric agriculture in this area are scarce. Here, based on archaeobotanical analysis and radiocarbon dating at the Jinchankou site, we investigated the history of agricultural development in the Datong River valley during the Qijia culture. Combined with previous archaeobotanical studies of the Gansu–Qinghai region, we explored the diachronic changes in the cropping patterns from the Late Neolithic to the Early Iron Age. The results suggest that millet remained the most important subsistence plant during 4100–3700 BP, while barley and wheat were first cultivated around 3900 BP at the Jinchankou site. Humans only cultivated foxtail and broomcorn millet in the Gansu–Qinghai region with a high level of agricultural management during 5900–4000 BP. Barley and wheat were added to the agricultural system in the area during 4000–3600 BP, although they played a subsidiary role compared with millet. During 3600–2100 BP, barley played an increasingly important role in the Gansu–Qinghai region but with evident differences among geomorphic units, and there was an obvious decrease in agricultural management level. It is likely that the transformation of cropping patterns and agricultural management levels in the Gansu–Qinghai region from 5900 to 2100 BP was primarily promoted by prehistoric trans-continental cultural exchange and secondly by climate change in the area. [ABSTRACT FROM AUTHOR]

Various cropping patterns are extensively used on the North China Plain, which greatly alter various soil properties. Although these soil properties respond differently to the different cropping patterns, little is known about the possible effects of cropping patterns on desalinized soils. To assess the effects of the different cropping patterns on soil properties and enzyme activities, a long-term field experiment was conducted at the Nanpi Agro-Ecological Station in Hebei Province, China. The cropping patterns under study were the single-cropping patterns of winter wheat (Triticum aestivum L.), spring maize (Zea mays), and spring cotton (Gossypium hirsutum), and the double-cropping pattern of winter wheat–summer maize (WW–SM), which is the predominant cropping pattern on the North China Plain. Soil samples were collected at a depth of 0–100 cm, depending on the soil properties. Our results indicated that soil available phosphorous (P) and available potassium (K) concentrations at 0–20 cm were greater than those of the subsurfaces. Soil organic matter declined with depth, and WW–SM had the highest amount in the deep levels. The WW–SM cropping pattern also generally showed greater levels of enzyme activities than did the single-cropping patterns, proving that diverse crops can enhance enzyme activities. Soil pH generally increased with depth despite there being no significant differences between the cropping patterns. No significant effects were observed on soil electrical conductivity (EC). However, EC generally increased with depth in spring cotton and winter wheat and declined in spring maize and WW–SM. Winter wheat had a significant aggregate size (

Cotton (Gossypium hirsutum L.) as an important fiber crop is considered the back bone of Pakistan's economy. Climatic variations causing shift in cropping systems in Pakistan and worldwide. So, it is high time to readjust sowing times. Nutrient deficiency in cotton crops along with delayed sowing has made this situation worse. For this purpose, a two-year field study was carried out at the Central Cotton Research Institute (CCRI) Multan, Punjab, Pakistan during 2016 and 2017. The experiment was laid out in Randomized Complete Block Design (RCBD) with split plot arrangements with three replications. Experimental treatments comprised of five sowing dates viz. S1 = 15th April, S2 = 01st May, S3 = 15th May, S4 = 01st June and S5 = 15th June and three cultivars viz. G1 = CIM-620, G2 = Cyto-120 and G3 = CIM-608. Sowing time significantly influenced the performance of different cotton cultivars. Each delay in sowing dates significantly reduced the seed cotton yield as the lowest yield (1387 kg ha-1) and (1264 kg ha-1) was produced with late sown cotton (June 15th) while the highest seed cotton yield (2759 kg ha-1 and 2569 kg ha-1) was produced when crop was sown early on 15th April during both study years 2016 and 2017, respectively. Moreover, cultivars also significantly differ under different sowing dates. Cultivar (CIM-620) produced significantly more seed cotton yield (2456 kg ha-1 and 2257 kg ha-1) than (CIM-608) that produced 1971 kg ha-1 and 1773 kg ha-1 of seed cotton yield during 2016 and 2017 respectively. In conclusion; the cultivar (CIM-620) results in increased seed cotton yield (24.61 and 27.30%) and lint percentage with more fiber brightness. Similarly, the performance of crop sown earlier on 15th May produced higher number of fruiting points, more intact fruits and higher number of bolls. It is directed to the cotton growers not to adopt late sowing with any cultivar particularly CIM-620 for which 15th May is a fairly good suggestion to improve its seed yield with optimum net returns. [ABSTRACT FROM AUTHOR]

