Individual Crops Research Articles

Вплив зрошення мінералізованими водами на процеси засолення ґрунтів Південно-Бузької та Кам’янської зрошувальних систем

In Ukraine, irrigation development is hampered by a shortage of high-quality irrigation water. Irrigation with mineralized irrigation water causes soil salinization, which negatively affects crop productivity, soil quality, and surface water conditions. The purpose of this article is to analyze the impact of irrigation water of different mineralization on soil salinity in the Southern Bug and Kamianske irrigation systems. The field and laboratory studies used the methods and criteria of the salinity laboratory of the US Department of Agriculture and the Food and Agriculture Organization. The studies showed that irrigation is carried out with water that has an average risk of soil salinity (ECw = 0.7-3.0 dS/m). At the same time, there is some spatial differentiation in the quality of irrigation water. The lands of the Southern Bug irrigation system are irrigated with water with an electrical conductivity of 0.90-1.07 dS/m, and the lands of the Kamianska irrigation system are irrigated with water with an electrical conductivity of 1.86-2.65 dS/m. The mineralization of irrigation water determines the degree of soil salinity. While the soils of the Southern Bug irrigation system are not saline (ECe is less than 2.0 dS/m), the soils of the Kamianske irrigation system have a significant accumulation of salts (ECe reaches 5.5 dS/m). According to the Food and Agriculture Organization classification, such soils are classified as saline. Analysis of the salinity tolerance of individual crops showed that any crops of any salt tolerance can be grown on the irrigated lands of the Southern Bug irrigation system. And on the lands of the Kamianske irrigation system, only moderately sensitive and least sensitive crops to soil salinity can be grown without loss of yield. The conducted research allows us to adjust the structure of sown areas and the structure of crop rotations in terms of salt tolerance of certain crops.

Benefits and pitfalls of irrigation timing and water amounts derived from satellite soil moisture

Despite the key role of irrigation in the Earth system, we lack fundamental information regarding the distribution of irrigated fields, irrigation timing and the amount of water utilized. In the past years, the SM_Delta and SM_Inversion approaches have been independently developed to provide estimates of irrigation timing and water amounts based on satellite soil moisture data. The SM_Delta approach retrieves irrigation from variations in soil moisture between an individual pixel and the surrounding rainfed area, while the SM_Inversion approach estimates the total amount of water entering the soil, then irrigation is derived by subtracting precipitation. In this study, we perform a comprehensive assessment of irrigation estimates from the SM_Delta and SM_Inversion algorithms based on Sentinel-1 surface soil moisture retrievals at 1 km resolution. Our analysis focuses on the Ebro basin, an irrigated region in Spain covering 83000 km2, during the period 2017–2019. We assess the ability of the two methods to discriminate irrigated and rainfed pixels, then we quantify the agreement of irrigation timing and water volumes with reference irrigation data. An inter-comparison between estimates from the SM_Delta and SM_Inversion methods is carried out considering both temporal and spatial features, i.e., monthly irrigation peaks and spatial irrigation patterns. Finally, we explore two potential applications of satellite-derived irrigation estimates: attributing irrigation water volumes to specific irrigation systems and to individual crops. We observe that both methods erroneously retrieve irrigation over rainfed pixels, and are therefore not suitable to map irrigated and rainfed fields. However, when auxiliary information on irrigated fields is available, we find a satisfactory agreement between district-scale reference data and satellite-retrieved irrigation, using both the SM_Delta and SM_Inversion approaches (Pearson R equal to 0.67 and 0.71, bias equal to −4.99 and −4.75 mm/15 days, respectively). When aggregated in space or time, the irrigation estimates exhibit coherent temporal dynamics and spatial patterns. For instance, estimates from both SM_Delta and SM_Inversion capture the delayed irrigation that occurred in 2018 due to wetter than usual conditions in spring. However, at the pixel-scale, limited consistency exists between irrigation estimates from the two methods due to different assumptions and parameterizations, e.g., use of constant vs pixel-specific soil water capacity (in the SM_Delta and SM_Inversion, respectively). Overall, the study demonstrates the reliability of irrigation estimates derived from the SM_Delta and SM_Inversion approaches, especially when shifting from small spatial and short temporal scales (pixel level, sub-weekly) to larger and longer scales (district level, seasonal). Hence, satellite-based irrigation estimates could inform water resources managers and basin authorities, as well as serve the modelling community by providing reliable information on the timing and the amounts of water employed at the basin level.

