Cropping Pattern Research Articles

Impact of shifts in cropping pattern on socioeconomic conditions of farmer

The present study;s impact of shifts in cropping patterns on the socioeconomic conditions of farmers conducted in the Pune district of Maharashtra state. Both primary and secondary data were used for the study. 68.67 per-cent of farmers buy new livestock, 68.00 per-cent of farmers dig new wells/bores, and 62.00 per-cent of farmers improve their existing land. On the home front, 84.00 per-cent started using LPG gas, while 82.00 per-cent of respondents had access to drinking water, As improving living conditions,80.00 per-cent started more spending on food and nutrition for the family,8.67 per-cent of farmers purchased ‘smart T. V.’ and 85.53 per-cent farmers purchase ‘android phones’, as farmers changing living conditions, 70.67 per-cent repaid their old loans,48.67 per-cent had increased savings/deposits and 43.33 per-cent of invested their money in other enterprises. The research based on empirical data of crops and area under them during years 2012 -21 from 15 villages, Data collection and analysis is major challenge faced during research. By overcoming this problem this research helps to understand how socioeconomic characteristics of farmer help him to change its cropping pattern and help him to improve live-hood conditions.8u

A Multi‐Objective Optimization for Determination of Sustainable Crop Pattern Using Game Theory

ABSTRACTAmid growing concerns about environmental sustainability in agriculture, this study explores the potential of a game‐theoretic approach to identify and implement sustainable crop patterns in the Marvdasht–Kherameh sub‐basin, Iran. Employing a cooperative game‐theoretic framework, the study considers farmers, environmental advocates and policymakers as stakeholders with distinct objectives: maximising profit, minimising environmental pollution and optimising water consumption. This research aims to develop and apply a comprehensive model that effectively integrates these diverse objectives, proposing practical, sustainable agricultural strategies for the region. The game‐theoretic model is integrated with data analysis and optimization techniques to identify Pareto‐optimal solutions that balance the competing objectives of stakeholders with vested interest. The advantage of this approach is its ability to produce a robust, spatially visualised solution using GIS‐based mapping, which improves communication and aids in implementing sustainable crop patterns. Using regression models, an equilibrium point, defined as the intersection of the three polynomial regression models representing profit, nitrate fertiliser amount and water consumption objectives is identified. The optimised crop pattern corresponding to this equilibrium point is determined using a single‐objective optimization model. Key findings include a 40% reduction in wheat cultivation and significant increases in barley (143%) and paddy rice (113%), leading to enhanced resource efficiency and sustainability by optimising water and nitrate fertiliser use. The results offer significant benefits, including detailed spatial visualisation and stakeholder‐specific optimization for sustainable crop patterns, which can be adapted to other agricultural regions facing similar challenges.

Major impacts of the US Renewable Fuel Standard on corn and soybean cultivation

AbstractWe use rich spatial data on observed crop choices and ethanol production to estimate the local impacts of ethanol plants and the overall impact of the 2007 US Renewable Fuel Standard (the RFS2) on crop patterns. We find that the RFS2 increased total corn area by 9.7%, corn monoculture by 16.8%, and soybean area by 6.7%, with notable spatial heterogeneity in both the overall impact and the local effects of ethanol plants. The results indicate an RFS2‐induced shift in the geography of corn and soybean cultivation within the Corn Belt—intensification in the core, and significant expansion near the periphery.

Enhancing Efficiency of the Minipe Left Bank Canal Irrigation System: A Strategic Approach

Minipe left bank canal faces a head-tail water imbalance due to insufficient water availability and inefficiency in water distribution. To address this issue, Minipe anicut is raised by 3.5m. Though ample water could be released, irrigation water would not be dispersed adequately along the canal due to mis-usage and mismanagement. The study aims to establish an operational pattern for the canal to deliver adequate water to the entire command area during both the Yala and Maha seasons. Simulation models were used to model the canal's operational pattern, including inflows and irrigation demands. Catchment areas, which bring water to the Minipe Left Bank canal, were delineated using ArcGIS software, and inflows were estimated using HEC-HMS software. Crop water requirements were estimated using CROPWAT program. A system simulation model developed usingWEAP software was used to simulate irrigation system operation. Nine management scenarios were evaluated, including introducing new cropping patterns, shifting sowing dates, increasing Minipe anicut discharges, and reducing the command area. The study found that introducing a 70% paddy crop pattern, advancing the sowing date by two weeks, and raising the Minipe anicut discharge by 15% significantly reduce unmet demands. Implementing these improvements could result in a 75% reduction in unmet demand.