The present study analyses the historical development of irrigated agriculture in the command area of the Malaprabha irrigation project located in a semi-arid region of Karnataka, India. Considering the historical period from 1965 to 2014, spatio-temporal changes in irrigated area, cropping patterns, crop and irrigation water requirements using CROPWAT model and sources of irrigation water were characterized relative to the pre-project period at decadal time-step. Overall results indicate an increase in irrigated area, departure from the planned cropping pattern and a shift towards cultivation of water-intensive cash crops, canal releases not conforming to estimated irrigation water requirements and an increase in groundwaterbased irrigation in the command area of the project. The findings of this study can be used to formulate more effective management strategies to improve the efficiency of the project and promote sustainable development of natural resources. [ABSTRACT FROM AUTHOR]

The preparation, formulation and implementation of crop patterns are important activities in the agriculture and food production organizations. According to the significance role of cropping pattern in sustainable production of agricultural and horticultural crops, determining the optimum cropping pattern in irrigation network is necessary. This research objective is maximizing the net profit of farmers using genetic algorithms to determine optimal crop pattern in Golestan dam irrigation and drainage network. This irrigation network has been exploited separately by 4 rural production cooperatives company. For this reason the cropping pattern has been set for each cooperative separately in fall and summer cultures. In fall planting, because of good rainfall and sufficient water, all land area of each cooperative would be cultivated; in this case around 38% remaining excess water would be stored in dam reservoirs. But in the summer planting because of increasing evaporation and decreasing rainfall, irrigation water requirement would be increased, for this reason; the model is taken in an average 34% of the each cooperative area under crops. By using the surplus water of fall planting in summer planting, it can be another 1388 hectares (around 13 %) of land to be planted under irrigated crops and as a result the profit increased up to 37 percent.

Utilizing available water resources efficiently is crucial to address both our present and future requirements and plays a vital role in safeguarding food security. This current investigation deals with assessment and optimizing water footprint (WF) and virtual water flow (VWF) for primary crops in Banas River Basin (BRB) using AquaCrop model with local datasets and district-level estimates. VWF in the basins were estimated by multiplying the WF of crops with the amount exported/imported, which is determined based on the difference between production and consumption in the basin. The possibility of changing the cropping patterns was evaluated for the potential reduction of the blue WF. Annual WF from primary crops in the basin amounts to 19,255 MCM/yr (70 % green, 21 % blue and 10 % grey WF, respectively). Banas basin is a net exporter of agriculture commodities with nearly 7391 MCM/yr of water flowing out of the basin due to agricultural exports of which approximately 265 MCM/yr is virtual blue water outflow. Crops having low economic water productivity of blue water are being grown in vast areas resulting in a high blue WF. The optimizing the cropping pattern can result in a 5-42 % lower blue water footprint with 11-39 % higher economic output under different scenarios with and without considering the consumption needs. Changing the cropping pattern and making trade plan to optimize the crop import/exports can be viable option for tackling the blue water scarcity issues in the basin. WF can be managed sustainably by improving water resource allocation for better economic, social, and environmental productivity and going for less aggressive agricultural production., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Author(s).)

It is of paramount importance to have sustainable agriculture since agriculture is the backbone of many nations' economic development. Majority of agricultural professionals rarely capture the cropping patterns necessary to promote Good Agricultural Practises. Objective of this research is to explore the potential of mapping cropping patterns occurring on different field parcels on small-scale farmlands in Zimbabwe. The first study location under investigation are the International Maize and Wheat Improvement Center (CIMMYT) research station and a few neighboring fields, the second is Middle Sabi Estate. Fourier time series modeling was implemented to determine the trends befalling on the two study sites. Results reveal that Sentinel-1 synthetic aperture radar (SAR) time series allow detection of subtle changes that occur to the crops and fields respectively, hence can be utilized to detect cropping patterns on small-scale farmlands. Discrimination of the main crops (maize and soybean) grown at CIMMYT was possible, and crop rotation was synthesized where sowing starts in November. A single cropping of early and late crops was observed, there were no winter crops planted during the investigation period. At Middle Sabi Estate, single cropping on perennial sugarcane fields and triple cropping of fields growing leafy vegetables, tomatoes and onions were observed. Classification of stacked images was used to derive the crop rotation maps representing what is practised at the farming lands. Random forest classification of the multi-temporal image stacks achieved overall accuracies of 99% and 95% on the respective study sites. In conclusion, Sentinel-1 time series can be implemented effectively to map the cropping patterns and crop rotations occurring on small-scale farming land. We recommend the use of Sentinel-1 SAR multi-temporal data to spatially explicitly map cropping patterns of single-, double- and triple-cropping systems on both small-scale and large-scale farming areas to ensure food security. [ABSTRACT FROM AUTHOR]

