Long-term Crop Yield Research Articles

EFFECTS OF TEA (CAMELLIA SINENSIS) CULTIVATION ON SOIL QUALITY IN THE LAM DONG, VIETNAM

The objectives of the study were to assess soil quality and its relationship to the sustainability of tea cultivation in the Lam Dong, Vietnam. Overall soil quality declined with increasing age of the tea plantations as evidenced by decreases in soil OC, total N, K and S, available P and K, mean weight diameter of aggregates. As well, total P, bulk density and mechanical resistance increased with increasing cultivation intensity. Because these soil properties were sensitive to cultivation effects, they were considered to be good indicators of soil quality. Soil properties that were less sensitive to change, and limited as soil quality indicators included texture, clay mineralogy and sesquioxides, and effective cation exchange capacity. Soil quality changes were greatest during the first 10 years of cultivation and were generally greatest in the surface 0- to 40-cm of soil. Soil and crop management factors were considered to be the most important factors affecting soil quality. Decreases in long-term crop yields were found to correspond with decreases in soil quality. In terms of crop productivity, the most important soil quality indicators (based on a multiple regression analysis) were OC, available P, total K and PAWC. Economic analysis of the yield and production cost data indicated that, under current conditions, tea cultivation in the Lam Dong province is sustainable for periods of about 20 years. Thus, measured values of soil quality indicators in the 20-yr tea soils were considered to represent the "critical levels" for economic sustainability of tea cultivation.

Intelligent crop yield prediction system using neural networks and databases

Machine learning plays an important role in decision support and yield forecasting. This is an effective tool for determining strategies during the growing season of plants. The article proposes a method for predicting yield using a complex system consisting of a convolutional neural network (CNN), a feedforward neural network (FNN), and a SQLiteStudio database. The system includes several stages of data processing, starting with the collection and analysis of images and digital data obtained from various sources, and ending with yield forecasting based on this data. A convolutional neural network (CNN) is used to analyze images and video streams to recognize and count fruits on trees, providing accurate data about the status of the crop. Feedforward neural network (FNN) is used to analyze digital data, such as weather station data and long-term crop yield data, to subsequently predict crop yields. The received data is stored in a relational database, which ensures their structured storage and access for subsequent processing. Used SQL language to perform various database operations. To automate the process of counting fruits on trees, the YOLOv8 convolutional neural network model is used, which allows recognizing objects in real time. A Python script has been developed to process images using YOLOv8 and save the results to a database. An integrated system combines various methods and technologies to predict yields and automate data collection and analysis processes. The developed model showed a mean square error (MSE) of 7.33 and a mean absolute percentage error (MAPE) of 6.27%.

Сluster analysis of long-term crop yield data in crop rotation

Today, cluster analysis is one of the most effective tools for processing large amounts of information and is used wherever computing technology is used, since data and the results of their analysis play an increasingly important role in modern information society, and clustering allows for a better understanding of this data. Whenever it is necessary to classify a large amount of information of this kind and present it in a form suitable for further processing, cluster analysis turns out to be very useful and effective. The analysis of 48-year observations on 107 variables (crop yield, crop rotation by tillage techniques and means of intensification, crop contamination, soil moisture content under sowing during the growing season, the sum of non-vegetative precipitation, the sum of decadal precipitation and average decadal air temperatures during the growing season) using clustering by the Ward method allowed us to group observations into 6 clusters and use them to analyze the studied variables. An average positive relationship of soil moisture content before sowing with the amount of non-vegetative precipitation and a negative one with air temperature in May has been established. Linear regression equations of the positive relationship of yield with the consumption of soil moisture during the growing season, precipitation in June-July and negative clogging and average air temperature in May-July were selected.

Crop yield prediction via explainable AI and interpretable machine learning: Dangers of black box models for evaluating climate change impacts on crop yield

Statistical crop modeling is pivotal for understanding climate impacts on crop yields. Choices of models matter: Linear regression is interpretable but limited in predictive power; machine learning predicts well but often remains a black box. To develop explainable artificial intelligence (AI) for exploring historical crop yield data and predicting crop yield, here we reported a Bayesian ensemble model (BM) that is interpretable with great explanatory and predictive power. BM embraces many competitive models via Bayesian model averaging, fits complex functions, and quantifies model uncertainty. Long-term crop yields are driven by both climate and technology; the common practice of first detrending and then analyzing the detrended data has an incorrigible bias. Therefore, BM was also aimed at decomposing historical yield data to jointly estimate technological trends and climate effects on crop yield. We compared BM with ElasticNet, Neural Network, MARS, SVM, Random Forests, and XGBoost. BM excelled at both predicting and explaining. When tested on synthetic data, BM was the only method unveiling the true relationships: BM has stronger interpretability; other methods predicted well but for wrong reasons. When tested on maize yield data in Ohio, BM detected two technological shifts, attributable to hybrid corn adoption in the 1940′s and the technological slowing-down in the 1970′s: No other methods detected such changepoints. BM derived nonlinear asymmetric crop responses to climate and non-negligible temperature-precipitation interacting effects, with patterns consistent with theoretical or experimental evidence. Extrapolation of all the models for future yield prediction was highly uncertain, but BM provided more reliable predictions under novel climate whereas Random Forests and XGBoost proved unsuitable for extrapolation. Overall, BM provided new insights unattainable by the existing black-box methods. We caution against blind use of black-box machine learning for statistical crop modeling and call for more efforts to apply interpretable machine learning for mechanistic understandings of crop-climate interactions.