Optimizing cropping patterns and resource allocation for sustainable agricultural development: A case study of Ilam province, Iran

Establishing connections between ecosystems and agricultural mechanization can help address erroneous decision-making in agricultural management and prevent further harm to ecosystems. It is essential for authorities and decision-makers in the agricultural sector to understand ecosystem dynamics and influencing factors to develop appropriate policies for sustainable development. This study focuses on identifying optimal conditions for determining crop patterns and input allocation in Ilam province's agricultural practices, considering technical and energy parameters. Cultivating one hectare of wheat and barley in the region requires an average energy consumption of 1.79 GJ from various sources, with an estimated energy output of 38.04 GJ ha−1 for these crops. The research involved farmers cultivating wheat, barley, fodder maize, and canola in the southern region of Ilam province, covering cities like Abdanan, Dareshahr, and Dehloran. The study suggests adjustments to cropping patterns, including reducing wheat cultivation and increasing irrigated fodder maize cultivation under optimal conditions. It also recommends exploring rainfed cultivation or enhancing the yield of rainfed wheat to offset the reduced wheat cultivation. The goal planning model indicates a shift towards sustainability, with changes in cropping patterns focusing on wheat, barley, fodder maize, and canola crops, including expanding the cultivation areas of irrigated wheat, barley, and fodder maize. Efforts aim to enhance the productivity of irrigated crops to promote sustainability and improve agricultural practices in the province for long-term sustainability goals.

A Monte Carlo Model for WWTP Effluent Flow Treatment through Enhanced Willow Evapotranspiration

The effectiveness of using enhanced evapotranspiration rates of willow plantation is a modern environmentally friendly practice for advanced treatment of effluent WWTP flow. The key idea is that through advanced willow evapotranspiration rates, a significant proportion of the effluent flow can be transferred into the atmosphere through the physical process of evapotranspiration. This study further discusses the concept in a real-world problem using a wide dataset consisting of a recent PET monthly remote dataset namely RASPOTION, monthly recorded rainfall gauge, and experimental willow evapotranspiration surveys across Ireland, to identify the monthly cropping pattern. A Monte Carlo water balance model has been developed for the period 2003–2016. The model was applied in an existing willow plantation at Donard WWTP co. Wicklow, Ireland to identify the exceedance probability of willow plantation runoff against estimated low flows (i.e., Q95, Q99) at the adjacent small tributary. In this case study, any failure which can lead to river quality deterioration was not assessed. The overall framework aims to provide new insights considering the multiple sources of uncertainty (i.e., monthly willow cropping pattern and WWTP effluent flow) in associated environmental engineering problems.

Improved method for cropland extraction of seasonal crops from multi-sensor satellite data

ABSTRACT Monitoring agricultural land over vast geographical areas presents challenges due to the absence of accurate, comprehensive and precise data, which has become a complex process that is difficult to do in terms of both timespans and consistency. Hence, this study presents an improved approach for the identification of agricultural land by utilizing the capabilities of Sentinel-1 and Sentinel-2 satellites with a variety of vegetation and non-vegetation indices and machine learning algorithms. The Multispectral Correlation Mapper (MCM) and Random Forest (RF) algorithms are adopted to train different agricultural lands, crop types and sowing and cultivation seasons. The 45-bands mega-file data cube (MFDC) fusion for each season incorporates essential indices and features derived from the Sentinel-1 and Sentinel-2 datasets for both seasons, i.e. Rabi (winter-spring season) and Kharif (summer-autumn season). The proposed method demonstrated resilience when applied to satellite datasets while effectively reducing the impact of non-agricultural elements such as shrubs, grass, bare soil and orchards. The results demonstrate a notable ability to differentiate between the Rabi and Kharif seasons, resulting in a high level of precision in measuring the extent of cultivated land during the Rabi and Kharif seasons with an area of 626,947 acres and 590,858 acres, respectively. The total land area, ascertained from the observation of the comprehensive cropping pattern and agricultural modifications during the entire year (June 2021–May 2022) is 635,655 acres. The validation exercise shows the higher accuracy of this method for cropland, with an overall accuracy of 98.8%, kappa of 0.97, user accuracy of 98.69% and producer accuracy of 99.13%. Additionally, it was spatially compared with the ESRI, ESA and MODIS cropland layers and government statistical data. Furthermore, the research investigates the temporal dynamics of agricultural growth phases using spectral bands and indices. This approach improves the accuracy of precise cropland identification and provides useful insights into crop phenology.

