Journal articles on the topic 'Crop yields – Methodology'
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1
Shirsath, Paresh B., Vinay Kumar Sehgal, and Pramod K. Aggarwal. "Downscaling Regional Crop Yields to Local Scale Using Remote Sensing." Agriculture 10, no. 3 (March 2, 2020): 58. http://dx.doi.org/10.3390/agriculture10030058.
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Local-scale crop yield datasets are not readily available in most of the developing world. Local-scale crop yield datasets are of great use for risk transfer and risk management in agriculture. In this article, we present a simple method for disaggregation of district-level production statistics over crop pixels by using a remote sensing approach. We also quantified the error in the disaggregated statistics to ascertain its usefulness for crop insurance purposes. The methodology development was attempted in Parbhani district of Maharashtra state with wheat and sorghum crops in the winter season. The methodology uses the ratio of Enhanced Vegetation Index (EVI) of pixel to total EVI of the crop pixels in that district corresponding to the growth phase of the crop. It resulted in the generation of crop yield maps at the 500 m resolution pixel (grid) level. The methodology was repeated to generate time-series maps of crop yield. In general, there was a good correspondence between disaggregated crop yield and sub-district level crop yields with a correlation coefficient of 0.9.
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Dmytrenko, V. P., L. P. Odnolyetok, О. О. Kryvoshein, and A. V. Krukivska. "Development of the methodology of estimating of agricultural crop yield potential with consideration of climate and agrophytotechnology impact." Ukrainian hydrometeorological journal, no. 20 (October 29, 2017): 52–60. http://dx.doi.org/10.31481/uhmj.20.2017.06.
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In the paper it is outlined the main methodological positions and the results of the approbation of new approaches to the integrated assessment of the potential of crop yields.
There are considered the theoretical foundations of a joint assessment of the biological, ecological and anthropogenic components of the yield potential of agricultural crops which are based on the ecosystem concept and the mathematical model "Weather-Crop Yield" developed by V. P. Dmytrenko. In the considered approaches the peculiarities of the influence of various environmental factors on the formation of crop yields are determined by indicators of various potential yields - general, climatic and trend (agrotechnological). Each type of yield potential can be used for evaluation of the effectiveness of the conditions of field crop growing for each factor taken into account, as well as the optimality criterion in the agrometeorological adaptation strategies and also as a criterion for the degree of sensitivity of the yield level to the conditions of crops cultivating.
The developed approaches are tested on the example of estimation of long-term dynamics of winter wheat yield potential in Ukraine. According to the results of the evaluation of different factors of the potential of the productivity of winter wheat for the periods 1961-1990 and 1991-2010 the dominant importance of organizational and technological processes in comparison with the contribution of changes of agroclimatic conditions has been determined in both periods.
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Neill, D. E., and G. B. Follas. "Use of crop sensing technology in crop protection research." New Zealand Plant Protection 64 (January 8, 2011): 287. http://dx.doi.org/10.30843/nzpp.2011.64.5993.
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Crop sensing technology is a new tool being rapidly adopted by farmers as a key component of precision agriculture This technology uses sensors to calculate normalized difference vegetative index (NDVI) by emitting red and near infrared light towards the crop and measuring the crops reflectance NDVI is used to evaluate canopy greenness plant biomass and as an indicator of plant health and vigour The methodology relevance and benefits of using this technology in crop protection trials are currently unclear A handheld Greenseeker (Ntech Industries USA) was used to record NDVI on a range of trials from 20082011 to establish whether crop sensing could replace visual assessments for disease and enable yield prediction NDVI readings were compared against other parameters measured in the trials such as disease scores green leaf area percentage and yields In some trials the NDVI followed similar trends to disease green leaf retention and yields However in other cases where clear treatment effects were recorded through visual or yield assessments there were no differences in NDVI between the treatments As NDVI can be affected by a number of factors it was concluded that crop sensing technology can be used as an additional objective measurement in conjunction with standard assessment practice but without further investigation cannot replace traditional assessment methods
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Narayan, Kale Jaydeep. "Review of Crop Yield Prediction using Machine Learning Techniques." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 4626–28. http://dx.doi.org/10.22214/ijraset.2021.36058.
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Machine learning (ML) could be a helpful decision-making tool for predicting crop yields, in addition as for deciding what crops to plant and what to try throughout the crop's growth season. To help agricultural yield prediction studies, variety of machine learning techniques are used. I performed a literature review (LR) to extract and synthesize the algorithms and options employed in crop production prediction analysis. Temperature, rainfall, and soil types are most common measure used in the prediction as per my knowledge, whereas Artificial Neural Networks is the foremost normally used methodology in these models.
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Kirthiga, S. M., and N. R. Patel. "In-Season Wheat Yield Forecasting at High Resolution Using Regional Climate Model and Crop Model." AgriEngineering 4, no. 4 (October 30, 2022): 1054–75. http://dx.doi.org/10.3390/agriengineering4040066.
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In-season crop production forecasts at the regional or sub-regional scale are essential to aid in food security through early warning of harvest shortfall/surplus, tailoring crop management decisions and addressing climatic shock. Considering the efforts to establish a framework towards quantifying the crop yield prediction at regional scales are limited, we investigated the utility of combining crop model with the regional weather prediction model to forecast winter wheat yields over space. The exercise was performed for various lead-times in the regions of Punjab and Haryana for the years 2008–2009. A numerical weather prediction (NWP) model was used to generate micro-meteorological variables at different lead times (1-week, 2-weeks, 3-weeks and 5-weeks) ahead of crop harvest and used within the CERES-Wheat crop simulation model gridded framework at a spatial resolution of 10 km. Various scenarios of the yield forecasts were verified with district-wide reported yield values. Average deviations of −12 to 3% from the actual district-wise wheat yields were observed across the lead times. The 3-weeks-ahead yield forecasts yielded a maximum agreement index of 0.86 with a root mean squared error (RMSE) of 327.75 kg/ha and a relative deviation of −5.35%. The critical crop growth stages were found to be highly sensitive to the errors in the weather forecast, and thus made a huge impact on the predicted crop yields. The 5-weeks-ahead weather forecasts generated anomalous meteorological data during flowering and grain-filling crop growth stages, and thus had the highest negative impact on the simulated yields. The agreement index of the 5-week-ahead forecasts was 0.41 with an RMSE of 415.15 kg ha−1 and relative deviation of −2.77 ± 5.01. The proposed methodology showed significant forecast skill for extended space and time scale crop yield forecasting, offering scope for further research and practical applicability.
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Eser, Adnan, Hajnalka Kató, Laura Kempf, and Márton Jolánkai. "Water footprint of yield protein content of twelve field crop species on a Hungarian crop site." Agrokémia és Talajtan 68, Supplement (December 2019): 53–60. http://dx.doi.org/10.1556/0088.2019.00041.
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Abstract Water availability is one of the major physiological factors influencing plant growth and development. An assessment study has been done at the Szent István University, Gödöllő to evaluate and identify the water footprint of protein yield of field crop species. Twelve field crop species (Sugar beet Beta vulgaris, spring and winter barley Hordeum vulgare, winter wheat Triticum aestivum, maize Zea mays, sunflower Helianthus annuus, peas Pisum sativum, potato Solanum tuberosum, alfalfa Medicago sativa, oilseed rape Brassica napus, rye Secale cereale and oats Avena sativa) were involved in the study. Evapotranspiration patterns of the crops studied have been identified by the regular agroclimatology methodology and physiologically reliable protein ranges within crop yields were evaluated. The results obtained suggest, that water footprint of cereals proved to be the lowest, however maize values were highly affected by the high variability of protein yield. Oilseed crops had considerably high protein yield with medium water efficiency. Alfalfa, potato and sugar beet water footprints were in accordance with their evapotranspiration patterns. Protein based water footprint assessment seems to be more applicable in crop species evaluations than that of yield based methodologies.
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Shevchenko, M. S., L. M. Decyatnik, and K. A. Derevenets-Shevchenko. "Modern systems of agriculture and a new interpretation of crop rotation value of agricultural crops." Scientific Journal Grain Crops 4, no. 2 (December 11, 2020): 319–29. http://dx.doi.org/10.31867/2523-4544/0141.
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Based on a broad experimental study of crop rotation productivity in different locations of the Steppe zone, a correlative model for estimating the role of predecessors in the formation of post-crop yields has been developed. The connection between quality of agrotechnologies and change of degree of crop rotation competitiveness of culture is presented. A retrospective analysis of the efficiency of farming and crop rotation systems showed that the constant improvement of varieties and hybrids of crops and technologies for their cultivation created objective agrobiological grounds for reassessment of predecessors in crop rotation. The main motive for this transformation was that in modern agricultural systems, high-potential biotechnological resources allow to obtain higher crop yields on the worst predecessors than on the best in the past. In order to universalize the evaluation of crop rotation efficiency and model their productivity, it is proposed to introduce a crop rotation depression coefficient, which shows the share of yield remaining after individual predecessors compared to its baseline level after black fallow. The most favorable conditions developed after crops with a coefficient above 0,80 – winter wheat, barley, rape, rye, spring barley, oats. At the same time, the development of post-rotational crops was significantly inhibited by sunflower, corn for grain and silage, beets, sorghum and soybeans, their depression coef-ficient was 0,66–0,78. The proposed methodology of system analysis for the assessment of predecessors opens wider opportunities for the formation of adapted crop rotations, optimization the set of crops to market requirements, formation important adjustments to crop rotations in extreme conditions, regulation crop rotation productivity taking into account agrotechnological modernization. Keywords: crop rotation, tillage, fertilizers, crops, grain, predecessors, harvest, minimization.
