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Agriculture, Volume 8, Issue 5 (May 2018)

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Cover Story (view full-size image) In Western Siberia, the dryland cropping of spring cereals is typically performed with conventional [...] Read more.
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Open AccessArticle A Novel Machine Learning Method for Estimating Biomass of Grass Swards Using a Photogrammetric Canopy Height Model, Images and Vegetation Indices Captured by a Drone
Agriculture 2018, 8(5), 70; https://doi.org/10.3390/agriculture8050070
Received: 29 March 2018 / Revised: 7 May 2018 / Accepted: 14 May 2018 / Published: 17 May 2018
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Abstract
Silage is the main feed in milk and ruminant meat production in Northern Europe. Novel drone-based remote sensing technology could be utilized in many phases of silage production, but advanced methods of utilizing these data are still developing. Grass swards are harvested three
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Silage is the main feed in milk and ruminant meat production in Northern Europe. Novel drone-based remote sensing technology could be utilized in many phases of silage production, but advanced methods of utilizing these data are still developing. Grass swards are harvested three times in season, and fertilizer is applied similarly three times—once for each harvest when aiming at maximum yields. Timely information of the yield is thus necessary several times in a season for making decisions on harvesting time and rate of fertilizer application. Our objective was to develop and assess a novel machine learning technique for the estimation of canopy height and biomass of grass swards utilizing multispectral photogrammetric camera data. Variation in the studied crop stand was generated using six different nitrogen fertilizer levels and four harvesting dates. The sward was a timothy-meadow fescue mixture dominated by timothy. We extracted various features from the remote sensing data by combining an ultra-high resolution photogrammetric canopy height model (CHM) with a pixel size of 1.0 cm and red, green, blue (RGB) and near-infrared range intensity values and different vegetation indices (VI) extracted from orthophoto mosaics. We compared the performance of multiple linear regression (MLR) and a Random Forest estimator (RF) with different combinations of the CHM, RGB and VI features. The best estimation results with both methods were obtained by combining CHM and VI features and all three feature classes (CHM, RGB and VI features). Both estimators provided equally accurate results. The Pearson correlation coefficients (PCC) and Root Mean Square Errors (RMSEs) of the estimations were at best 0.98 and 0.34 t/ha (12.70%), respectively, for the dry matter yield (DMY) and 0.98 and 1.22 t/ha (11.05%), respectively, for the fresh yield (FY) estimations. Our assessment of the sensitivity of the method with respect to different development stages and different amounts of biomass showed that the use of the machine learning technique that integrated multiple features improved the results in comparison to the simple linear regressions. These results were extremely promising, showing that the proposed multispectral photogrammetric approach can provide accurate biomass estimates of grass swards, and could be developed as a low-cost tool for practical farming applications. Full article
(This article belongs to the Special Issue Remote Sensing in Agricultural System)
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Open AccessFeature PaperArticle Food Energy Availability from Agriculture at the Farm-Level in Southeastern Nigeria: Level, Composition and Determinants
Agriculture 2018, 8(5), 69; https://doi.org/10.3390/agriculture8050069
Received: 3 May 2018 / Revised: 3 May 2018 / Accepted: 11 May 2018 / Published: 15 May 2018
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Abstract
Among the four pillars of ‘food security’ (i.e., ‘food availability’, ‘food accessibility’, ‘food stability’ and ‘food utilization’), ‘food availability (FA)’ underpins the core concept because at the micro-level it is strongly related to the overall availability of food, which is determined by domestic