Spatiotemporal Dynamics and Evolution of Grain Cropping Patterns in Northeast China: Insights from Remote Sensing and Spatial Overlay Analysis

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.

Utilising GIS-based spatial land resource analysis for subsidised fertiliser allocation in Jombang Regency, Indonesia

A subsidised fertiliser policy is a clear option to increase rice production and maintain farmer benefits. It has proven to be costly and hence needs to be effectively calculated. This research proposes the use of advanced multi- disciplinary techniques to precisely estimate annual fertiliser demands for food production, based on the local field conditions and crop characteristics. Time-series satellite imageries (Landsat 8 Surface Reflectance of Operational Land Imager) were analysed to identify the annual cropping patterns, delineate agricultural field boundaries, and estimate land area. Monitoring and data collection on cropping patterns were conducted using normalised difference vegetation index (NDVI) data. The corrected rice field maps and cropping patterns were validated through field inspections. Subsequently, soil sampling and analysis were performed to determine precise fertiliser doses for each crop during each planting season. Finally, a fertiliser demand allocation map was created to inspect the results visually. A case study conducted in Jombang Regency, East Java Province, Indonesia, highlighted a gap of approximately 5.91 Mg urea and 1.02 Mg NPK in fertiliser demand measured using this study method as compared to the current subsidised fertiliser allocation. This gap could lead to an ineffective use of the fertiliser subsidy budget, which could jeopardise achieving the national food production target. Therefore, this study urges government stakeholders to implement the proposed method to maintain national food security in the country.

Optimizing water-energy-food nexus index, CO2 emissions, and chemical pollutants under irrigation water salinity scenarios

The escalation of crises related to water quantity of quality, energy, food, and the environment has posed numerous challenges to sustainable development in the agricultural sector. This study seeks to establish an optimal and sustainable framework within the agricultural sector by meeting the objectives of the WEF (Water-Energy-Food) nexus index and environmental. For this, we individually optimized the objectives: maximizing the WEFN index (O1), minimizing CO2 emissions (O2), reducing chemical fertilizer consumption (O3), minimizing chemical pesticide usage (O4), and maximizing gross margin (O5) under various electrical conductivity (EC) scenarios (optimistic, moderate, and pessimistic) within the irrigation network of the Jiroft plain in Iran. In the second stage, we employed a Multi-Objective Programming (MOP) Model to attain these objectives using a weighted sum approach simultaneously. The results of the first stage revealed that under moderate and pessimistic EC irrigation water scenarios, significant alterations in the crop selection within the optimized models for O1 and O5 objectives occurred compared to the baseline scenario, which did not consider EC irrigation water. Specifically, in the pessimistic scenario with an EC irrigation water level of 2.25 ds/m, crops such as onions, tomatoes, and potatoes were replaced within the optimal cropping pattern by wheat, barley, and green-maize, leading to a notable decline in program efficiency. The results from the MOP optimization model indicated that in the pessimistic EC irrigation water scenario, the WEFN index, CO2 emissions, fertilizer consumption, pesticide consumption, and gross margin would change by approximately +12%, −48%, −28%, −8%, and −74%, respectively, compared to the baseline scenario. Consequently, an increase in EC irrigation water, despite improving environmental conditions, would substantially diminish economic profit. Thus, overlooking the impact of EC irrigation water in the WEF nexus analysis could result in misleading conclusions.

Technology Development with Zero Tillage and Stubble Residue Management for Sustainable Soil Health and System Productivity in Wheat-maize Cropping Pattern