INFLUENCE OF POPULUS BALZAMIFERA BUDNEY EXTRACT ON THE GROWTH AND DEVELOPMENT OF CABBAGE

In recent years, there has been a persistent search for new methods and technologies for cultivating individual crops in order to increase their productivity and improve product quality. New methods of pre-sowing seed treatment with biological stimulants are increasingly being introduced, increasing not only crop yields, but also changing (increasingly) the content of important nutrients (proteins, fats, carbohydrates, oils, and others). Despite the rapid development of chemistry and the growth in the number of new highly effective drugs of biotechnological and synthetic origin, plants continue to occupy a significant place as a growth stimulator of agricultural crops.
 The aim of the work is to evaluate the effect of poplar extract on the growth and development of white cabbage.
 Tasks: to investigate the qualitative composition of the aqueous extract of poplar buds; to establish the growth-stimulating activity of the extract of balsamic poplar buds in relation to the seeds of white cabbage.
 Methodology and scientific approaches: in the course of the work, experimental studies were carried out on the extraction of natural compounds of balsam poplar buds, evaluating the effectiveness of the balsam poplar bud extract on the growth and development of white cabbage.
 Results and conclusions: in the composition of the aqueous extract of poplar buds, such classes of compounds as flavonoids, tannins, coumarins, saponins, amino acids, phenolic acids, polysaccharides were identified; the use of poplar bud extract had a significant impact on morphogenesis, physiological and biochemical parameters, and the productivity of white cabbage of the "Gift" variety.

Germination and outgrowth of Bacillus mycoides KBAB4 spores are impacted by environmental pH, quantitatively analyzed at single cell level with sporetracker

Quantifying spore germination and outgrowth heterogeneity is challenging. Single cell level analysis should provide supplementary knowledge regarding the impact of unfavorable conditions on germination and outgrowth dynamics. This work aimed to quantify the impact of pH on spore germination and outgrowth, investigating the behavior of individual spore crops, produced under optimal and suboptimal conditions. Bacillus mycoides (formerly B. weihenstephanensis) KBAB4 spores, produced at pH 7.4 and at pH 5.5 were incubated at different pH values, from pH 5.2 to 7.4. The spores were monitored by microscopy live imaging, in controlled conditions, at 30 °C. The images were analyzed using SporeTracker, to determine the state of single cells. The impact of pH on germination and outgrowth times and rates was estimated and the correlation between these parameters was quantified. The correlation between germination and outgrowth times was significantly higher at low pH. These results suggest that an environmental pressure highlights the heterogeneity of spore germination and outgrowth within a spore population. Results were consistent with previous observations at population level, now confirmed and extended to single cell level. Therefore, single cell level analyses can be used to quantify the heterogeneity of spore populations, which is of interest in order to control the development of spore-forming bacteria, responsible for food safety issues.

The contribution of the Gaza envelope region to Israel's food security.