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Among the four pillars of ‘food security’ (i.e., ‘food availability’, ‘food accessibility’, ‘food stability’ and ‘food utilization’), ‘food availability (FA)’ underpins the core concept because at the micro-level it is strongly related to the overall availability of food, which is determined by domestic food production, food imports and food aid. This paper examines the level of food energy availability (FEA) at the farm level, relationships between farm size and FEA and the determinants of FEA based on a survey of 400 households from Ebonyi and Anambra States of Southeastern Nigeria. FEA in this study refers to Partial Food Energy Availability (PFEA) because it excludes procurement of food from other sources, e.g., purchase from the market, borrow/exchange from others and/or receiving as food aid. Results show that the sample is dominated by small–scale farmers (81% of the total sample) owning land <1.00 ha. The average farm size is small (1.27 ha). Farmers grow multiple food crops. Sixty-eight percent of the farmers produced at least two food crops. Average PFEA is estimated at 4492.78 kcals/capita/day produced from one ha of land area. Approximately 30.92% of the total food produced is set aside for home consumption. Among the food crops, 40.70% of cassava output is set aside for home consumption while most of yam and rice are mainly destined for the market. Inverse farm size–PFEA relationship exists amongst the sampled farmers. The regression results reveal that subsistence pressure, profit motive and share of yam in total output significantly reduces PFEA whereas an increase in the share of cassava in total output significantly increases PFEA. A one percent increase in the share of cassava output will increase PFEA by 0.14%. A one percent increase in subsistence pressure will reduce PFEA by 0.98%. Farmers identified a lack of agricultural extension agents, farm inputs and basic infrastructures as the main constraints adversely affecting food production at the farm-level. Policy implications include investments targeted to improve cassava production and measure to reduce future family size by improved family planning to increase PFEA at the farm-level. Full article
(This article belongs to the Special Issue Energy and Agriculture)
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Open AccessFeature PaperArticle The Genetic Variability of Floral and Agronomic Characteristics of Newly-Bred Cytoplasmic Male Sterile Rice
Agriculture 2018, 8(5), 68; https://doi.org/10.3390/agriculture8050068
Received: 2 April 2018 / Revised: 26 April 2018 / Accepted: 7 May 2018 / Published: 11 May 2018
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Abstract
Male sterility enabled commercialization of heterosis in rice but low seed set remains a constraint on hybrid dissemination. We evaluated 216 F6 maintainer lines for agronomic and floral characteristics in augmented design and selected 15 maintainer lines, which were testcrossed with IR58025A.
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Male sterility enabled commercialization of heterosis in rice but low seed set remains a constraint on hybrid dissemination. We evaluated 216 F6 maintainer lines for agronomic and floral characteristics in augmented design and selected 15 maintainer lines, which were testcrossed with IR58025A. Five backcrosses were conducted to transfer cytoplasmic male sterility (CMS) to select maintainer lines. Newly-bred BC5:6 CMS lines were evaluated for outcrossing rates and agronomic characteristics. There were highly significant differences among 216 F6 maintainer lines for characteristics whose genotypic variance was higher than environmental variance. The phenotypic coefficient of variation was almost the same as the genotypic coefficient of variation, indicating that most phenotypic variation was due to genetics. There were highly significant differences among CMS lines for number of days to 50% flowering and maturity; stigma exertion; panicle exertion, length and weight; spikelet fertility; tillers per plant; plant height; grains per panicle; grain yield per plant; and 1000-grain weight, but not for pollen and panicle sterility during dry and wet seasons. Three CMS lines (CMS3, CMS12, and CMS14), exhibited high outcrossing rates (56.17%, 51.42% and 48.44%, respectively), which had a highly significant, positive correlation with stigma exertion (0.97), spikelet opening angle (0.82), and panicle exertion (0.95). Full article
(This article belongs to the Special Issue Plant Breeding in Agriculture)
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Open AccessArticle The Effect of Supplemental Irrigation on Canopy Temperature Depression, Chlorophyll Content, and Water Use Efficiency in Three Wheat (Triticum aestivum L. and T. durum Desf.) Varieties Grown in Dry Regions of Jordan
Agriculture 2018, 8(5), 67; https://doi.org/10.3390/agriculture8050067
Received: 20 February 2018 / Revised: 16 April 2018 / Accepted: 1 May 2018 / Published: 4 May 2018
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Abstract
One critical challenge facing the world is the need to satisfy the food requirements of the dramatically growing population. Drought stress is one of the main limiting factors in the wheat-producing regions; therefore, wheat yield stability is a major objective of wheat-breeding programs