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Arumugam, Surendran, Ashok K.R., Suren N. Kulshreshtha., Isaac Vellangany, and Ramu Govindasamy. "Yield variability in rainfed crops as influenced by climate variables." International Journal of Climate Change Strategies and Management 7, no. 4 (November 16, 2015): 442–59. http://dx.doi.org/10.1108/ijccsm-08-2013-0096.
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Purpose – This paper aims to explore the impact of climate change on yields and yield variances in major rainfed crops and measure possible changes in yields under projected climate changes in different agro-climatic zones of Tamil Nadu, India. Although many empirical studies report the influence of climate change on crop yield, only few address the effect on yield variances. Even in such cases, the reported yield variances were obtained through simulation studies rather than from actual observations. In this context, the present study analyzes the impact of climate change on crops yield and yield variance using the observed yields. Design/methodology/approach – The Just-Pope yield function (1978) is used to analyze the impact of climate change on mean yield and variance. The estimated coefficient from Just-Pope yield function and the projected climatic data for the year 2030 are incorporated to capture the projected changes in crop yield and variances. Findings – By the year 2030, the yield of pulses is estimated to decline in all the zones (Northeast, Northwest, Western, Cauvery delta, South and Southern zones), with significant declines in the Northeast zone (6.07 per cent), Cauvery delta zone (3.55 per cent) and South zone (3.54 per cent). Sorghum yield may suffer more in Western zone (2.63 per cent), Southern zone (1.92 per cent) and Northeast zone (1.62 per cent). Moreover, the yield of spiked millet is more likely to decrease in the Southern zone (1.39 per cent), Northeast zone (1.21 per cent) and Cauvery delta zone (0.24 per cent), and the yield of cotton may also decline in the Northeast zone (12.99 per cent), Northwest zone (8.05 per cent) and Western zone (2.10 per cent) of Tamil Nadu, India. Originality/value – The study recommends introducing appropriate crop insurance policies to address possible financial losses to the farmers. Prioritizing area-specific stress-tolerant crop varieties without complementing yield would sustain crops cultivation further.
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Delbridge, Timothy A., and Robert P. King. "How important is the transitional yield (t-yield)? An analysis of reforms to organic crop insurance." Agricultural Finance Review 79, no. 2 (April 1, 2019): 234–54. http://dx.doi.org/10.1108/afr-03-2017-0022.
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PurposeThe USDA’s Risk Management Agency (RMA) made several changes to the crop insurance products available to organic growers for the 2014 crop year. Most notably, a 5 percent premium surcharge was removed and organic-specific transitional yields (t-yields) were issued for the first time. The purpose of this paper is to use farm-level organic crop yield data to analyze the impact of these reforms on producer insurance outcomes and compare the insurance options for new organic growers.Design/methodology/approachThis study uses a unique panel data set of organic corn and soybean yields to analyze the impact of organic crop insurance reforms. Actual Production History values and premium rates are calculated for each farm and crop yield sequence. Producer loss ratios and subsidized premium wedges are compared for yield, revenue and area-risk products before and after the instituted reforms.FindingsResults indicate that RMA succeeded in improving the actuarial soundness of the organic insurance program, though further refinement of organic t-yields may be necessary to accurately reflect the yield potential of organic producers and avoid reductions in program participation.Originality/valueThis paper provides insight into the effectiveness of reforms intended to improve the actuarial soundness of organic crop insurance and demonstrates the effect that the reforms are likely to have on new and existing organic farms. Because this analysis uses data collected independently of RMA and includes farms that may or may not have purchased crop insurance, it avoids the self-selection problems that might affect analyses using crop insurance program data.
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SHARIFIFAR, Amin, Hadi GHORBANI, and Fereydoon SARMADIAN. "Soil suitability evaluation for crop selection using fuzzy sets methodology." Acta agriculturae Slovenica 107, no. 1 (April 6, 2016): 159. http://dx.doi.org/10.14720/aas.2016.107.1.16.
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In this study appraisal of four different agricultural land evaluation methods including the so-called Storie method, square root method, maximum limitation method and fuzzy sets method, was done. The study was performed in Bastam region, located in Semnan province at the north east of Iran.<strong> </strong>Three crops including tomato, wheat and potato were assessed for the purpose of this research. Soil characteristics assessed were rooting depth, CaCo<sub>3, </sub>organic carboncontent, clay content, pH and slope gradient. Statistical analyses were done at significance levels of <em>α </em>= 0.1 and <em>α</em> = 0.05. Results of regression between land indices, calculated through the four methods, with observed yields of the crops, showed that the regression were significant in fuzzy sets method for all of the assessed crops at <em>p </em>= 0.05 but not significant in maximum limitation method for any of the crops. The Storie and square root methods also showed a significant correlation with wheat yield at <em>p </em>= 0.1. This study was a demonstrative test of fuzzy sets theory in land suitability evaluation for agricultural uses, which revealed that this methodology is the most correct method in given circumstances.
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Panoutsou, Calliope, and David Chiaramonti. "Socio-Economic Opportunities from Miscanthus Cultivation in Marginal Land for Bioenergy." Energies 13, no. 11 (May 29, 2020): 2741. http://dx.doi.org/10.3390/en13112741.
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Substantial areas of agricultural land in south European countries are becoming increasingly marginal and being abandoned due to arid climate with prolonged summers and low rainfall. Perennial, lignocellulosic crops, such as Miscanthus, offer an outlet that couples agriculture with energy, creates employment, and increases profits from feedstock production in rural areas. This research paper follows an Input Output methodology and uses an econometric model to investigate the impact of crop yielding performance and marginal land to jobs and profit from the cultivation and supply of Miscanthus in low quality, marginal land in Italy and Greece. Two value chain cases are analysed: small scale Combined Heat and Power (CHP) and Fast Pyrolysis Bio Oil (FPBO). The cultivation of Miscanthus in both reference value chains exhibits good employment prospects, with smaller scale value chains creating more labour-intensive logistics operations. The activities can also generate substantial financial profit especially with higher crop yields. Results show a pronounced relationship between profitability and crop yield for both reference value chains - cultivation and supply operations become more profitable with increasing yield. It is, therefore, important to achieve higher yields through good cropping practices, while maintaining high levels of environmental sustainability.
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Raksapatcharawong, Mongkol, Watcharee Veerakachen, Koki Homma, Masayasu Maki, and Kazuo Oki. "Satellite-Based Drought Impact Assessment on Rice Yield in Thailand with SIMRIW−RS." Remote Sensing 12, no. 13 (June 30, 2020): 2099. http://dx.doi.org/10.3390/rs12132099.
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Advances in remote sensing technologies have enabled effective drought monitoring globally, even in data-limited areas. However, the negative impact of drought on crop yields still necessitates stakeholders to make informed decisions according to its severity. This research proposes an algorithm to combine a drought monitoring model, based on rainfall, land surface temperature (LST), and normalized difference vegetation index/leaf area index (NDVI/LAI) satellite products, with a crop simulation model to assess drought impact on rice yields in Thailand. Typical crop simulation models can provide yield information, but the requirement for a complicated set of inputs prohibits their potential due to insufficient data. This work utilizes a rice crop simulation model called the Simulation Model for Use with Remote Sensing (SIMRIW–RS), whose inputs can mostly be satisfied by such satellite products. Based on experimental data collected during the 2018/19 crop seasons, this approach can successfully provide a drought monitoring function as well as effectively estimate the rice yield with mean absolute percentage error (MAPE) around 5%. In addition, we show that SIMRIW–RS can reasonably predict the rice yield when historical weather data is available. In effect, this research contributes a methodology to assess the drought impact on rice yields on a farm to regional scale, relevant to crop insurance and adaptation schemes to mitigate climate change.
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Sukhanovskii, Yurii, Anastasya Prushchik, Vladimir Vitovtov, and Alexandr Titov. "On methodology problems of developing innovative technologies taking into account anti-erosion measures." BIO Web of Conferences 32 (2021): 01008. http://dx.doi.org/10.1051/bioconf/20213201008.
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The increase in world population and the decline in soil resources requires the increase in crop yields. Erosion and soil pollution are among the major threats to soil resources. With modern land use the rate of erosion exceeds the rate of soil formation. It is almost impossible to restore erosion soil loss. Soil pollution is a source of contaminated crop products and environment. In crop production innovative technologies are needed that must simultaneously solve three problems. The first problem is to ensure the necessary quantity and quality of crop products. The second problem is to preserve soil resources. The third one is to preserve the environment. In Russia, the increase in yields is mainly due to an increase in rates of mineral fertilizers, the use of plant protection tools and the use of varieties with a greater ability to utilize mineral fertilizers. In some regions of Russia, up to 70% of the arable land area is subject to water erosion of the soil. For the conditions of Russia, an analysis of the existing problems in assessing the long-term consequences of new technologies in crop production was carried out. Approaches have been proposed to solve some of the problems.
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Meinke, Holger, and Graeme L. Hammer. "Forecasting regional crop production using SOI phases: an example for the Australian peanut industry." Australian Journal of Agricultural Research 48, no. 6 (1997): 789. http://dx.doi.org/10.1071/a96155.