Abstract Armed conflicts can have severe implications on food security by displacing farmers, destroying farms and agricultural infrastructure, disrupting supply chains, and limiting the ability of governance to control the highly volatile food markets. Here, we aim to understand the impact of the current Israel-Hamas War on local food production in Israel. Our analysis focused on challenges in food production in the Gaza Envelope region, the region in Israel that borders the Gaza Strip that has been significantly affected by the conflict. Our goals were to understand the agricultural significance of the region- both regarding crop diversity and the risks posed to individual crops due to the conflict, and to assess the nutritional contribution of the region's agricultural output. We found that the Gaza Envelope region is crucial for the propagation of several key crops, notably cherry tomatoes, radishes, and sweet potatoes. Furthermore, we identified 16 crops whose local production is particularly vulnerable due to their proximity to conflict, including radishes and potatoes. The nutritional output of the edible crops of the Gaza Envelope region is extensive and produces over 50% of the annual energetic requirements of the Israeli population. Beyond the caloric contribution, the produce of the region is nutrient-dense, exceeding 100% of the annual Dietary Reference Intakes for the Israeli population of 12 vital nutrients. These 12 nutrients include dietary fiber, six vitamins such as Vitamin A, Vitamin C, and Vitamin K, and five minerals including Potassium and Iron. Based on these findings, we suggest that the Israel-Hamas War also has detrimental ramifications on local food production in Israel.

Использование процесса проращивания для повышения антиоксидантных свойств сырья

Abstract. The development of technology for controlled germination of grains and raw ingredients based on them, as well as the adaptation of methods and approaches for each individual crop, is one of the promising areas in the development of a line of food products that meets all the requirements of the modern population. The novelty of the research lies in the use of short-term temperature exposure as a disinfecting step before the process of germination of barley grain and the formation of an evidence base that this type of exposure does not have a negative impact on the remaining technological stages. The purpose of the study was to study the possibility of using barley grain in germination technology for the further production of raw ingredients with increased antioxidant properties. Research methods. The objects of study were barley grain (Hordeum vulgare L.), harvested from 2019 to 2022. The production of sprouted grain included separate technological stages: disinfection (short-term exposure to high temperatures), soaking (in water at 20 ± 2 °C for 20 hours) and germination (in a chamber with a temperature of 22 ± 2 °C and air humidity 95 ± 3 % , with a time range of 12–48 hours). Standard methods were used to evaluate: organoleptic indicators, humidity, nature, mass fraction of protein, ability and energy of germination, as well as microbiological indicators. To select the optimal duration of germination, the total content of flavonoids, polyphenols and antioxidant activity was controlled. Results. As a result of the studies, it was established that the use of short-term thermal exposure at a temperature of 190 °C and a duration of 10 s allows minimizing the risks of activating the development of the present microflora of barley grain. Using the proposed approaches, when carrying out the technological stage of germination, it was possible to identify the optimal duration of the process – 36 hours, which leads to an increase in total antioxidant activity by an average of 46 %, flavonoid content by 560 % and polyphenols by 145 %.

Water footprints and crop water use of 175 individual crops for 1990–2019 simulated with a global crop model

The water footprint of a crop (WF) is a common metric for assessing agricultural water consumption and productivity. To provide an update and methodological enhancement of existing WF datasets, we apply a global process-based crop model to quantify consumptive WFs of 175 individual crops at a 5 arcminute resolution over the 1990–2019 period. This model simulates the daily crop growth and vertical water balance considering local environmental conditions, crop characteristics, and farm management. We partition WFs into green (water from precipitation) and blue (from irrigation or capillary rise), and differentiate between rainfed and irrigated production systems. The outputs include gridded datasets and national averages for unit water footprints (expressed in m3 t−1 yr−1), water footprints of production (m3 yr−1), and crop water use (mm yr−1). We compare our estimates to other global studies covering different historical periods and methodological approaches. Provided outputs can offer insights into spatial and temporal patterns of agricultural water consumption and serve as inputs for further virtual water trade studies, life cycle and water footprint assessments.

Water Framework Directive micropollutant monitoring mirrors catchment land use: Importance of agricultural and urban sources revealed