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One critical challenge facing the world is the need to satisfy the food requirements of the dramatically growing population. Drought stress is one of the main limiting factors in the wheat-producing regions; therefore, wheat yield stability is a major objective of wheat-breeding programs in Jordan, which experience fluctuating climatic conditions in the context of global climate change. In the current study, a two-year field experiment was conducted for exploring the effect of four different water regimes on the yield, yield components, and stability of three wheat (Triticum aestivum L.; T. durum Desf.) Jordanian cultivars as related to Canopy Temperature Depression (CTD), and Chlorophyll Content (measured by Soil-Plant Analysis Development, SPAD). A split plot design was used in this experiment with four replicates. Water treatment was applied as the main factor: with and without supplemental irrigation; 0%, 50%, 75%, and 100% of field capacity were applied. Two durum wheat cultivars and one bread wheat cultivar were split over irrigation treatments as a sub factor. In both growing seasons, supplemental irrigation showed a significant increase in grain yield compared to the rain-fed conditions. This increase in grain yield was due to the significantly positive effect of water availability on yield components. Values of CTD, SPAD, harvest index, and water use efficiency (WUE) were increased significantly with an increase in soil moisture and highly correlated with grain yield. Ammon variety produced the highest grain yield across the four water regimes used in this study. This variety was characterized by the least thermal time to maturity and the highest values of CTD and SPAD. It was concluded that Ammon had the highest stability among the cultivars tested. Furthermore, CTD and SPAD can be used as important selection parameters in breeding programs in Jordan to assist in developing high-yielding genotypes under drought and heat stress conditions. Full article
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Open AccessArticle Salt Tolerance of Six Switchgrass Cultivars
Agriculture 2018, 8(5), 66; https://doi.org/10.3390/agriculture8050066
Received: 5 April 2018 / Revised: 26 April 2018 / Accepted: 28 April 2018 / Published: 29 April 2018
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Abstract
Panicum virgatum L. (switchgrass) cultivars (‘Alamo’, ‘Cimarron’, ‘Kanlow’, ‘NL 94C2-3’, ‘NSL 2009-1’, and ‘NSL 2009-2’) were evaluated for salt tolerance in two separate greenhouse experiments. In experiment (Expt.) 1, switchgrass seedlings were irrigated with a nutrient solution at an electrical conductivity (EC) of
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Panicum virgatum L. (switchgrass) cultivars (‘Alamo’, ‘Cimarron’, ‘Kanlow’, ‘NL 94C2-3’, ‘NSL 2009-1’, and ‘NSL 2009-2’) were evaluated for salt tolerance in two separate greenhouse experiments. In experiment (Expt.) 1, switchgrass seedlings were irrigated with a nutrient solution at an electrical conductivity (EC) of 1.2 dS·m−1 (control) or a saline solution (spiked with salts) at an EC of 5.0 dS·m−1 (EC 5) or 10.0 dS·m−1 (EC 10) for four weeks, once a week. Treatment EC 10 reduced the tiller number by 32% to 37% for all switchgrass cultivars except ‘Kanlow’. All switchgrass cultivars under EC 10 had a significant reduction of 50% to 63% in dry weight. In Expt. 2, switchgrass was seeded in substrates moistened with either a nutrient solution of EC 1.2 dS·m−1 (control) or a saline solution of EC of 5.0, 10.0, or 20.0 dS·m−1 (EC 5, EC 10, or EC 20). Treatment EC 5 did not affect the seedling emergence, regardless of cultivar. Compared to the control, EC 10 reduced the seedling emergence of switchgrass ‘Alamo’, ‘Cimarron’, and ‘NL 94C2-3’ by 44%, 33%, and 82%, respectively. All switchgrass cultivars under EC 10 had a 46% to 88% reduction in the seedling emergence index except ‘NSL 2009-2’. No switchgrass seedlings emerged under EC 20. In summary, high salinity negatively affected switchgrass seedling emergence and growth. Dendrogram and cluster of six switchgrass cultivars indicated that ‘Alamo’ was the most tolerant cultivar, while ‘NSL 2009-2’ was the least tolerant cultivar at both seedling emergence and growth stages. A growth-stage dependent response to salinity was observed for the remaining switchgrass cultivars. ‘NSL 2009-1’ and ‘NL 94C2-3’ were more tolerant to salinity than ‘Cimarron’ and ‘Kanlow’ at the seedling emergence stage; however, ‘Kanlow’ and ‘Cimarron’ were more tolerant to salinity than ‘NSL 2009-1’ and ‘NL 94C2-3’ at the seedling growth stage. Full article
(This article belongs to the Special Issue Response and Tolerance of Agricultural Crops to Salinity Stress)
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Open AccessArticle Multi-Temporal Site-Specific Weed Control of Cirsium arvense (L.) Scop. and Rumex crispus L. in Maize and Sugar Beet Using Unmanned Aerial Vehicle Based Mapping
Agriculture 2018, 8(5), 65; https://doi.org/10.3390/agriculture8050065
Received: 23 March 2018 / Revised: 23 April 2018 / Accepted: 25 April 2018 / Published: 29 April 2018
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Abstract