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Using peanuts as an example, a generic methodology is presented to forward-estimate regional crop production and associated climatic risks based on phases of the Southern Oscillation Index (SOI). Yield fluctuations caused by a highly variable rainfall environment are of concern to peanut processing and marketing o/Southern bodies the industtry could profitable to adjust their operations stategically. Significantly , physically based lag-relationships exist between an index of ocean/atmospher EI Niño/southern Oscillation phenomenon and future rainfall in Australia and elsewhere. Combining knowledge of SOI phases in November and December with output from a dynamic simulation model allows the derivation of yield probability distributions based on historic rainfall data. This information is available shortly after planting a crop and at least 3-5 months prior to harvest. The study shows that in years when the November-December SOI phase is positive there is an 80% chance of exceeding average district yields. Conversely, in years when the November-December SOI phase is either negative or rapidly falling there is only a 5% chance of exceeding average district yields, but a 95% chance of below average yields. This information allows the industry to adjust strategically for the expected volume of production. The study shows that simulation models can enhance SOI signals contained in rainfall distributions by discriminating between useful and damaging rainfall events. The methodology can be applied to other industries and regions.
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Hina, Firdous. "A Review on Agriculture Crop Prediction Techniques Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (December 31, 2021): 484–88. http://dx.doi.org/10.22214/ijraset.2021.39170.
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Abstract: Machine learning is a useful decision-making tool for predicting crop yields, as well as for deciding what crops to plant and what to do during the crop's growth season. To aid agricultural yield prediction studies, a number of machine learning techniques have been used. We employed a Systematic Literature Review (SLR) to extract and synthesize the algorithms and features used in crop production prediction research in this investigation This paper provides a comprehensive overview of the most recent machine learning applications in agriculture, with a focus on pre-harvesting, harvesting, and post-harvesting issues The papers have been studied in depth, analysed the methodology and features employed, and made recommendations for future study. Temperature, rainfall, and soil type are the most commonly utilised features, according to our data, while Artificial Neural Networks are the most commonly employed method in these models.
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K. N. CHAUDHARI, ROJALIN TRIPATHY, and N. K. PATEL. "Spatial wheat yield prediction using crop simulation model, GIS, remote sensing and ground observed data." Journal of Agrometeorology 12, no. 2 (December 1, 2010): 174–80. http://dx.doi.org/10.54386/jam.v12i2.1300.
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A study was conducted with a broad objective of developing and demonstrating a methodology for crop growth monitoring and yield forecasting which can provide periodical crop growth assessment with spatial information. The procedure was developed to generate grid-weather, link the point based simulation model WOFOST (World Food Studies) to spatial inputs like crop, soil and weather and predict wheat yield at grid and administrative scale. Two approaches were adopted to predict wheat yield; a) the regression approach, in which simulated potential yields were regressed with final estimated yields by Directorate of Economics and Statistics (DES) for each of the six major wheat growing states and b) forcing approach in which LAI for each grid (25km x 25km) derived from remote sensing was forced into the simulation model to divert the simulation output and final grain yield into right direction. The deviations between the estimated state yield and reported yield were more in case of the forcing (0.7 – 25.4 %) as compared to regression approach (0.5 – 9.2 %). However, the spatial variability at grid level was explained more in case of forcing approach. Results indicated that regression approach is suitable for in season yield forecasting at state level and forcing approach is better for spatial crop condition assessment and crop growth monitoring.
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Ahmad, T., F. Y. Hafeez, T. Mahmood, and K. A. Malik. "Residual effect of nitrogen fixed by mungbean (Vigna radiata) and blackgram (Vigna mungo) on subsequent rice and wheat crops." Australian Journal of Experimental Agriculture 41, no. 2 (2001): 245. http://dx.doi.org/10.1071/ea99175.
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Annual crop legumes, grown in rotation with cereal crops, contribute to the total pool of nitrogen in the soil and improve the yield of cereals. The present study aimed at the quantification of nitrogen fixation by mungbean and blackgram using 15N isotopic dilution methodology; and the quantification of grain and nitrogen yield differences of succeeding rice and wheat crops compared with a cereal–cereal rotation. There were 2 experiments in separate fields but with the same layout: in experiment 1, rice followed the mungbean and blackgram varieties and in experiment 2, wheat followed the mungbean and blackgram varieties. Nitrogen fixed ranged from 26 to 36 kg/ha in experiment 1 and from 30 to 36 kg/ha in experiment 2. Soil nitrogen spared by legume crops ranged from 2 to 26 kg/ha in experiment 1 and from 4 to 23 kg/ha in experiment 2. Rice paddy yields were 0.6–1.1 t/ha higher in the legume–cereal rotation than in the cereal–cereal sequence. Similarly, wheat grain yields were 0.5–1.1 t/ha higher in the legume–cereal rotation.
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Havlíčková, Kamila, Jan Weger, and Jana Šedivá. "Methodology of analysis of biomass potential using GIS in the Czech Republic." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 58, no. 5 (2010): 161–70. http://dx.doi.org/10.11118/actaun201058050161.
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This article deals with the issue of a methodology for and analyzing biomass potential in the Czech Republic using a geographic information system. The biomass sources considered include cereal and rape straw, permanent grasslands and forest residuals. The process of assessing biomass potential from agricultural soils is based on assigning yields of individual biomass sources according to the production soil-ecological units (BPEJ) which were created for better agricultural planning in the Czech and Slovak Republics. The analysis of energy crop suitability is based on the evaluation of crop yields related to individual BPEJ’s respectively to its component the main soil climate units (HPKJ). To ascertain the production potential of residual biomass (straw) from conventional agriculture, the wheat (grain) yield related to HPKJ was multiplied by the straw coefficient. The yield of the permanent grasslands in the main soil climate units was also multiplied by the coefficient of dry matter. The methodology for the analysis of biomass potential for forest land is based on forest management plans that describe the composition of all forest stands. Data from these forest management plans can be used to determine in detail the potential of the forest biomass in individual periods according to the plan for silvacultural treatment and major harvest of the wood. This detailed analysis is suitable only on the municipality level. On a higher government level, the forest management plan can be used to calculate a coefficient that determines the average yield from biomass in the form of forest residuals and in relation to the forest size for specific areas or levels of analysis. The energy potential of residual biomass is around 136 PJ from present area of conventional agriculture in the Czech Republic. Biomass consumption in animal production and harvest loses were deducted from this calculation.
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Kuschel-Otárola, Mathias, Niels Schütze, Eduardo Holzapfel, Alex Godoy-Faúndez, Oleksandr Mialyk, and Diego Rivera. "Estimation of Yield Response Factor for Each Growth Stage under Local Conditions Using AquaCrop-OS." Water 12, no. 4 (April 10, 2020): 1080. http://dx.doi.org/10.3390/w12041080.
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We propose a methodology to estimate the yield response factor (i.e., the slope of the water-yield function) under local conditions for a given crop, weather, sowing date, and management at each growth stage using AquaCrop-OS. The methodology was applied to three crops (maize, sugar beet, and wheat) and four soil types (clay loam, loam, silty clay loam, and silty loam), considering three levels of bulk density: low, medium, and high. Yields are estimated for different weather and management scenarios using a problem-specific algorithm for optimal irrigation scheduling with limited water supply (GET-OPTIS). Our results show a good agreement between benchmarking (mathematical approach) and benchmark (estimated by AquaCrop-OS) using the Normalised Root Mean Square Error (NRMSE), allowing us to estimate reliable yield response factors ( K y ) under local conditions and to dispose of the typical simple mathematical approach, which estimates the yield reduction as a result of water scarcity at each growth stage.
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Achli, Soumia, Terence Epule Epule, Driss Dhiba, Abdelghani Chehbouni, and Salah Er-Raki. "Vulnerability of Barley, Maize, and Wheat Yields to Variations in Growing Season Precipitation in Morocco." Applied Sciences 12, no. 7 (March 27, 2022): 3407. http://dx.doi.org/10.3390/app12073407.
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Climate change continues to have adverse effects on crop yields in Africa and globally. In Morocco, rising temperatures and declining precipitation are having daunting effects on the vulnerability of crops. This study examines the vulnerability of barley, maize, and wheat to variations in growing season precipitation and socio-economic proxies of adaptive capacity such as literacy and poverty rates at both national and sub-national scales in Morocco. The methodology is based on a composite vulnerability index (vulnerability is a function of exposure, sensitivity, and adaptive capacity). National and sub-national crop yield data used to compute the sensitivity index were downloaded from FAOSTAT and the global crop yield gaps Atlas. The mean annual growing season precipitation data at both the national and sub-national scales used to compute the exposure index were downloaded from the world bank climate portal. Proxy data for adaptive capacity in the form of literacy and poverty rates were downloaded from the world bank, figshare, and MPR archives. The CANESM model was used to validate the crop yield observations. The results show that wheat shows the lowest vulnerability and the highest adaptive capacity, while maize has the highest vulnerability and lowest adaptive capacity. Sub-nationally, vulnerability indexes decrease northwards while adaptive capacity and normalized growing season precipitation increase northwards. Wheat also shows the lowest vulnerability and highest adaptive capacity and normalized growing season precipitation at each latitude northward. Model validation shows that the models used here reproduce most of the spatial patterns of the crops concerned. These findings have implications for climate change adaptation and climate policy in Morocco, as it becomes evident which of these most cultivated crops are more vulnerable nationally and spatially. These results have implications for future research, as it might be important to understand how these crops perform under growing season temperature as well as what future projections and yield gaps can be observed.
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Browning, Gordon, and Andrew Dorward. "A Survey Methodology for Assessing Yield Potential and Coffee Berry Losses in Peasant Coffee." Experimental Agriculture 25, no. 2 (April 1989): 235–42. http://dx.doi.org/10.1017/s0014479700016732.