River monitoring programs worldwide consistently unveil micropollutant concentrations (pesticide, pharmaceuticals, and industrial chemicals) exceeding regulatory quality targets with deteriorating effects on aquatic communities. However, both the composition and individual concentrations of micropollutants are likely to vary with the catchment land use, in particular regarding urban and agricultural area as the primary sources of micropollutants. In this study, we used a dataset of 109 governmental monitoring sites with micropollutants monitored across the Federal State of North Rhine-Westphalia, Germany, to investigate the relationship between high-resolution catchment land use (distinguishing urban, forested and grassland area as well as 22 different agricultural crop types) and 39 micropollutants using Linear Mixed Models (LMMs). Ecotoxicological risks were indicated for mixtures of pharmaceutical and industrial chemicals for 100 % and for pesticides for 55 % of the sites. The proportion of urban area in the catchment was positively related with concentrations of most pharmaceuticals and industrial chemicals (R2 up to 0.54), whereas the proportions of grassland and forested areas generally showed negative relations. Cropland overall showed weak positive relationships with micropollutant concentrations (R2 up to 0.29). Individual crop types, particularly vegetables and permanent crops, showed higher relations (R2 up to 0.46). The findings suggest that crop type-specific pesticide applications are mirrored in the detected micropollutant concentrations. This highlights the need for high-resolution spatial land use to investigate the magnitude and dynamics of micropollutant exposure and relevant pollution sources, which would remain undetected with highly aggregated land use classifications. Moreover, the findings imply the need for tailored management measures to reduce micropollutant concentrations from different sources and their related ecological effects. Urban point sources, could be managed by advanced wastewater treatment. The reduction of diffuse pollution from agricultural land uses requires additional measures, to prevent pesticides from entering the environment and exceeding regulatory quality targets.

A global meta-analysis of yield-scaled N2 O emissions and its mitigation efforts for maize, wheat, and rice.

Maintaining or even increasing crop yields while reducing nitrous oxide (N2 O) emissions is necessary to reconcile food security and climate change, while the metric of yield-scaled N2 O emission (i.e., N2 O emissions per unit of crop yield) is at present poorly understood. Here we conducted a global meta-analysis with more than 6000 observations to explore the variation patterns and controlling factors of yield-scaled N2 O emissions for maize, wheat and rice and associated potential mitigation options. Our results showed that the average yield-scaled N2 O emissions across all available data followed the order wheat (322 g N Mg-1 , with the 95% confidence interval [CI]: 301-346) > maize (211 g N Mg-1 , CI: 198-225) > rice (153 g N Mg-1 , CI: 144-163). Yield-scaled N2 O emissions for individual crops were generally higher in tropical or subtropical zones than in temperate zones, and also showed a trend towards lower intensities from low to high latitudes. This global variation was better explained by climatic and edaphic factors than by N fertilizer management, while their combined effect predicted more than 70% of the variance. Furthermore, our analysis showed a significant decrease in yield-scaled N2 O emissions with increasing N use efficiency or in N2 O emissions for production systems with cereal yields >10 Mg ha-1 (maize), 6.6 Mg ha-1 (wheat) or 6.8 Mg ha-1 (rice), respectively. This highlights that N use efficiency indicators can be used as valuable proxies for reconciling trade-offs between crop production and N2 O mitigation. For all three major staple crops, reducing N fertilization by up to 30%, optimizing the timing and placement of fertilizer application or using enhanced-efficiency N fertilizers significantly reduced yield-scaled N2 O emissions at similar or even higher cereal yields. Our data-driven assessment provides some key guidance for developing effective and targeted mitigation and adaptation strategies for the sustainable intensification of cereal production.

IMPLICATIONS OF NEURAL NETWORK AS A DECISION-MAKING TOOL IN MANAGING KAZAKHSTAN’S AGRICULTURAL ECONOMY

This study investigates the application of Artificial Neural Networks (ANN) in forecasting agricultural yields in Kazakhstan, highlighting its implications for economic management and policy-making. Utilizing data from the Bureau of National Statistics of the Republic of Kazakhstan (2000-2023), the research develops two ANN models using the Neural Net Fitting library in MATLAB. The first model predicts the total gross yield of main agricultural crops, while the second forecasts the share of individual crops, including cereals, oilseeds, potatoes, vegetables, melons, and sugar beets. The models demonstrate high accuracy, with the total gross yield model achieving an R-squared value of 0.98 and the individual crop model showing an R value of 0.99375. These results indicate a strong predictive capability, essential for practical agricultural and economic planning. The study extends previous research by incorporating a comprehensive range of climatic and agrochemical data, enhancing the precision of yield predictions. The findings have significant implications for Kazakhstan's economy. Accurate yield predictions can optimize agricultural planning, contribute to food security, and inform policy decisions. The successful application of ANN models showcases the potential of AI and machine learning in agriculture, suggesting a pathway towards more efficient, sustainable farming practices and improved quality management systems.