Sensor-based weed mapping in arable fields is a key element for site-specific herbicide management strategies. In this study, we investigated the generation of application maps based on Unmanned Aerial Vehicle imagery and present a site-specific herbicide application using those maps. Field trials for
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Sensor-based weed mapping in arable fields is a key element for site-specific herbicide management strategies. In this study, we investigated the generation of application maps based on Unmanned Aerial Vehicle imagery and present a site-specific herbicide application using those maps. Field trials for site-specific herbicide applications and multi-temporal image flights were carried out in maize (Zea mays L.) and sugar beet (Beta vulgaris L.) in southern Germany. Real-time kinematic Global Positioning System precision planting information provided the input for determining plant rows in the geocoded aerial images. Vegetation indices combined with generated plant height data were used to detect the patches containing creeping thistle (Cirsium arvense (L.) Scop.) and curled dock (Rumex crispus L.). The computed weed maps showed the presence or absence of the aforementioned weeds on the fields, clustered to 9 m × 9 m grid cells. The precision of the correct classification varied from 96% in maize to 80% in the last sugar beet treatment. The computational underestimation of manual mapped C. arvense and R. cripus patches varied from 1% to 10% respectively. Overall, the developed algorithm performed well, identifying tall perennial weeds for the computation of large-scale herbicide application maps. Full article
(This article belongs to the Special Issue Weed Management)
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Open AccessArticle Relationship of Date Palm Tree Density to Dubas Bug Ommatissus lybicus Infestation in Omani Orchards
Agriculture 2018, 8(5), 64; https://doi.org/10.3390/agriculture8050064
Received: 12 February 2018 / Revised: 21 April 2018 / Accepted: 24 April 2018 / Published: 29 April 2018
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Abstract
Date palm trees, Phoenix dactylifera, are the primary crop in Oman. Most date palm cultivation is under the traditional agricultural system. The plants are usually under dense planting, which makes them prone to pest infestation. The main pest attacking date palm crops
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Date palm trees, Phoenix dactylifera, are the primary crop in Oman. Most date palm cultivation is under the traditional agricultural system. The plants are usually under dense planting, which makes them prone to pest infestation. The main pest attacking date palm crops in Oman is the Dubas bug Ommatissus lybicus. This study integrated modern technology, remote sensing and geographic information systems to determine the number of date palm trees in traditional agriculture locations to find the relationship between date palm tree density and O. lybicus infestation. A local maxima method for tree identification was used to determine the number of date palm trees from high spatial resolution satellite imagery captured by WorldView-3 satellite. Window scale sizes of 3, 5 and 7 m were tested and the results showed that the best window size for date palm trees number detection was 7 m, with an overall estimation accuracy 88.2%. Global regression ordinary least square (OLS) and local geographic weighted regression (GWR) were used to test the relationship between infestation intensity and tree density. The GWR model showed a good positive significant relationship between infestation and tree density in the spring season with R2 = 0.59 and medium positive significant relationship in the autumn season with R2 = 0.30. In contrast, the OLS model results showed a weak positive significant relationship in the spring season with R2 = 0.02, p < 0.05 and insignificant relationship in the autumn season with R2 = 0.01, p > 0.05. The results indicated that there was a geographic effect on the infestation of O. lybicus, which had a greater impact on infestation severity, and that the impact of tree density was higher in the spring season than in autumn season. Full article
(This article belongs to the Special Issue Remote Sensing in Agricultural System)
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Open AccessReview Sustainable Intensification in Dryland Cropping Systems—Perspectives for Adaptions across the Western Siberian Grain Belt
Agriculture 2018, 8(5), 63; https://doi.org/10.3390/agriculture8050063
Received: 2 April 2018 / Revised: 21 April 2018 / Accepted: 26 April 2018 / Published: 29 April 2018
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Abstract
The Western Siberian grain belt is of global significance in terms of agricultural production as well as carbon sequestration and biodiversity preservation. Regional downscaling of general circulation models predict increasing drought risks and water scarcity for this area. Additionally, significant land-use changes took