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SUMMARYThe effects of coffee berry disease (CBD) on yield are usually estimated by counting infected berries on marked branches throughout fruit development. This method is impractical when surveys are required of yield losses over large areas of extensively cultivated peasant coffee. A survey method is described for estimating yield potentials and the loss in yields due to severe CBD infection under such conditions. The method exploits the relation between numbers of flowering nodes and yield per tree, and requires a single subjective estimate of disease incidence on young expanding berries. Yield losses are estimated from the changes in this relation resulting from berry infection. Results are presented for peasant coffee at different stages of rehabilitation in Ethiopia. They indicate that the method is suitable for estimating.production in agronomic improvement projects and for identifying areas where CBD incidence might economically justify fungicide spraying.
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Fisher, P. D., M. Abuzar, M. A. Rab, F. Best, and S. Chandra. "Advances in precision agriculture in south-eastern Australia. I. A regression methodology to simulate spatial variation in cereal yields using farmers' historical paddock yields and normalised difference vegetation index." Crop and Pasture Science 60, no. 9 (2009): 844. http://dx.doi.org/10.1071/cp08347.
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Despite considerable interest by Australian farmers in precision agriculture (PA), its uptake has been low. Analysis of the possible financial benefits of alternative management options that are based on the underlying patterns of observed spatial and temporal yield variability in a paddock could increase farmer confidence in adopting PA. The cost and difficulty in collecting harvester yield maps have meant that spatial yield data are generally not available in Australia. This study proposes a simple, economical and easy to use approach to generate simulated yield maps by using paddock-specific relationships between satellite normalised difference vegetation index (NDVI) and the farmer’s average paddock yield records. The concept behind the approach is illustrated using a limited dataset. For each of 12 paddocks in a property where a farmer’s paddock-level yield data were available for 3–5 years, the paddock-level yields showed a close to linear relationship with paddock-level NDVI across seasons. This estimated linear relationship for each paddock was used to simulate mean yields for the paddock at the subpaddock level at which NDVI data were available. For one paddock of 167 ha, for which 4 years of harvester yield data and 6 years of NDVI data were available, the map of simulated mean yield was compared with the map of harvester mean yield. The difference between the two maps, expressed as percentage deviation from the observed mean yield, was <20% for 63% of the paddock and <40% for 78% of the paddock area. For 3 seasons when there were both harvester yield data and NDVI data, the individual season simulated yields were within 30% of the observed yields for over 70% of the paddock area in 2 of the seasons, which is comparable with spatial crop modelling results reported elsewhere. For the third season, simulated yields were within 30% of the observed yield in only 22% of the paddock, but poor seasonal conditions meant that 40% of the paddock yielded <100 kg/ha. To illustrate the type of financial analysis of alternative management options that could be undertaken using the simulated yield data, a simple economic analysis comparing uniform v. variable rate nitrogen fertiliser is reported. This indicated that the benefits of using variable rate technology varied considerably between paddocks, depending on the degree of spatial yield variability. The proposed simulated yield mapping requires greater validation with larger datasets and a wider range of sites, but potentially offers growers and land managers a rapid and cost-effective tool for the initial estimation of subpaddock yield variability. Such maps could provide growers with the information necessary to carry out on-farm testing of the potential benefits of using variable applications of agronomic inputs, and to evaluate the financial benefits of greater investment in PA technology.
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Kamunywe, Jilet Makrini. "Provenance of Food Insecurity. A Critical Literature Review." Journal of Climate Policy 1, no. 1 (October 8, 2022): 36–48. http://dx.doi.org/10.47941/jcp.1057.
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Purpose: In particular, it affects crop production due to temperature and rainfall changes, and more extreme weather events. Erratic rainfall and temperatures are said to reduce crop yields through shortening growing seasons, exaggeration of water stress and promote invasion and intensity of weeds, pests and diseases. The overall objective of this study was to examine provenance of food insecurity.
Methodology: The paper used a desk study review methodology where relevant empirical literature was reviewed to identify main themes and to extract knowledge gaps.
Findings: This study concluded that the effects of rainfall and temperature adversely affect to maize and beans production in Africa. The effects are significant and positive for all crops. Generally, there is better correlation of production with precipitation than temperature. From the findings Pearson’s correlation showed positive correlation for crop yields against climate data except for minimum temperature that exhibited weak negative correlation for maize and no correlation for beans. This shows plainly that “business as usual” food grain growth is altered by changes in climate. These changes could alter growing seasons, planting and harvesting calendars or even invasion of pests, weeds and diseases.
Unique Contribution to Theory, Policy and Practice: This study recommended that adoption of other food grains that may do well under this current climatic condition. Crops such as millet and sorghum are encouraged due to their high tolerance to droughts, soil infertility and high temperatures. Households also to be guided on how to monitor crop-climate relationship so as to achieve improved crop production drought resistant modern seed varieties are very important to the population.
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Purdy, Sarah Jane, Amitha K. Hewavitharana, Razlin Azman Halimi, Nelson Joel Magner, Tyson James Peterswald, Amy Trebilco, Tobias Kretzschmar, and Deborah Hailstones. "A One-Step Grafting Methodology Can Adjust Stem Morphology and Increase THCA Yield in Medicinal Cannabis." Agronomy 12, no. 4 (March 30, 2022): 852. http://dx.doi.org/10.3390/agronomy12040852.
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The standard two-step methods for grafting horticultural crops involve cultivating the rootstock for a period of time and then connecting the scion. Medicinal Cannabis differs from most annual horticultural crops because it is usually clonally propagated from cuttings. We developed a grafting methodology specifically for medicinal Cannabis, involving a single step, in which a freshly cut scion is grafted to a freshly cut donor stem that will become the rootstock. This study also aimed to uncover a potential role for roots in influencing cannabinoid content. Two varieties with desirable attributes but cultivation limitations were selected to act as scions. The first, “CBD1” was a high CBDA accumulating variety with low biomass yield, and the second, “THC2”, was a high yielding, high THCA accumulating line with inconsistent root development during cloning. Two candidate rootstocks, “THC9r” and “THC8r”, were identified; both were high THCA, low CBDA varieties. Biomass yields in the THC2 scions grafted to THC9 rootstocks (THC9r_2s) were 20% higher than in the non-grafted THC2 plants. In CBD1 grafted plants, the concentrations of CBDA and some minor cannabinoids were significantly different to non-grafted CBD1, but biomass yields were lower. There was a trend towards a higher concentration of THCA in THC9r_2s plants, and when combined with the increased biomass, yield of THCA was increased from 8 g Plant−1 to 13 g Plant−1. Our results present a new grafting method for medicinal Cannabis that improved yield in THC2 and required no additional cultivation time.
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Lillo-Saavedra, Mario, Alberto Espinoza-Salgado, Angel García-Pedrero, Camilo Souto, Eduardo Holzapfel, Consuelo Gonzalo-Martín, Marcelo Somos-Valenzuela, and Diego Rivera. "Early Estimation of Tomato Yield by Decision Tree Ensembles." Agriculture 12, no. 10 (October 10, 2022): 1655. http://dx.doi.org/10.3390/agriculture12101655.
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Crop yield forecasting allows farmers to make decisions in advance to improve farm management and logistics during and after harvest. In this sense, crop yield potential maps are an asset for farmers making decisions about farm management and planning. Although scientific efforts have been made to determine crop yields from in situ information and through remote sensing, most studies are limited to evaluating data from a single date just before harvest. This has a direct negative impact on the quality and predictability of these estimates, especially for logistics. This study proposes a methodology for the early prediction of tomato yield using decision tree ensembles, vegetation spectral indices, and shape factors from images captured by multispectral sensors on board an unmanned aerial vehicle (UAV) during different phenological stages of crop development. With the predictive model developed and based on the collection of training characteristics for 6 weeks before harvest, the tomato yield was estimated for a 0.4 ha plot, obtaining an error rate of 9.28%.
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Atuahene-Amankwa, G., D. E. Falk, A. D. Beattie, and T. E. Michaels. "Early generation testing of common bean (Phaseolus vulgaris L.) populations in sole crop and in maize/bean intercrop." Canadian Journal of Plant Science 78, no. 4 (October 1, 1998): 583–88. http://dx.doi.org/10.4141/p97-041.
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Few plant-breeding studies have examined methodology for improving common bean (Phaseolus vulgaris L.) yields by selecting in an intercrop situation. We hypothesized that early-generation testing would be as useful in a maize (Zea mays L.)/bean intercrop as in sole crop for identifying superior bean populations for yield. F2 to F5 bulks of six selected crosses and their F5-derived advanced lines were evaluated in sole crop or intercrop. The F2 and F5 bulks were evaluated together in a preliminary trial in one location, while the advanced lines were evaluated with the F3s in one location, and with the F4s in two locations. Within sole crop, selection of the best three populations, based on F2 performance, provided 67% of the top advanced lines. The rank correlation between average bulk yield across generations and the average line yield was positive and significant. Within intercrop, selection of the best three populations provided 56% of the top advanced lines. The rank correlation between advanced line yield and bulk yield across generations was positive but not significant. Also, the top three F2 populations selected in sole crop produced 89% of the top advanced lines in intercrop. Advanced line performance showed a positive significant correlation with mean F4 bulk performance for grain yield, 100-seed weight and seeds per pod within sole crop, while positive significant correlation was seen with pods per plant and seeds per pod in intercrop. Results indicate that F2 bulk yields can be used to discard the least promising crosses in both cropping systems. Key words: Early generation testing, Phaseolus vulgaris, intercropping
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Huang, Chengfang, Ning Li, Zhengtao Zhang, Yuan Liu, Xi Chen, Fang Wang, and Qiong Chen. "What Is the Consensus from Multiple Conclusions of Future Crop Yield Changes Affected by Climate Change in China?" International Journal of Environmental Research and Public Health 17, no. 24 (December 10, 2020): 9241. http://dx.doi.org/10.3390/ijerph17249241.