Intensive processing reduces quality of grains: a triangulation of three assessment methods

ABSTRACT The presented study evaluated different manufacturing procedures of breakfast cereals in view of their ability to preserve the natural quality of the raw material, as this is recommended by the EU Regulation 2018/848 for organic farming and the Demeter guidelines. From four crops (spelt, buckwheat, rice and quinoa), different products were manufactured: flakes, roasted flakes from cooked grains, extrudates from dough, and puffed grains. The samples (in total n = 18) were analysed for positive (thiamine, folate, lysine) and negative (acrylamide) health-related constituents, delayed fluorescence by Fluorescence Excitation Spectroscopy (FES) and food-induced emotions by the EmpathicFoodTest (EFT). The results showed that the four crops reacted similarly to the manufacturing processes, but the sensitivity of the crop was different for the health-related constituents, with spelt and rice showing greater sensitivity with respect to acrylamide formation and thiamine loss, than buckwheat and quinoa (though differences at the individual crop level were not statistically verified). The different assessment methods led to largely consistent evaluations of the processing methods: after extrusion, puffing and roasting flakes, significant quality losses were observed. Depending on the parameter of the test method, the classification of the degradation due to processing differed slightly: lysine degradation was significantly higher in puffed products (especially in rice and spelt) and acrylamide concentration was high (> 200 µg kg-1) in roasted and puffed spelt. The methods EFT and FES rated extruded samples particularly unfavourably. The least harmful process in all parameters was the production of flakes.

Multi-Species Weed and Crop Classification Comparison Using Five Different Deep Learning Network Architectures

Highlights ConvNeXt model was applied for weed and crop species classification, a first in this field. Five CNN architectures were utilized to classify six weed species and eight crop species. CNN architectures with millions of parameters were trained for improved performance. All CNN models showcased impressive performance, except for Densenet. Abstract. The detection of individual weed and crop species from RGB images is a challenging task that becomes even more difficult as the number of species increases. This is because similarities in the phenotypic traits of weeds and crops make it difficult to accurately distinguish one species from another. In this study, five deep learning Convolutional Neural Networks (CNNs) were employed to classify six weed and eight crop species from North Dakota and assess the performance of each model for specific species from a single image. An automated data acquisition system was utilized to collect and process RGB images twice in a greenhouse setting. The first set of data was used to train the CNN models by updating all of its convolutional layers, while the second set was used to evaluate the performance of the models. The results showed that all CNN architectures, except Densenet, demonstrated strong performance, with macro average f1-scores (measurement of model accuracy) ranging from 0.85 to 0.87 and weighted average f1-scores ranging from 0.87 to 0.88. The presence of three weed classes—palmer amaranth, redroot pigweed, and waterhemp, all of which share similar phenotypic traits—negatively affected the model's performance. In conclusion, the results of this study indicate that CNN architectures hold great potential for classifying weed and crop species in North Dakota, with the exception of situations where plants have similar visible characteristics. Keywords: Deep learning, Precision agriculture, Weed and crop classification.

Applying machine learning to soil analysis for accurate farming

A crucial component of agriculture is soil. Soil analysis is critical for optimizing agricultural practices and ensuring sustainable crop production. Traditional methods are often time-consuming and labor intensive, limiting their scalability and real-time applicability. The application of machine learning techniques in soil nutrient analysis has emerged as a promising solution. There is a lot of complicated soil data, but algorithms that use machine learning can handle it all, enabling accurate prediction and assessment of soil nutrient content. Integration with remote sensing technologies enhances the capabilities of soil nutrient analysis, allowing for rapid assessment at different scales. Machine learning facilitates personalized recommendations for fertilizer application, irrigation strategies, and soil amendments, tailored to the specific needs of learning and adaptive individual fields or crops. The continuous capabilities of machine learning models ensure up-to-date nutrient management recommendations. Challenges include the availability of representative interpretability of models. Nevertheless, the integration of machine learning in soil nutrient analysis offers improved resource utilization, enhanced crop productivity, and sustainable soil management practices. Ongoing research and collaboration with domain experts will further advance the application of machine learning in this field.