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The Western Siberian grain belt is of global significance in terms of agricultural production as well as carbon sequestration and biodiversity preservation. Regional downscaling of general circulation models predict increasing drought risks and water scarcity for this area. Additionally, significant land-use changes took place in this region after the dissolution of the USSR and collapse of the state farm system: Land-use intensity in Western Siberia (Russian Federation) continuously decreased on grassland, whilst on cropland the intensity increased through recultivation of abandoned cropland and rising fertilizer inputs since 2003. Together, these changing conditions have led to challenges for sustainable agriculture in this semi-arid environment. For sustainable land management, strategies for adapted crop production systems are needed. In agronomic field trials, the potential of enhanced water use efficiency as contribution to a resilient agricultural system under changing climate conditions was evaluated and related to the common practice and regional research. In participatory on-farm trials, higher average soil water content (+40%) in the top soil layer led to higher grain yield (+0.4 t ha−1) and protein yield (+0.05 t ha−1) under no-till compared to the common practice of conventional tillage. Despite this, regional research still promotes bare fallowing with beneficial effects only in the first harvest after fallow, whereas the potential of no-till was visible each year, even under above-average wet and cool growing conditions. In this case study from the Western Siberian grain belt, we depict a possible pathway to make cereal production in Western Siberia more sustainable. However, the approach of applied sustainable intensification by promoting no-till is related to the negative concomitant effect of increased herbicide applications. Due to the strict rejection of GMOs in Russian agriculture by the federal government, this is a great opportunity to maintain a large, pristine area of over 17 million km2 with a lower risk of glyphosate-dependent cropping systems. Full article
(This article belongs to the Special Issue Sustainable Crop Production Intensification)
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Open AccessFeature PaperArticle Exploring the Relationships between Greenhouse Gas Emissions, Yields, and Soil Properties in Cropping Systems
Agriculture 2018, 8(5), 62; https://doi.org/10.3390/agriculture8050062
Received: 9 February 2017 / Revised: 2 April 2018 / Accepted: 24 April 2018 / Published: 26 April 2018
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Abstract
Relationships between greenhouse gas emissions, yields, and soil properties are not well known. Utilizing two datasets from long-term cropping systems in Illinois, USA, our we aim to address these knowledge gaps. The objective of this study was to explore the relationships between the
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Relationships between greenhouse gas emissions, yields, and soil properties are not well known. Utilizing two datasets from long-term cropping systems in Illinois, USA, our we aim to address these knowledge gaps. The objective of this study was to explore the relationships between the physical and chemical properties and greenhouse gas (GHG) emissions of soil, and cash crop yields over a four-year time-period and following 15 years of treatment implementation in Illinois, USA. The experimental layout was a split-plot arrangement involving rotation and tillage treatments in a randomized complete block design with four replications. The studied crop rotations were continuous corn [Zea mays L.] (CCC), corn-soybean [Glycine max (L.) Merr.] (CS), continuous soybean (SSS), and corn-soybean-wheat [Triticum aestivum L.] (CSW), with each phase being present for every year. The tillage options were chisel tillage (T) and no-tillage (NT). We used an array of multivariate approaches to analyze both of our datasets that included 31 soil properties, GHG emissions (N2O, CO2, and CH4) and cash crop yields. The results from our analyses indicate that N2O emissions are associated with a low soil pH, an increased Al concentration, the presence of soil nitrate throughout the growing season, an increase in plant available water (PAW) and an increased soil C concentration. Likewise, soil CO2 respiration was correlated with low pH, elevated Al concentrations, low Ca, increased PAW, higher levels of microbial biomass carbon (MBC), and lower water aggregate stability (WAS). Emissions of CH4 were associated with increased levels of MBC. Lastly, the yield index (YdI) was correlated with lower levels of soil Ca and available P and lower values of WAS. The association between high YdI and lower WAS can be attributed to tillage, as tillage lowers WAS, but increases yields in highly productive cropping systems in the Midwest. Full article
(This article belongs to the Special Issue Sustainable Crop Production Intensification)
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