Evaluation of MODIS, Landsat 8 and Sentinel-2 Data for Accurate Crop Yield Predictions: A Case Study Using STARFM NDVI in Bavaria, Germany

The increasing availability and variety of global satellite products and the rapid development of new algorithms has provided great potential to generate a new level of data with different spatial, temporal, and spectral resolutions. However, the ability of these synthetic spatiotemporal datasets to accurately map and monitor our planet on a field or regional scale remains underexplored. This study aimed to support future research efforts in estimating crop yields by identifying the optimal spatial (10 m, 30 m, or 250 m) and temporal (8 or 16 days) resolutions on a regional scale. The current study explored and discussed the suitability of four different synthetic (Landsat (L)-MOD13Q1 (30 m, 8 and 16 days) and Sentinel-2 (S)-MOD13Q1 (10 m, 8 and 16 days)) and two real (MOD13Q1 (250 m, 8 and 16 days)) NDVI products combined separately to two widely used crop growth models (CGMs) (World Food Studies (WOFOST), and the semi-empiric Light Use Efficiency approach (LUE)) for winter wheat (WW) and oil seed rape (OSR) yield forecasts in Bavaria (70,550 km2) for the year 2019. For WW and OSR, the synthetic products’ high spatial and temporal resolution resulted in higher yield accuracies using LUE and WOFOST. The observations of high temporal resolution (8-day) products of both S-MOD13Q1 and L-MOD13Q1 played a significant role in accurately measuring the yield of WW and OSR. For example, L- and S-MOD13Q1 resulted in an R2 = 0.82 and 0.85, RMSE = 5.46 and 5.01 dt/ha for WW, R2 = 0.89 and 0.82, and RMSE = 2.23 and 2.11 dt/ha for OSR using the LUE model, respectively. Similarly, for the 8- and 16-day products, the simple LUE model (R2 = 0.77 and relative RMSE (RRMSE) = 8.17%) required fewer input parameters to simulate crop yield and was highly accurate, reliable, and more precise than the complex WOFOST model (R2 = 0.66 and RRMSE = 11.35%) with higher input parameters. Conclusively, both S-MOD13Q1 and L-MOD13Q1, in combination with LUE, were more prominent for predicting crop yields on a regional scale than the 16-day products; however, L-MOD13Q1 was advantageous for generating and exploring the long-term yield time series due to the availability of Landsat data since 1982, with a maximum resolution of 30 m. In addition, this study recommended the further use of its findings for implementing and validating the long-term crop yield time series in different regions of the world.

Relating soil chemical properties to other soil properties and dryland crop production

Soil chemical properties have often been related to some other soil properties and crop yields, but extensive evaluation of the relationships of soil chemical properties with other properties and long-term crop yields under dryland cropping systems is lacking. We related six soil chemical properties (pH, electrical conductivity [EC], cation exchange capacity [CEC], and inorganic P [IP], K, and Al concentrations) with 62 other soil physical, chemical, biological, and biochemical properties and crop yields at two long-term (14- and 36-year old) dryland farming sites in the northern Great Plain, United States. Treatments were rotations of no-tillage and conventional tillage spring wheat (Triticum aestivum L.), barley (Hordeum vulgare L.), pea (Pisum sativum L.), and fallow with or without N fertilization. Soil samples collected to a depth of 0–15 cm in April 2019 were analyzed for soil properties and long-term crop yields were determined. Soil chemical properties were mostly correlated to each other at the short-term than at the long-term site. Based on the principal component analysis, EC, CEC, and IP, and K concentrations were associated with most of the physical, chemical, biological, and biochemical properties at both sites. The CEC, IP, and K concentrations were related to mean crop yields across years at individual or combined sites, but other chemical properties were not related to yields. We conclude that CEC, IP, and K concentrations may be used as potential chemical indicators of soil health that were related to most soil properties and crop yields under dryland cropping systems in the semiarid region.