A study on microclimate monitoring and control inside greenhouse using fans automation

<span>Efficient microclimate management is crucial in enhancing crop yields in greenhouses. Factors like temperature, relative humidity, and ultraviolet (UV) index significantly impact crop quality. The absence of adequate ventilation mechanisms in greenhouses presents a challenge for temperature regulation. This study proposes a solution for tropical greenhouses by designing a system that automatically activates fans when temperatures rise above 30 °C. This system regulates temperature and cultivates optimal growth conditions for crops. It is supported by a web page that enables monitoring and adjustment of microclimate data. To accommodate individual crop requirements, the minimum temperature threshold for fan activation can be modified, enhancing the system's adaptability. The impact of the UV index on greenhouse temperature is also considered. The automation system decreases the temperature by around ±3 °C when the UV index hits 10. Nonetheless, its cooling impact wanes beyond the UV index of 10. A greenhouse automation system, equipped with fans and internet access, proves quite useful for agricultural environment management. It tackles the temperature control issue and offers varied solutions.</span>

No relationship between outputs of simple humus balance calculators (VDLUFA and STAND) and soil organic carbon trends

AbstractSimple humus balance calculators were developed for farmers and consultants to determine the best crop rotation and amount of organic fertilizer required to improve soil quality and prevent nutrient leaching in croplands. Although the potential of these tools to infer the impact of different agricultural practices on soil organic carbon (SOC) dynamics in croplands is not well studied, they have been integrated in several farm‐level climate or environmental impact assessment calculators. Here we examine the correlation between humus balance values estimated with two different tools developed in Germany/Central Europe and observed changes in SOC content at 14 long‐term sites in Switzerland. The first tool was developed by the Association of German Agricultural Investigation and Research Institutes and is referred to as the VDLUFA. The humus balance calculator STAND is a descendent of the VDLUFA that accounts for pedoclimatic factors in Central Europe. Crop rotations were distinguished based on cultivation practice, whereby those with mixed fertilization were supplied with mineral fertilizer alone and in combination with organic materials, while those with organic fertilization include unfertilized and organic fertilizer treatments. An analysis of 133 short‐term observations (i.e. individual crop rotations of five and 6‐year duration) and 26 long‐term observations (i.e. several crop rotations with a total duration of ≥10 years) showed that humus balance values (kg C ha−1 year−1) of short‐term crop rotations were not or only poorly correlated with the observed change in SOC content (%) (R2 = 0.06 in STAND and R2 = 0.05 in VDLUFA for crop rotations with organic fertilization, and R2 < 0.01 for crop rotations with mixed fertilization). The correlation did not improve when the humus balance values of long‐term observations with mixed fertilization were compared with decadal SOC development (R2 = 0.04 for STAND and R2 = 0.06 for the VDLUFA). Stronger correlations were found only for long‐term observations with organic fertilization (R2 = 0.68 for STAND and R2 = 0.64 for the VDLUFA). These findings underline that while the studied humus balance calculators are able to distinguish the effect of different fertilizers (organic vs. mineral) on a farm's humus supply on the longer term, neither are suited for predicting SOC trends over single crop rotations. Although this study was carried out in Switzerland, the results should apply to any region with temperate climate and similar soil properties.