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Many studies have shown that climate change has a significant impact on crop yield in China, while results have varied due to uncertain factors. This study has drawn a highly consistent consensus from the scientific evidence based on numerous existing studies. By a highly rational systematic review methodology, we obtained 737 result samples with the theme of climate change affecting China’s crop yields. Then, we used likelihood scale and trend analysis methods to quantify the consensus level and uncertainty interval of these samples. The results showed that: (i) The crop yield decrease in the second half of the 21st century will be greater than 5% of that in the first half. (ii) The crop most affected by climate change will be maize, with the decreased value exceeding −25% at the end of this century, followed by rice and wheat exceeding −10% and −5%. (iii) The positive impact of CO2 on crop yield will change by nearly 10%. Our conclusions clarify the consensus of the impact of future climate change on China’s crop yield, and this study helps exclude the differences and examine the policies and actions that China has taken and should take in response to climate change.
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Malienkо, А. M., N. E. Borуs, and N. G. Buslaeva. "The questions of methodology of field experiments in agriculture and crop production." Interdepartmental thematic scientific collection "Agriculture" 1, no. 94 (May 22, 2018): 38–44. http://dx.doi.org/10.31073/zem.94.38-44.
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In the article, the results of research on the methodology for conducting studies with corn culture under various methods of sowing and weather conditions. The aim of the research was to establish and evaluate the reliability and high accuracy of the experiment, with a decrease in the area's acreage and taking one plant per repetition. Based on the results of the analysis of biometric parameters and yields, the possibility of sampling from 5 to 108 plants was established statistically and mathematically to establish the accuracy of the experiment. The established parameters of sites in experiments with maize indicate the possibility of obtaining much more information from a smaller unit of area, that is, to increase labor productivity not only with tilled crops. This is the goal of further scientific research with other field crops taking 1 plant of repetitions, observing the conditions of leveling the experimental plot according to the fertility of the soil and sowing seeds with high condition. The data obtained give grounds for continuing research on the minimum space required and the sample in the experiments.
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Levitan, Nathaniel, and Barry Gross. "Utilizing Collocated Crop Growth Model Simulations to Train Agronomic Satellite Retrieval Algorithms." Remote Sensing 10, no. 12 (December 6, 2018): 1968. http://dx.doi.org/10.3390/rs10121968.
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Due to its worldwide coverage and high revisit time, satellite-based remote sensing provides the ability to monitor in-season crop state variables and yields globally. In this study, we presented a novel approach to training agronomic satellite retrieval algorithms by utilizing collocated crop growth model simulations and solar-reflective satellite measurements. Specifically, we showed that bidirectional long short-term memory networks (BLSTMs) can be trained to predict the in-season state variables and yields of Agricultural Production Systems sIMulator (APSIM) maize crop growth model simulations from collocated Moderate Resolution Imaging Spectroradiometer (MODIS) 500-m satellite measurements over the United States Corn Belt at a regional scale. We evaluated the performance of the BLSTMs through both k-fold cross validation and comparison to regional scale ground-truth yields and phenology. Using k-fold cross validation, we showed that three distinct in-season maize state variables (leaf area index, aboveground biomass, and specific leaf area) can be retrieved with cross-validated R2 values ranging from 0.4 to 0.8 for significant portions of the season. Several other plant, soil, and phenological in-season state variables were also evaluated in the study for their retrievability via k-fold cross validation. In addition, by comparing to survey-based United State Department of Agriculture (USDA) ground truth data, we showed that the BLSTMs are able to predict actual county-level yields with R2 values between 0.45 and 0.6 and actual state-level phenological dates (emergence, silking, and maturity) with R2 values between 0.75 and 0.85. We believe that a potential application of this methodology is to develop satellite products to monitor in-season field-scale crop growth on a global scale by reproducing the methodology with field-scale crop growth model simulations (utilizing farmer-recorded field-scale agromanagement data) and collocated high-resolution satellite data (fused with moderate-resolution satellite data).
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Kumhálová, J., P. Novák, and M. Madaras. "Monitoring Oats and Winter Wheat Within-Field Spatial Variability by Satellite Images." Scientia Agriculturae Bohemica 49, no. 2 (June 1, 2018): 127–35. http://dx.doi.org/10.2478/sab-2018-0018.
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Abstract Remote sensing is a methodology using different tools to monitor and predict yields. Spatial variability of crops can be monitored through sampling of vegetation indices derived from the entire crop growth; spatial variability can be used to plan further agronomic management. This paper evaluates the suitability of vegetation indices derived from satellite Landsat and EO-1 data that compare yield, topography wetness index, solar radiation, and meteorological data over a relatively small field (11.5 ha). Time series images were selected from 2006, 2010, and 2014, when oat was grown, and from 2005, 2011 and 2013, when winter wheat was grown. The images were selected from the entire growing season of the crops. An advantage of this method is the availability of these images and their easy application in deriving vegetation indices. It was confirmed that Landsat and EO-1 images in combination with meteorological data are useful for yield component prediction. Spatial resolution of 30 m was sufficient to evaluate a field of 11.5 ha.
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Mamiev, D. M. "Optimized crop rotation schemes for the steppe zone of Republic of North Ossetiа — Аlania." Agrarian science, no. 9 (November 12, 2022): 74–78. http://dx.doi.org/10.32634/0869-8155-2022-362-9-74-78.
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Relevance. The most important condition for the growth of crop production is the correct use of arable land, the improvement of the structure of areas under crops and the optimization of crop rotation schemes. The purpose of the work is to optimize crop rotation schemes for the steppe zone of Republic of North Ossetiа — Аlania. The novelty lies in the fact that for the first time for the natural conditions of the steppe zone of Republic of North Ossetiа —Alania the schemes of soil-protective crop rotations of a new type were optimized in order to increase soil fertility, ecological balance and productivity of agricultural landscapes.Methodology. The research was carried out on the basis of scientific principles and approaches set out in the methodological guidelines: "Agroecological land assessment, design of adaptive-landscape farming systems and agricultural technologies" (2005), "Methodology for designing the basic elements of an adaptive-landscape farming system" (2010) and "Methodology for optimizing crop rotations and patterns of arable land use” (2004).Results. For more efficient use of arable land, increasing crop yields, meeting the needs of farms with crop products and improving soil fertility, improved crop rotations are proposed. In the developed structure, winter crops should account for 42%, corn for grain — 13%, millet — 1%, peas — 3%, soybeans — 4%, sunflower — 8%, potatoes — 0.5%, fodder root crops — 1%, vegetables — 3%, corn for silage — 4%, annual grasses — 1.5%, perennial grasses — 3%, winter rapeseed — 7%, flax — 3%, mustard — 2%, pure fallows — 4%. Optimization of the structure of sown areas and crop rotations developed on its basis will make it possible to grow intermediate crops on 30–50% of arable land, provide farms in the steppe zone of Republic of North Ossetiа — Аlania with high-quality and balanced fodder and a longer operation of the "green conveyor". Optimized crop rotation schemes for the steppe zone of Republic of North Ossetiа — Аlania provide a reduction in degradation processes, increase soil fertility and crop productivity by 12–15%.
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Lalić, B., A. Firanj Sremac, L. Dekić, J. Eitzinger, and D. Perišić. "Seasonal forecasting of green water components and crop yields of winter wheat in Serbia and Austria." Journal of Agricultural Science 156, no. 5 (December 11, 2017): 645–57. http://dx.doi.org/10.1017/s0021859617000788.
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AbstractA probabilistic crop forecast based on ensembles of crop model output (CMO) estimates offers a myriad of possible realizations and probabilistic forecasts of green water components (precipitation and evapotranspiration), crop yields and green water footprints (GWFs) on monthly or seasonal scales. The present paper presents part of the results of an ongoing study related to the application of ensemble forecasting concepts for agricultural production. The methodology used to produce the ensemble CMO using the ensemble seasonal weather forecasts as the crop model input meteorological data without the perturbation of initial soil or crop conditions is presented and tested for accuracy, as are its results. The selected case study is for winter wheat growth in Austria and Serbia during the 2006–2014 period modelled with the SIRIUS crop model. The historical seasonal forecasts for a 6-month period (1 March-31 August) were collected for the period 2006–2014 and were assimilated from the European Centre for Medium-range Weather Forecast and the Meteorological Archival and Retrieval System. The seasonal ensemble forecasting results obtained for winter wheat phenology dynamics, yield and GWF showed a narrow range of estimates. These results indicate that the use of seasonal weather forecasting in agriculture and its applications for probabilistic crop forecasting can optimize field operations (e.g., soil cultivation, plant protection, fertilizing, irrigation) and takes advantage of the predictions of crop development and yield a few weeks or months in advance.
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Munishi, Linus K., Anza A. Lema, and Patrick A. Ndakidemi. "Decline in maize and beans production in the face of climate change at Hai District in Kilimanjaro Region, Tanzania." International Journal of Climate Change Strategies and Management 7, no. 1 (March 16, 2015): 17–26. http://dx.doi.org/10.1108/ijccsm-07-2013-0094.