Conventional farm operations comprising traditional soil tillage and burning stubble residues become the cause of concern for soil sickness threatening sustainable system productivity. Wheat and maize constituting one of the predominant cropping systems strive with the commitment of substantial contribution to the world food security front. A better understanding of these alarming issues insisted on us to explore ‘Conservation Agriculture’ practices designing a field experiment with tillage and crop residue management. The study from 2019 to 2022 derived phenomenal achievement with Zero Tillage (ZT) and Minimal Tillage (MT) in compliance with crop residue retention (+ R); while, the eventuality of stubble burning (- R) deteriorated soil health, especially with Conventional Tillage (CT). Results illustrated significantly higher system productivity (11.60 - 12.0 t ha-1) in terms of wheat equivalent yield at (ZT+R) stands followed by those (11.34-11.64 t ha-1) at (MT + R), significantly higher than those (10.52 - 10.59 t ha-1) at conventional (CT-R) stands and those (10.63-10.78 t ha-1) at (CT+R) stands. Soil health also significantly improved at (ZT + R) stands accounting for higher soil porosity (39.45%), pH (7.64), electrical conductivity (0.370 dS m-1), hydraulic conductivity (10.56 mm h-1), soil organic carbon (0.458%), and N, P and K contents (272.5, 18.36 and 254.8 kg ha-1) than at conventional (CT -R) stands. Therefore, the study could develop a viable cutting-edge agro-technology fostering sustainable maize-wheat production in a system perspective mode. Nonetheless, the stewardship of zero tillage along with stubble residues could also be envisaged for the mitigation of soil sickness, too.

Assessment of Soil Quality Parameters under Rice and Maize Cropping Systems in Hilly Region of Central Vietnam

Background: Assessments of the soil quality can be used to evaluate sustainable soil management techniques. There is currently a considerable risk of desertification and soil deterioration in the Central region of Vietnam, which includes the province of Nghe An. The present research aims to report data on 10 physical and chemical properties of soil samples collected from the hilly zone of Nghe An province and relate their characteristics to cropping patterns. Methods: 142 composite soil samples were taken from the 0-20 cm layer of rice and maize crop plots after the harvest in the summer autumn season of 2022. They were air dried and sieved at a 2 mm mesh, then were analyzed to physico-chemical characterization using standard laboratory techniques in 2023. Result: Our results indicate that soil properties were linked significantly to different cropping patterns including paddy soils and maize soils. Analysis of soil characteristics demonstrated that rice soils are finer textured (clay content »30.1%), whereas maize soils have clay content lower than 25%. All 10 properties are significantly different for the rice field and another of which CEC and organic matter content are the most important to govern other characteristics. The content of organic matter was higher in fields with two rice crops (3.49%) than in fields with maize crops (2.20%).

Diverse biological communities promote SOM molecular diversity and compositional transformations during natural fallow stage in paddy fields

Monoculture cultivation patterns in agro-ecosystems only provide less varied soil organic matter (SOM) molecules of plant origin. Whether and how the natural fallow stage between cultivation seasons facilitates the restoration of SOM molecular diversity and mitigates the adverse impacts of constant cropping pattern is elusive. Here, we utilized FT-ICR-MS, UHPLC-MS/MS, and high-throughput sequencing to investigate the biological change processes in SOM molecular composition under cultivation and fallow status in a long-farmed paddy field. Our study showed that SOM molecular diversity increased by 45.70%–85.36% in fallow stage compared to rice cultivation season. SOM molecular diversity was positively correlated with bacterial diversity and root exudate molecular diversity, and negatively correlated with fungal diversity. Notably, root exudate molecular diversity accounted for 48.48% of the variation in SOM molecular diversity. The increased SOM molecular diversity in fallow stage was attributed more to the diverse plant-produced molecules than the microbe-consumed molecules. Plant species turnover resulted in the conversion of root exudate components to Organoheterocyclic compounds and Organic acids/derivatives from rice planting stage to fallow stage. Recruited microbes were dominated by Basidiomycita, Ascomycota, Acidobacteria, Chloroflexi and Proteobacteria, resulting in the transformation from carbohydrates, lipid-like SOM molecules to lipid-like and lignin-like SOM molecules. Both field and microcosm experiments confirmed that root exudates are the main source of SOM molecules, and are influenced by the soil microbial community. This study provides solid evidence that fallow status in agro-ecosystems provides explosion of biodiversity and counteracts the negative effects of long-term monoculture cultivation on SOM diversity.Graphical

Optimization of Irrigation Programming for Different Water Allocation Strategies at Network Level: Method and Application