Late blight caused by Phytophthora infestans is one of the main diseases in potato production. The Netherlands has a high potato density and favourable weather conditions for the disease, and this combination leads to frequent outbreaks of late blight. A spatially explicit agent-based model of the host-pathogen system was used in workshops with conventional and organic farmers to demonstrate and discuss the potential role of resistant varieties for effective and sustainable control of late blight. We presented model-based scenarios and used qualitative and quantitative measures to analyse the effect of the workshop on farmers' perception on late blight control. The scenarios simulated effects of farmer decisions regarding the use of crop resistance and fungicide application on disease control at the landscape level over a period of ten years. The model showed that growing a resistant variety (with a single resistance gene) can reduce disease incidence in the landscape, however, after a couple of years resistance breakdown occurs by emergence of a new virulent P. infestans strain. If no countermeasures were taken the new virulent population could spread fast through the landscape, reducing potato yield of resistant fields. The model showed a number of resistance management strategies that could be effective to increase resistance durability. Differences in farmer perception were observed before and after the workshop as well as between conventional and organic farmers. By analysing the disease dynamics at the landscape level, the model showed the importance of collective action. To prevent emergence and spread of a virulent strain it is important to keep disease pressure low. To achieve this, all farmers have to cooperate. During the workshops farmers exchanged views and negotiated possible solutions. We conclude that the use of model-based scenarios in workshops was very useful to increase farmers' knowledge of the system and served as a good starting point for discussions among actors facing the complex problems of late blight control and potato resistance management. • We analysed farmer perceptions on potato late blight control using model scenarios. • Scenarios were discussed in workshops with conventional and organic farmers. • Results showed impact of crop protection and of crop resistance at landscape level. • Qualitative and quantitative measures were used for analysing farmers' perceptions. • The workshop increased awareness that collective action of farmers is needed. [ABSTRACT FROM AUTHOR]

Following the renewed effort at achieving a new green revolution for Africa, emphasis has been placed on modernizing smallholder agriculture through the deployment of improved inputs especially mechanized technologies. In Ghana, the government has in the last decade emphasized the provision of subsidized mechanized ploughing services to farmers alongside a rapidly growing private sector tractor service market. While mechanized technology adoption rates have increased rapidly, the deployment of these technologies has been without critical analysis of the impacts on production patterns and local agrarian systems. This paper examines the distributional impacts of agriculture mechanization on cropping patterns and farm sizes of smallholder farmers in northern Ghana using Geographic Information Systems (GIS) techniques, and semi-structured interviews with smallholder farmers (n=60). Specifically, comparative analysis of the field sizes and cropping patterns of participant farmers prior to and after the adoption of mechanized technologies was conducted. In-depth interviews were used to contextualize the experiences of smallholder farmers toward understanding how mechanization may be impacting traditional agriculture. Our findings reveal a mechanization paradox in which farm sizes are expanding, while cropping patterns are shifting away from traditional staple crops (pearl millet and sorghum bicolor) to market-oriented crops (maize, rice and groundnuts). This transition we argue, has adverse implications on the cultural dimension of food security, the organization of social life, and climate change adaptation. We recommend a retooling of the current agricultural policy focus to ensure context sensitivity for a more robust battle against food insecurity. [ABSTRACT FROM AUTHOR]

Home gardens in southern Ethiopia are regarded as efficient farming systems, allowing interactions and synergies between crop, tree and livestock components. However, these age-old traditional home gardens are evolving rapidly in response to changes in both the socio-economic and biophysical environment. Altered cropping patterns, farm size and component interactions may affect the systems’ sustainability. Home gardens exhibit a huge diversity in farms and farming systems, which needs to be understood in order to design interventions for improvement. Dynamics of home gardens were studied over two-decades (1991-2013) based on a survey of 240 farm households and focus group discussions. Farms were grouped into five types: Khat-based, Enset-cereal-vegetable, Enset-based, Enset-coffee and Enset-livestock. Farm trajectories revealed a shift from food-oriented Enset-based and Enset-livestock systems to (1) cash crop oriented khat-based systems, and (2) combined food and cash crop oriented Enset-cereal-vegetable systems. In densely populated, market proximate areas a major trend was expansion of khat, from 6 to 35% of the area share per farm, while the combined area share of enset and coffee decreased from 45 to 25%. Concurrently, the cattle herd size fell from 5.8 TLU to 3.9 TLU per household. In medium populated, less accessible areas the trend was consolidation of combined production of food and cash crops. Enset and coffee together maintained a share of over 45%. Easy transport and marketing of the perishable cash-generating khat compared with traditional crops favoured its cultivation among smallholders located close to markets. The insights in home garden change in response to increasing population pressure, decreasing farm size and market development may help to design interventions to increase system sustainability. [ABSTRACT FROM AUTHOR]