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Purpose – The purpose of this paper is to show how climatic change in Africa is expected to lead to a higher occurrence of severe droughts in semiarid and arid ecosystems. Understanding how crop productions react to such events is, thus, crucial for addressing future challenges for food security and poverty alleviation. Design/methodology/approach – The authors explored how temperature and rainfall patterns determined maize and beans production in Hai District in Kilimanjaro Region, Tanzania. Findings – Annual food crops were particularly sensitive to the drought and maize and beans yields were lower than perennial crops during the years of drought. The authors also report strong and significant association between maize and beans production with temperature and rainfall patterns. Practical implications – This study highlights how severe droughts can dramatically affect yields of annual crops and suggests that extreme climatic events might act as a major factor affecting agriculture production and food security, delaying or preventing the realization of the Millennium Development Goals. Originality/value – This is the first study that highlights how severe droughts can dramatically affect yields of annual crops in Hai District contributing to other climate studies done elsewhere in Tanzania and the world at large.
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Armstrong, R. D., J. Fitzpatrick, M. A. Rab, M. Abuzar, P. D. Fisher, and G. J. O'Leary. "Advances in precision agriculture in south-eastern Australia. III. Interactions between soil properties and water use help explain spatial variability of crop production in the Victorian Mallee." Crop and Pasture Science 60, no. 9 (2009): 870. http://dx.doi.org/10.1071/cp08349.
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A major barrier to the adoption of precision agriculture in dryland cropping systems is our current inability to reliably predict spatial patterns of grain yield for future crops for a specific paddock. An experiment was undertaken to develop a better understanding of how edaphic and climatic factors interact to influence the spatial variation in the growth, water use, and grain yield of different crops in a single paddock so as to improve predictions of the likely spatial pattern of grain yields in future crops. Changes in a range of crop and soil properties were monitored over 3 consecutive seasons (barley in 2005 and 2007 and lentils in 2006) in the southern section of a 167-ha paddock in the Mallee region of Victoria, which had been classified into 3 different yield (low, moderate, and high) and seasonal variability (stable and variable) zones using normalised difference vegetation index (NDVI) and historic yield maps. The different management zones reflected marked differences in a range of soil properties including both texture in the topsoil and potential chemical-physical constraints in the subsoil (SSCs) to root growth and water use. Dry matter production, grain yield, and quality differed significantly between the yield zones but the relative difference between zones was reduced when supplementary irrigation was applied to barley in 2005, suggesting that some other factor, e.g. nitrogen (N), may have become limiting in that year. There was a strong relationship between crop growth and the use of soil water and nitrate across the management zones, with most water use by the crop occurring in the pre-anthesis/flowering period, but the nature of this relationship appeared to vary with year and/or crop type. In 2006, lentil yield was strongly related to crop establishment, which varied with soil texture and differences in plant-available water. In 2007 the presence of soil water following a good break to the season permitted root growth into the subsoil where there was evidence that SSCs may have adversely affected crop growth. Because of potential residual effects of one crop on another, e.g. through differential N supply and use, we conclude that the utility of the NDVI methodology for developing zone management maps could be improved by using historical records and data for a range of crop types rather than pooling data from a range of seasons.
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Liu, Yong, and Alan P. Ker. "When Less Is More: On the Use of Historical Yield Data with Application to Rating Area Crop Insurance Contracts." Journal of Agricultural and Applied Economics 52, no. 2 (December 18, 2019): 194–203. http://dx.doi.org/10.1017/aae.2019.40.
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AbstractCrop insurance is the cornerstone program of domestic farm policy in most developed countries. Although most countries’ rating methodology corrects for time-varying movements in the first two moments, it is unclear whether using the entire yield series remains appropriate. We use distributional tests and an out-of-sample retain-cede rating game to answer whether governments/insurers should historically trim yields in estimating their premium rates. Despite small sample sizes and the need to estimate tail probabilities, the historical data appear to be sufficiently different such that trimming is justified.
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Gong, Liyun, Miao Yu, Vassilis Cutsuridis, Stefanos Kollias, and Simon Pearson. "A Novel Model Fusion Approach for Greenhouse Crop Yield Prediction." Horticulturae 9, no. 1 (December 20, 2022): 5. http://dx.doi.org/10.3390/horticulturae9010005.
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In this work, we have proposed a novel methodology for greenhouse tomato yield prediction, which is based on a hybrid of an explanatory biophysical model—the Tomgro model, and a machine learning model called CNN-RNN. The Tomgro and CNN-RNN models are calibrated/trained for predicting tomato yields while different fusion approaches (linear, Bayesian, neural network, random forest and gradient boosting) are exploited for fusing the prediction result of individual models for obtaining the final prediction results. The experimental results have shown that the model fusion approach achieves more accurate prediction results than the explanatory biophysical model or the machine learning model. Moreover, out of different model fusion approaches, the neural network one produced the most accurate tomato prediction results, with means and standard deviations of root mean square error (RMSE), r2-coefficient, Nash-Sutcliffe efficiency (NSE) and percent bias (PBIAS) being 17.69 ± 3.47 g/m2, 0.9995 ± 0.0002, 0.9989 ± 0.0004 and 0.1791 ± 0.6837, respectively.
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Buchholz, Matthias, and Oliver Musshoff. "Risk‐efficient portfolio crop choice with amended water and irrigation policies in northern Germany." Agricultural Finance Review 73, no. 2 (July 26, 2013): 373–88. http://dx.doi.org/10.1108/afr-10-2012-0056.
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PurposeIncreasing environmental concerns have placed the need for an enhanced water resources management on the policy agenda. In this context, a restrictive regulation of water withdrawals for irrigation has gained in importance. The purpose of this paper is to investigate how a reduction in water quotas and increased water prices affect risk‐efficient crop choices and the related economic implications for northern German farmers.Design/methodology/approachThe authors apply a whole‐farm risk programming approach to a typical arable farm in northern Germany. By using irrigation field trials, production activities with varying irrigation intensities and inherently incorporated crop yield uncertainty are defined.FindingsIn contrast to increased water prices, a reduction in water quotas leads to higher water savings and lower economic disadvantages for farmers. Due to an adjusted portfolio crop choice, as well as irrigation intensity, the reduction in the expected total gross margin is partially offset.Research limitations/implicationsThis example ensures volumetric water monitoring at the farm level which, however, remains a major pitfall in many other countries. From a methodological perspective, the crop yield distribution choice might affect the findings. Likewise, the consideration of downside risk in an irrigation context appears to be interesting for future research.Originality/valueThis is the first paper to compare the implications of differentiated water quotas and water pricing schemes suggested by the European Water Framework Directive, while taking risk‐efficient crop portfolio considerations into account. This approach facilitates water reallocation not only between crops, but also in terms of the crop‐specific irrigation intensity. Crop yields are based on a unique panel of micro data rather than expert opinions or simulations.
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Bybee-Finley, K., and Matthew Ryan. "Advancing Intercropping Research and Practices in Industrialized Agricultural Landscapes." Agriculture 8, no. 6 (June 8, 2018): 80. http://dx.doi.org/10.3390/agriculture8060080.
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Sustainable intensification calls for agroecological and adaptive management of the agrifood system. Here, we focus on intercropping and how this agroecological practice can be used to increase the sustainability of crop production. Strip, mixed, and relay intercropping can be used to increase crop yields through resource partitioning and facilitation. In addition to achieving greater productivity, diversifying cropping systems through the use of strategic intercrops can increase yield stability, reduce pests, and improve soil health. Several intercropping systems are already implemented in industrialized agricultural landscapes, including mixed intercropping with perennial grasses and legumes as forage and relay intercropping with winter wheat and red clover. Because intercropping can provide numerous benefits, researchers should be clear about their objectives and use appropriate methods so as to not draw spurious conclusions when studying intercrops. In order to advance the practice, experiments that test the effects of intercropping should use standardized methodology, and researchers should report a set of common criteria to facilitate cross-study comparisons. Intercropping with two or more crops appears to be less common with annuals than perennials, which is likely due to differences in the mechanisms responsible for complementarity. One area where intercropping with annuals in industrialized agricultural landscapes has advanced is with cover crops, where private, public, and governmental organizations have harmonized efforts to increase the adoption of cover crop mixtures.
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TORRES-DORANTE, L., R. PAREDES-MELESIO, A. LINK, and J. LAMMEL. "A methodology to develop algorithms that predict nitrogen fertilizer needs in maize based on chlorophyll measurements: a case study in Central Mexico." Journal of Agricultural Science 154, no. 4 (August 17, 2015): 705–19. http://dx.doi.org/10.1017/s002185961500074x.
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SUMMARYIdentifying and applying the optimum fertilizer nitrogen (N) rate is a permanent challenge for farmers. Prediction of fertilizer N requirement, based on crop chlorophyll measurements (CMs), relies on a strong relationship between fertilizer N supply and leaf chlorophyll concentration at a given crop growth stage. A methodological approach is described, aiming to develop an algorithm that uses CM inputs to derive the economically optimum fertilizer N rate for top-dressing, without using a reference plot for data normalization. The method was tested on maize (Zea mays L. cvar Jabali) at experimental and farmer sites in the central (‘Bajío’) region of Mexico over 3 years (2010–12). Increasing fertilizer N supply at planting significantly influenced chlorophyll concentration at the seventh unfolded maize leaf stage (GS 17 on the Zadoks scale). Maize grain yields increased with increasing total fertilizer N supply and fitted quadratic models, which allowed economically optimum fertilizer N rates (Nopt) to be calculated. The Nopt ranged from 160 to 300 kg N/ha and corresponding grain yields ranged from 7·7 to 14 t/ha. Grouped data analysis (sites–years) confirmed a highly significant relationship between the Nopt and the chlorophyll concentration at GS 17, which could be described by a linear model: Nopt = 513·3–0·58 × CM. This model predicted the top-dressing Nopt within a fertilizer N management regime adapted to local maize cropping systems and led to similar grain yields across test sites compared with the same parameters calculated based on grain yield response trials. The current approach is variety-specific, so development of so-called correction factors accounting for variety-related differences in chlorophyll concentration is described. The results demonstrated the feasibility of the proposed algorithms to support decision-making on the optimum fertilizer N rate to apply in maize production systems with one top-dressing application.