Optimum irrigation programming at network level is important not only for maximum yield and benefit from the system but also for sustainable use of constrained resources. The subject of this investigation is to devise a method which enables optimum water allocation in irrigation networks, to apply it to a real system, and to analyze the results. In the first step, the irrigation district was divided into the most suitable water allocation zones considering the hydraulic properties of the canals. Next, alternative system rotation periods were defined in accordance with the properties of the research area, the hydraulic infrastructure of the irrigation network and the crop pattern. In this process, the model was run for five different water allocation strategies. The model was applied to the command area of Sarıkız Irrigation Association in the Ahmetli Regulator Right Bank Irrigation System in the Gediz Basin. Therefore, irrigation programs were prepared for the crop pattern, which receives water from 45 tertiary canals of the Y9 secondary. The irrigation time allocated to each tertiary canal and the amount of irrigation water were evaluated, together with the water shortage levels which occurred in these tertiary canals. The results indicated that the model defined the optimum system rotation period, the borders and the sizes of the most suitable water allocation zones, and the most suitable irrigation programs under the prevailing conditions. The proposed model can provide an insight for decision makers as a decision support tool.

Assessing Groundwater Use Efficiency and Productivity across Punjab Agriculture: District and Farm Size Perspectives

While previous studies have focused on the technical aspects of groundwater extraction and optimal cropping patterns, there is a notable lack of research on the socio-economic factors affecting groundwater access and agricultural productivity, especially at a micro-level in Punjab, India. This study, therefore, investigates the water use efficiency (WUE) and economic water productivity (EWP) of paddy and wheat crops across diverse farm sizes and districts in Punjab, offering critical insights into agricultural water management. The study is based on primary data from Punjab, India, with Sangrur, Jalandhar, Pathankot, and Bathinda districts selected for groundwater profile analysis via multistage random sampling of 246 farmers. Notably, Punjab exhibits high EWP for wheat, surpassing the national average. However, disparities exist across districts, emphasizing the importance of localized interventions. Larger farms consistently demonstrate higher WUE and EWP, underlining the significance of scale in optimizing water inputs. Factors such as farm size, crop variety, and regional variations significantly influence WUE and EWP. Tailored approaches for marginal farmers and districts with lower efficiency are crucial for promoting sustainable agricultural practices. The findings underscore the need for targeted policy interventions to enhance water use efficiency and productivity in Punjab’s agriculture sector.

Maize, Peanut, and Millet Rotations Improve Crop Yields by Altering the Microbial Community and Chemistry of Sandy Saline-Alkaline Soils.

Crop rotation increases crop yield, improves soil health, and reduces plant disease. However, few studies were conducted on the use of intensive cropping patterns to improve the microenvironment of saline soils. The present study thoroughly evaluated the impact of a three-year maize-peanut-millet crop rotation pattern on the crop yield. The rhizosphere soil of the crop was collected at maturity to assess the effects of crop rotation on the composition and function of microbial communities in different tillage layers (0-20 cm and 20-40 cm) of sandy saline-alkaline soils. After three years of crop rotation, the maize yield and economic benefits rose by an average of 32.07% and 22.25%, respectively, while output/input grew by 10.26%. The pH of the 0-40 cm tillage layer of saline-alkaline soils decreased by 2.36%, organic matter rose by 13.44%-15.84%, and soil-available nutrients of the 0-20 cm tillage layer increased by 11.94%-69.14%. As compared to continuous cropping, crop rotation boosted soil nitrogen and phosphorus metabolism capacity by 8.61%-88.65%. Enrichment of Actinobacteria and Basidiomycota increased crop yield. Crop rotation increases microbial community richness while decreasing diversity. The increase in abundance can diminish competitive relationships between species, boost synergistic capabilities, alter bacterial and fungal community structure, and enhance microbial community function, all of which elevate crop yields. The obtained insights can contribute to achieving optimal management of intensive cultivation patterns and green sustainable development.

Europe's orchard: The role of irrigation on the Spanish agricultural production

In the latter half of the twentieth century, Spanish agriculture witnessed sustained production growth, aligning with Western European levels after a period of lag in both production and productivity. This growth predominantly occurred in the warm, sunny provinces of southern and eastern Spain, which specialized in high-value crops such as fruits, vegetables, and olive oil. Various factors have been identified in the scientific literature as contributors to this transformation, including technological innovations, the internationalization of Spanish agriculture, domestic development, and advancements in irrigation. This paper aims to examine the impact of irrigation on Spanish agricultural production, focusing on the creation of regional and productive disparities since the mid-twentieth century. These diverse effects have been crucial in shaping the regional and product-specific specialization patterns that characterize the historical dynamism of Spanish agriculture. To that aim, we have compiled a comprehensive database containing detailed information on agricultural production values, irrigated and rainfed area and yields. The level of disaggregation of data, both in terms of production and regional breakdowns, enables us to identify specific regional and crop patterns. Additionally, this study highlights the significant and regionally varied transformations within the Spanish agricultural sector over the long term, which have had profound effects on rural areas.