CROP ATTRIBUTES, FARM DECISIONS CROP SPECIFIC POLICIES IN THE CONTEXT OF SUSTAINABILITY OF PRODUCTION IN ETHIOPIA

Ethiopia’s government has created a number of policies and programs to address the urgent issue of rising food prices. Extension and regulation initiatives aiming at influencing the production and marketing are frequently used in conjunction with them. However, lack of indices on relative importance of individual crops results poor policy outcomes. Analyzing household decision dynamics and how household decisions respond to policies is crucial to reduce persistent complaints from recipients and avoid negative policy outcomes. For this study, data from 392 randomly chosen households of Kewot woreda was used. Sorghum found with higher calorie index per profit while mung bean was found higher profit index. Crop diversification found inefficient that arise from profitability differences and return to scale. Additionally multiple objectives could be met using tradeoffs among different crop benefits. This study also summarizes major findings from previous crop-related policies and linked them to specific crop characteristics. It is advised that policies be adjusted to reflect the value, character, and utility of crops.

Evolving patterns of agricultural production space in China: A network-based approach

The agricultural production space, as where and how much each agricultural product grows, plays a vital role in meeting the increasing and diverse food demands. Previous studies on agricultural production patterns have predominantly centered on individual or specific crop types, using methods such as remote sensing or statistical metrological analysis. In this study, we characterize the agricultural production space (APS) by bipartite network connecting agricultural products and provinces, to reveal the relatedness between diverse agricultural products and the spatiotemporal characteristic of provincial production capabilities in China. The results show that core products are cereal, pork, melon, and pome fruit; meanwhile the milk, grape, and fiber crop show an upward trend in centrality, which is in line with diet structure changes in China over the past decades. The little changes in community components and structures of agricultural products and provinces reveal that agricultural production patterns in China are relatively stable. Additionally, identified provincial communities closely resemble China’s agricultural natural zones. Furthermore, the observed growth in production capabilities in North and Northeast China implies their potential focus areas for future agricultural production. Despite the superior production capabilities of southern provinces, recent years have witnessed a notable decline, warranting special attentions. The findings provide a comprehensive perspective for understanding the complex relationship of agricultural products’ relatedness, production capabilities and production patterns, which serve as a reference for the agricultural spatial optimization and agricultural sustainable development.

Informing the operation of intelligent automated intra-row weeding machines in direct-sown sugar beet (Beta vulgaris L.): Crop effects of hoeing and flaming across early growth stages, tool working distances, and intensities

The effective management of intra-row weeds is necessary to preserve crop yields in slow-to-establish direct-seeded crops like sugar beet, and suitable non-chemical control options are limited. However, the advent of automated intelligent intra-row weeding machines has provided a promising solution. These machines identify individual crop plants and selectively hoe or flame weed the between-crop zone, leaving the intra-row close-to-crop zone untreated. To characterize crop tolerance to intra-row weeding and describe optimal tool working intensities and distances, five pot experiments were performed using sugar beet as the test crop. From cotyledon through the four-leaf stage, hoeing could be implemented as close as 1 cm to the center of sugar beet plants before adverse crop effects were observed. Based on crop injury measured, direct flaming of sugar beet at the cotyledon stage is not advised at any intensity, although flaming tolerance was observed to increase with crop growth stage. Direct flaming did not affect sugar beets when the propane dose was ≤0.74 kg km−1 at the two-leaf stage, ≤1.49 kg km−1 at the four-leaf stage, and ≤5.95 kg km−1 at the six-leaf stage. Sugar beets at the two-leaf growth stage were not damaged by flaming at distances ≥1.5 cm at a propane dose of 3.72 kg km−1. Field experiments will be necessary to confirm these operational guidelines for next-generation automated intelligent weeders.