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Rankoana, Sejabaledi Agnes. "Indigenous knowledge and innovative practices to cope with impacts of climate change on small-scale farming in Limpopo Province, South Africa." International Journal of Climate Change Strategies and Management 14, no. 2 (February 14, 2022): 180–90. http://dx.doi.org/10.1108/ijccsm-04-2021-0040.
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Purpose This paper aims to describe the indigenous and innovative practices adopted by the small-scale farmers to cope with the impacts of climate change hazards on subsistence farming. Design/methodology/approach The data were collected through focus group discussions with 72 small-scale farmers from a rural community in Limpopo Province, South Africa. The discussions were analysed through verbatim transcripts and content analysis. Findings The study results show the farmers’ understanding of climate change variability and its hazards in the form of rainfall scarcity and excessively increased temperature, which are responsible for a declining production of indigenous crops. It has also been found that in the face of these hazards, the farmers experience low crop yields, which cannot provide the household food requirements. However, the small-scale farmers use a combination of local and innovative knowledge and skills to improve their crop production. They have adopted the indigenous adaptation mechanisms, which include rainfall prediction, preparation of the gardens, change of crops and the planting season to ensure better crop yields. The farmers also adopted innovative adaptation practices such as the use of fertilisers, growing of exotic crops and use of extension officers’ guidance and skills to minimise the risks and maximise the chances of resilient crop production. Research limitations/implications This paper describes the farmers’ ability to use the indigenous and innovative adaptation practices. It is only focused on the farmers’ knowledge and skills other than the extension officers’ skills. Originality/value The adaptation practices reported in the study fall within the adaptation and mitigation systems stipulated in the South African National Climate Change Strategy to assist the small-scale farmers grow and maintain the crops to improve production and minimise the risks, thus ensuring food security under observable harsh climate hazards.
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Ballot, Christiant Simplice Armand, Silla Semballa, Wouyo Atakpama, Tatiana Maryse Yangakola, Arnaud Doubro Bo-Mbi, Didier Blavet, Innocent Zinga, Kpérkouma Wala, Kolman Batawila, and Koffi Akpagana. "Effet De Fumures Minérales Sur Le Rendement Et La Qualité Organoleptique Du Manioc (Manihot Esculenta Crantz) Dans La Zone De Savane Au Centre-Sud De Centrafrique." European Scientific Journal, ESJ 12, no. 6 (February 29, 2016): 185. http://dx.doi.org/10.19044/esj.2016.v12n6p185.
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Aims - The farming intensification in Sub-Saharan Africa induced soil fertility and crop yields depletion such as cassava, a main food and an important source of income of farmers in Central Africa Republic. To transcend the soil fertility depletion and improve cassava yield, LaSBAD has initiate a study focused on the mineral fertilizers of cassava crops. Methods - Four types of fertilizer were tested: the urea (Co(NH2)2), the potassium sulfate (K2SO4), the potassium chloride (KCl) and the triple superphosphate (TSP). The methodology consisted in the physical and chemical analysis of soil, the cultivation, the application of mineral fertilizers and the harvest of cassava after 12 months. Additionally, the evaluation of the organoleptic propriety of raw tubers and cassava balls after the use of fertlizers were achieved throughout semi-strucrured interviews. Results - The contribution of nitrogen and phosphorus has increased very significantly cassava yield from 18.70 to 40.20 t/ha respectively for the control treatment (T0) and the best treatment obtained (T10). A significant interaction was observed between nitrogen (N) and potassium (K) inputs on yields. The increasing doses of potassium as potassium chloride (KCl) had reduced cassava plant growth and yield. According to respondents, the organoleptic proprieties of cassava were remain quite unchangeable by mineral fertilizers. Conclusion - The application of mineral fertilizer improved cassava yield and did not affected the organoleptic quality of the raw tubers and cassava ball. Nevertheless, further studies are needed to prevent soil degradation, namely the potential use of termite nest as natural fertilizers and legumes in association or rotation with other crops.
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Durrer, Ademir, Thiago Gumiere, Maurício Rumenos Guidetti Zagatto, Henrique Petry Feiler, Antonio Marcos Miranda Silva, Rodrigo Henriques Longaresi, Sérgio K. Homma, and Elke J. B. N. Cardoso. "Organic farming practices change the soil bacteria community, improving soil quality and maize crop yields." PeerJ 9 (September 23, 2021): e11985. http://dx.doi.org/10.7717/peerj.11985.
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Background The importance of organic farming has increased through the years to promote food security allied with minimal harm to the ecosystem. Besides the environmental benefits, a recurring problem associated with organic management is the unsatisfactory yield. A possible solution may rely on the soil microbiome, which presents a crucial role in the soil system. Here, we aimed to evaluate the soil bacterial community structure and composition under organic and conventional farming, considering the tropical climate and tropical soil. Methodology Our organic management treatments were composed by composted poultry manure and green manure with Bokashi. Both organic treatments were based on low nitrogen inputs. We evaluated the soil bacterial community composition by high-throughput sequencing of 16S rRNA genes, soil fertility, and soil enzyme activity in two organic farming systems, one conventional and the last transitional from conventional to organic. Results We observed that both organic systems evaluated in this study, have higher yield than the conventional treatment, even in a year with drought conditions. These yield results are highly correlated with changes in soil chemical properties and enzymatic activity. The attributes pH, Ca, P, alkaline phosphatase, and β- glucosidase activity are positively correlated with organic systems, while K and Al are correlated with conventional treatment. Also, our results show in the organic systems the changes in the soil bacteria community, being phyla Acidobacteria, Firmicutes, Nitrospirae, and Rokubacteria the most abundant. These phyla were correlated with soil biochemical changes in the organic systems, helping to increase crop yields. Conclusion Different organic management systems, (the so-called natural and organic management systems, which use distinct organic sources), shift the soil bacterial community composition, implying changes in their functionalities. Also, our results contributed to the identification of target bacterial groups and changes in soil chemical properties and enzymatic activity in a trophic organic farming system, which may contribute to higher crop yields.
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An, Zhichao, Chong Wang, Xiaoqiang Jiao, Zhongliang Kong, Wei Jiang, Dong Zhang, Wenqi Ma, and Fusuo Zhang. "Methodology of Analyzing Maize Density Loss in Smallholder’s Fields and Potential Optimize Approach." Agriculture 11, no. 6 (May 24, 2021): 480. http://dx.doi.org/10.3390/agriculture11060480.
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Increasing plant density is a key measure to close the maize (Zea mays L.) yield gap and ensure food security. However, there is a large plant density difference in the fields sown by agronomists and smallholders. The primary cause of this phenomenon is the lack of an effective methodology to systematically analyze the density loss. To identify the plant density loss processes from experimental plots to smallholder fields, a research methodology was developed in this study involving a farmer survey and measurements in a smallholder field. The results showed that the sowing density difference caused by farmer decision-making and plant density losses caused by mechanical and agronomic factors explained 15.5%, 5.5% and 6.8% of the plant density difference, respectively. Changing smallholder attitudes toward the value of increasing the plant density could help reduce this density loss and increase farm yields by 12.3%. Therefore, this methodology was effective for analyzing the plant density loss, and to clarify the primary causes of sowing density differences and plant density loss. Additionally, it was beneficial to identify the priorities and stakeholders who share responsibility for reducing the density loss. The methodology has wide applicability to address the sowing density differences and plant density loss in other areas to narrow crop yield gaps and ensure food security.
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Inzunza-Ibarra, Marco Antonio, Ignacio Sánchez-Cohen, Sergio Iván Jiménez-Jiménez, Ernesto Alonso Catalán-Valencia, and Mariana de Jesús Marcial-Pablo. "Soil moisture depletion rates on sunflower yield." Ingeniería Agrícola y Biosistemas 144, no. 1 (2022): 51–63. http://dx.doi.org/10.5154/r.inagbi.2021.09.105.
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ntroduction: Optimizing the irrigation water resource is essential due to its scarcity, so it is therefore important to consider efficient crops such as sunflower. Objective: To estimate the sunflower grain yield at different levels of available soil moisture depletion to estimate water use efficiency (WUE) of the crop under different water conditions. Methodology: Seven treatments resulting from four soil moisture levels (40, 60, 60, 80 and 100 % of available soil moisture [ASM] and two periods of sunflower growth (from emergence to the beginning of flowering [first stage] and from flowering to physiological maturity [second stage]). Results: The highest grain yield (5.5 t·ha-1) and WUE of (0.922 kg·m-3) were recorded in the 60-60 % ASM treatment in the first and second stages of sunflower development, and by consuming 62.8 cm of water. Limitations of the study: The results should not be extrapolated to conditions outside the study levels. Originality: To generate research methodologies to quantify, in a more realistic way, the relationship of yields with crop water requirements. Conclusions: The highest WUE in sunflower (0.922 kg·m-3) was recorded when it consumed 31.4 and 28.12 cm of water, with 58.8 and 60.5 % of ASM in the first and second stages, respectively, which was similar to the 60-60 % treatment.