Market Potential and Farmers' Buying Behaviour towards Cumin Seeds in Rajkot District, Gujarat, India

The Indian agriculture sector heavily relies on quality seeds for optimal crop productivity. The Indian seed industry plays a crucial role in providing high-quality seeds to farmers. Hybrid seeds are preferred over open-pollinated varieties, and quality seeds not only increase yield but also reduce production costs. The majority of farmers in Rajkot District are middle-aged, with varied education levels and predominantly low incomes. Experienced middle-aged farmers dominate the region, with small and semi-medium landholdings. Different crops are grown across the Kharif, Rabi, and summer seasons, highlighting diverse cropping patterns. The survey in Rajkot District reveals diverse irrigation sources, with various preferences for cumin seed brands among farmers. Factors influencing cumin seed purchases include performance, branding, quality, growth, and price. Retailers' suggestions, demonstrations, and farmers' meetings are the most effective marketing tools. Overall, 61.84% of respondents rated their experience as good, suggesting satisfaction with products and services, but there is room for improvement.

Cropping Systems Diversification as an Approach to Enhancing Crop Productivity: A Review

Agricultural intensification increased crop productivity but simplified production with lower diversity of cropping systems, higher genetic uniformity, and a higher uniformity of agricultural landscapes. Associated detrimental effects on the environment and biodiversity as well as the resilience and adaptability of cropping systems to climate change are of growing concern. Crop diversification may stabilize productivity of cropping systems and reduce negative environmental impacts and loss of biodiversity, but a shared understanding of crop diversification including approaches towards a more systematic research is lacking. The current review highlights the use of Cropping Systems Diversification as an Approach to Enhancing Crop Productivity. Crop diversification can be considered as an attempt to increase the diversity of crops through, e.g. crop rotation, multiple cropping or intercropping, compared to specialized farming with the aim to improve the productivity, stability and delivery of ecosystem services. It can be one measure to develop more sustainable production systems, develop value-chains for minor crops. Crop diversification practices can include higher crop diversity, more diverse crop, mixed cropping; cultivation of grain legumes in otherwise cereal dominated systems, perennial leys or grassland and regionally adapted varieties or variety mixtures. Crop diversification and/or additional diversification measures like variation of seeding time or changing cropping patterns have the potential to lead to higher and more stable yields, increase profitability and lead to greater resilience of agro-ecosystems in the long term. These practices have the potential to make cropping systems more diverse in space, time and genetics. Through a balanced portfolio approach to agricultural sustainability, cropping system performance can be optimized in multiple dimensions, including food and biomass production, profit, energy use, pest management, and environmental impacts.

Crop type classification, trends, and patterns of central California agricultural fields from 2005 to 2020

AbstractCalifornia produces many key agricultural products in the United States. Current geospatial agricultural datasets are limited in mapping accuracy, spatial context, or observation period. This study uses machine learning and high‐resolution imagery to produce a time series of crop maps to assess crop type trends and patterns across central California from 2005 to 2020. National Agriculture Imagery Program and Landsat imagery were used to classify nine crop types that are common in the study region: grain crops, field crops, rice, citrus and subtropical, deciduous fruit and nut, vineyard, berry and vegetable, pasture, and fallow/young perennial crop types. To create labeled data, we sampled 1253 fields and manually identified crop types for each examined year using high‐resolution imagery and Landsat normalized difference vegetation index time series. We applied a random forest machine learning algorithm in Google Earth Engine. Results show that the mean overall classification accuracy of the nine‐class map was 93.1%, with individual accuracies ranging from 99.3% (rice) to 89.5% (fallow/young perennial). Mann–Kendall trend tests showed significant (p < 0.05) declines in field crop and pasture area during the study period, while deciduous fruit and nut, citrus and subtropical, and fallow/young perennial crop types experienced significant increases. At an aggregate level, there was a general shift from annual crop types to perennial crop types. These data provide a 16‐year time span of spatially explicit crop type classifications, trends, and patterns in central California that can be used to aid managers and decision makers for resource planning or hazard mitigation.