Maize-Brachiaria grass intercropping: A meta-analysis of major productivity drivers in Brazil

Context or problemIncreasing crop intensification while enhancing soil conservation is an important aspect of global food security. Intercropping (syn. interseeding) between cash crops and tropical forages improves soil cover, prevents soil erosion, and enhances land use via crop and livestock integration. In Brazil, intercropping between maize (Zea mays L.) and Brachiaria grass (Urochloa spp.) is the most commonly adopted system, with opportunities for expansion across tropical and subtropical landscapes worldwide. Nonetheless, there is little consensus on the effects of intercropping on the performance of each individual crop species. Objective or research questionA meta-analysis was conducted to quantify the effect of intercropping on maize grain yield and Brachiaria grass biomass production and to understand the impact of environmental and management factors on intercropping performance. MethodsA literature search was conducted to retrieve peer-reviewed studies carried out on this intercropping system under field conditions. Manuscripts were scrutinized and included in this meta-analysis when they met the following criteria: i) data on maize grain yield and Brachiaria grass biomass under intercropping were reported, ii) inclusion of a control treatment (monocropping) for at least one species, and iii) field trials with at least three randomized replications. Our database included 429 data points extracted from 56 published manuscripts from all regions of Brazil. ResultsRelative to monocropping, intercropping reduced maize grain yield (−5.6 %) and Brachiaria grass biomass production (−64 %) in all scenarios. The intercropped maize grain yield was less affected under the following conditions: i) in the subtropical climate zone (−3.6 %), ii) sowing the intercrop early in the season (−5.8 %), iii) applying monocot-selective postemergence herbicides (−4.4 %), iv) relay temporal arrangement (−2.8 %), and v) with N and P fertilization rates above 150 and 35 kg ha-1, respectively. In relative terms, Brachiaria grass performance was more affected by i) intercropping adoption, with a reduced biomass penalty under a tropical climate (−63.9 %), ii) late sowing time for the intercrop (−59.5 %), iii) application of N fertilizer at a rate above 50 kg ha-1 (−70.5 %), and iv) non-use of postemergence herbicides (−21.9 %). ConclusionMaize-Brachiaria grass intercropping was an effective alternative to produce residue (no-till system adoption) or forage (crop-livestock integrated system adoption) with reduced maize grain yield penalties, diversifying cropping systems in Brazil. Implications or significanceDespite the production penalties for both crop species, considering different relative values, specific management practices and climate conditions could minimize yield penalties. Scenarios favoring maize yields may be preferred for intercropping systems focused on improving soil quality; in contrast, Brachiaria grass biomass may benefit the integration of crop-livestock systems focused on ensuring forage production.

Relationships between combined and individual field crops’ biomass and planting density

ContextField crops, irrigated and rain-fed, constitute the majority of cropland worldwide. An important agricultural variable determining their biomass production is planting density. ObjectiveThe objective of the study was to explore the relationships between the biomass (dry matter) and planting density for either major individual field crops or all field crops, combined, and to suggest empirical models describing them. MethodsThis article describes a meta-analysis of the literature data on biomass per unit area or plant versus planting density for 8 major field crops: 4 cereals (corn, wheat, sorghum, and pearl millet) and 4 forbs (soybean, sunflower, rapeseed, and buckwheat). ResultsWhen data from all 8 field crops are combined, it seems that areal biomass production is practically independent of planting density, apparently obeying the constant final yield hypothesis. By contrast, analyzing each of the 8 crops separately, there is a clear rising trend of areal biomass with increasing planting density, with a mean intraspecific allometric exponent of about 0.25, and individual exponents that increase systematically with a decrease in the average planting density. ConclusionsApplying a planting density-dependent allometric exponent improves the predictability of the interspecific biomass–planting density relationship for field crops. A bounding, exponential model that describes the biomass–planting density relationship in the agricultural range of 1–1000 plants per m2 better than power-law allometric equations is proposed. Although the biological meaning of a derived generic expression relating areal biomass to single-plant biomass for field crops is not clear, the deviation of the outliers probably reflects already utilized or remaining breeding potential for increased areal biomass and agricultural yields. ImplicationsA good understanding, quantification, and prediction of the biomass–planting density relationship is essential for both plant breeding and management of crop production. Plant breeders need this knowledge to choose between strategies for either increasing the single-plant biomass or increasing the plants’ tolerance to high planting density. Agronomists and extensionists need it to optimize planting density for a particular variety under given environmental and agricultural (GxExM) conditions.