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de Sousa Fragoso, Rui Manuel, and Carlos José de Almeida Noéme. "Economic effects of climate change on the Mediterranean’s irrigated agriculture." Sustainability Accounting, Management and Policy Journal 9, no. 2 (May 8, 2018): 118–38. http://dx.doi.org/10.1108/sampj-07-2017-0078.
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Purpose This paper aims to assess the economic effects of climate change on the Mediterranean’s irrigated agriculture and how the adoption of alternative crop varieties adapted to the expected length of the growing season can be an effective adaptation measure. Design/methodology/approach A case study of two irrigation areas in Southern Portugal is used to assess the response to climate change impacts on crop yields and irrigation requirements, and an agricultural supply model is calibrated using a positive mathematical programming (PMP) approach was developed. Findings Climate change reduces crop yields and causes a slight decrease in irrigation requirements, which could allow an increase in the irrigated area. However, positive impacts on rural areas regarding employment and investment are not expected. The adoption of adaptation measures based on alternative crop varieties, which could maintain crop yields at current levels, increases dramatically the economic value of water and mitigates losses in farm income. Research limitations/implications The impacts on output and input market prices, as well as other biophysical impacts (for instance, CO2 and water availability), are important in understanding the effects of climate change on irrigated agriculture, but they were not considered in this study. While this may be a limitation, it can also be a stimulus for further research. Practical implications This is an empirical paper, whose results contribute to improving knowledge about the effects of climate change on irrigated agriculture in Mediterranean areas, namely, its economic impacts on returns and the use of agricultural resources (land, water, labour and capital). Other practical implications of the paper are associated with the methodological approach, which provides a framework able to deal with the complexity and multidimensional effects of climate change. Social implications The results of the paper provide important information for scientists, politicians and other stakeholders about the design of more effective adaptation measures able to mitigate the effects of climate change. Originality/value Crop yields and irrigation requirements were previously calculated based on data generated by the regional climate models. This is the first time that an application is developed for Portugal. Two distinct profiles of irrigation areas were studied and a large set of crops was considered, which is not common in the existing studies. To specify the PMP approach used to calibrate the agricultural supply model, exogenous crop-specific supply elasticities were estimated through a least square model, which is not common in previous studies.
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Rodríguez Coca, Liuder Isidoro, Marcos Tulio García González, Zuleiqui Gil Unday, Janet Jiménez Hernández, Marcia María Rodríguez Jáuregui, and Yander Fernández Cancio. "Effects of Sodium Salinity on Rice (Oryza sativa L.) Cultivation: A Review." Sustainability 15, no. 3 (January 17, 2023): 1804. http://dx.doi.org/10.3390/su15031804.
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Sodium salinity negatively affects and reduces yields in international agricultural systems. This stress decreases crop growth and development, causing tissue death, flowering abortion, and senescence of the fertilized embryo, and negatively affects enzymatic activity, protein synthesis, among other processes. Rice is a cereal of great international demand for its nutritional properties and its productivity is affected by the presence of salts in agricultural surfaces. The objective of this article is to review the main effects of sodium salinity on morpho-physiological characteristics in rice cultivation. For the design and strategy of the information search, a methodology was followed to compile and summarize the existing studies on the effects of sodium salinity on this crop. The results of this search showed that sodium salts cause poor root growth, chlorosis, leaf curling and leaf scorching in this cereal; it also induces stomatal closure, inhibits photosynthesis, alters cell metabolism, causes oxidative stress in the crop, influences spikelet sterility and grain yield, among other effects.
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Krzyśko, Mirosław, Adriana Derejko, Tomasz Górecki, and Edward Gacek. "Principal component analysis for functional data on grain yield of winter wheat cultivars." Biometrical Letters 50, no. 2 (December 1, 2013): 81–94. http://dx.doi.org/10.2478/bile-2013-0019.
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Summary The aim of this paper is to present a statistical methodology to assess patterns of cultivars' adaptive response to agricultural environments (agroecosystems) on the basis of complete Genotype x Crop Management x Location x Year (GxMxLxY) data obtained from 3-year multi-location twofactor trials conducted within the framework of the Polish post-registration trials (PDOiR), with an illustration of the application and usefulness of this methodology in analyzing winter wheat grain yield. Producing specific varieties for each subregion of a target region, from widely adapted varieties, may exploit positive genotype x location (GL) interactions to increase crop yields. Experiments designed to examine combinations of environment (E), management practices (M) and cultivars (G) also provide evidence of the relative importance of each of these factors for yield improvement. The evidence shows that variation due to E far outweighs the variation of grain yield that can be attributed to M or G, or the interactions between these factors, and between these factors and E (Anderson, 2010). This statistical method involves the use of functional PCA and cluster analysis. A total of 24 cultivars were evaluated over 3 years in 20 environments using randomized incomplete split-block designs with two replications per trial. The methodology proved an efficient tool for the reliable classification of 24 winter wheat cultivars, distinguishing cultivar groups that exhibited homogeneous adaptive response to environments. It enables the identification of cultivars displaying wide or specific adaptation. The remaining cultivars were locally adapted to some testing environments, or some of them were not relatively adapted to the environments because they always yielded substantially below the environmental means. Performing earlier specific selection, or adopting distinct genetic bases for each agro-ecosystem, may further increase the advantage of specific breeding.
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Fukuda, Motohisa, Takashi Okuno, and Shinya Yuki. "Central Object Segmentation by Deep Learning to Continuously Monitor Fruit Growth through RGB Images." Sensors 21, no. 21 (October 21, 2021): 6999. http://dx.doi.org/10.3390/s21216999.
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Monitoring fruit growth is useful when estimating final yields in advance and predicting optimum harvest times. However, observing fruit all day at the farm via RGB images is not an easy task because the light conditions are constantly changing. In this paper, we present CROP (Central Roundish Object Painter). The method involves image segmentation by deep learning, and the architecture of the neural network is a deeper version of U-Net. CROP identifies different types of central roundish fruit in an RGB image in varied light conditions, and creates a corresponding mask. Counting the mask pixels gives the relative two-dimensional size of the fruit, and in this way, time-series images may provide a non-contact means of automatically monitoring fruit growth. Although our measurement unit is different from the traditional one (length), we believe that shape identification potentially provides more information. Interestingly, CROP can have a more general use, working even for some other roundish objects. For this reason, we hope that CROP and our methodology yield big data to promote scientific advancements in horticultural science and other fields.
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Glancey, James L., Edwin Kee, and Tracy Wootten. "Technical Advances and Strategies for Future Developments in Mechanization." HortTechnology 15, no. 3 (January 2005): 486–88. http://dx.doi.org/10.21273/horttech.15.3.0486.
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The vegetable industry is important to our nation as a provider of nutritious and safe food directly consumed by our citizens. It is also critical to a rich and vigorous national agriculture. During the 20th century, engineering innovations coupled with advances in genetics, crop science, and plant protection have allowed the vegetable industry in the U.S. to plant and harvest significantly more land with higher yields while using less labor. Currently, fresh and processed vegetables generate 16% of all U.S. crop income, but from only 2% of the harvested cropland. Yet, many of the challenges in production that existed a century ago still exist for many crops. Perhaps the most significant challenge confronting the industry is labor, often accounting for 50% of all production costs. A case study of the mechanized production system developed for processed tomatoes (Lycopersicon esculentum) confirms that systematic methodology in which the machines, cultural practices, and cultivars are designed together must be adopted to improve the efficiency of current mechanized systems as well as provide profitable alternatives for crops currently hand-harvested. Only with this approach will horticultural crop production remain competitive and economically viable in the U.S.
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Alvarez, Roberto, and Josefina L. De Paepe. "Modelling the effects of stover harvest on soil organic carbon in the Pampas of Argentina." Soil Research 57, no. 3 (2019): 257. http://dx.doi.org/10.1071/sr18262.
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Our objective was to estimate the impact of harvesting stover from agricultural crops to generate biofuels or electricity on the soil organic carbon levels of the Pampean Region in Argentina. For this purpose, a carbon balance methodology based on artificial neural networks was used. Contrasting soil carbon scenarios for different subregions were constructed using a current map of organic carbon and statistical data for crop rotations. Average yields were also estimated using this information. The neural network methodology allowed calculating the annual carbon balance as the difference between estimating the contribution of carbon in crop residues (stover+roots) to the soil and losses as heterotrophic respiration. The model was run for each level of residue input until the soil carbon attained a steady-state. Current rotations were modelled, with predominance of soybean (Glycine max (L.) Merr.) and alternatives that included a greater proportion of wheat (Triticum aestivum L.) and corn (Zea mays L.). Only the stover of these latter two crops was considered to be partially harvested (30% and 60%). The input of carbon to soil was highly dependent on rotation, increasing as the proportion of wheat and corn in the rotation and the level of yield increased. In contrast, stover harvest had little impact on the carbon input due to the low proportion of both crops in the predominant current rotation. By increasing the proportion of cereal crops or the technological level and yield, it was possible to compensate for the effect of stover harvest on soil carbon. The carbon input from residue needed to maintain soil carbon ranged within 2.0–6.0 t C ha–1 year–1 depending on the initial soil carbon level. Retention efficiency of residue carbon was ~30% across different management scenarios. It is not recommended to harvest more than 30% of the stover in order to maintain the level of carbon in the soil organic matter of many Pampean soils.