Historical wheat yield mapping using time-series satellite data and district-wise yield statistics over Uttar Pradesh state, India

Crop productivity has often been affected by undesirable climatic events such as heat stress, flood, unseasonal rainfall, drought, etc. Historical crop yield estimation and mapping shall provide a pivotal input for food security measures and planning purposes. The present study deployed various satellite (i.e., normalized difference vegetation index) and gridded (i.e., precipitation, temperature, evapotranspiration) products to portray the historical wheat yield at satellite pixel-level during 2001–2019 over the Uttar Pradesh state of India. Directorate of Economics and Statistics (DES) based district-wise wheat yield dataset was also used with satellite variables to develop the historical wheat yield model based on the multiple regression analysis. Spatially, higher, lower, and lower to moderate wheat yields are observed in the western and northwest parts, southern and southeast parts, and lower-middle to the lower right parts of the state respectively throughout the study years. Remarkably, the years 2017,2018, and 2019 witnessed higher yields, while 2004, 2006, 2007, 2008, and 2015 witnessed lower yield. The developed wheat yield models performed well, as R2 values were observed between 0.3 and 0.76 between the satellite-derived and DES-based district-wise mean yield. The mean absolute error was found to be considerable and ranged between 0.22 and 1.7 t/ha during the study years. The adopted methodology can be used across the different parts of the globe at the local to country scale for a quantitative crop yield depiction and mapping at the satellite pixel level. Such historical crop yield mapping can assist in understanding the long-term crop yield pattern in response to the climate change activity, and hence food security measures can be formulated and implemented.

Long-term evidence for ecological intensification as a pathway to sustainable agriculture

Ecological intensification (EI) could help return agriculture into a ‘safe operating space’ for humanity. Using a novel application of meta-analysis to data from 30 long-term experiments from Europe and Africa (comprising 25,565 yield records), we investigated how field-scale EI practices interact with each other, and with N fertilizer and tillage, in their effects on long-term crop yields. Here we confirmed that EI practices (specifically, increasing crop diversity and adding fertility crops and organic matter) have generally positive effects on the yield of staple crops. However, we show that EI practices have a largely substitutive interaction with N fertilizer, so that EI practices substantially increase yield at low N fertilizer doses but have minimal or no effect on yield at high N fertilizer doses. EI practices had comparable effects across different tillage intensities, and reducing tillage did not strongly affect yields.

Assessing climate vulnerability of historical wheat yield in south-eastern Australia's wheat belt

CONTEXTAgricultural vulnerability assessment is a comprehensive and powerful analytical tool to locate hotspots with states of susceptibility to harm and powerlessness of agricultural system. It plays an important role in guiding policy makers to plan and implement adaptation practices to mitigate potential climate risks to crop. However, due to the diversity in the methodology of vulnerability assessment, there are still knowledge gaps in assessing and comparing crop vulnerability to climate in different regions of the world, including Australia. OBJECTIVEOur main objectives were to: (1) present a vulnerability analytical method for wheat yield, which can be applied to different areas where long-term crop yield and climate data are available. (2) quantify temporal changes of the vulnerability of wheat yield to historical climate. (3) identify the most vulnerable region in study area to provide guidance for climate mitigation. METHODSOur study developed an indicator-based method using exposure, sensitivity, and adaptive capacity to assess the vulnerability of crop yield. We used the long-term recorded wheat yield data, combining with comprehensive exposure index to assess climate vulnerability of historical yield with a case study area of south-eastern Australia's wheat belt. RESULTS AND CONCLUSIONSThe results showed that from the 1930s to the 1990s, both climate exposure and sensitivity had large inter-annual variations with no significant trends detected. However, adaptive capacity increased by 34% from 1930s to 1950s, 54% from 1950s to 1970s, and 54% from 1970s to 1990s. By contrast, climate vulnerability across the wheat belt decreased by 13% from 1930s to 1950s, 15% from 1950s to 1970s, and 33% from 1970s to 1990s. This is mainly due to increased adaptive capacity with the improvement of agronomic management practices, technological and socio-economic progress. We identified the areas with the highest vulnerability were in the northwestern parts of wheat belt while the least vulnerable areas located in the southeast. SIGNIFICANCEWe expect that these identified vulnerable hotspots can be used by different landholders to allocate natural resources and policymakers to plan the priority mitigation to adapt to climate change in the local scale. Moreover, the method of vulnerability assessment used in this study can be applied to other regions around the world where long-term crop yield and climate data are available.

Review Effect of Combined Application of Organic and Inorganic Fertilizer on Cabbage and Its Economic Importance’s

Cabbage belongs to the family cruciferae. Among the other vegetable cabbage is one of the easiest crops to establish and most of the people grow this crop in their home garden. In the major vegetable-growing areas, it is also an essential economical and rotational crop. Cabbage is an important vegetable crop and it is one of favorite crop grown in kitchen garden because easy to rise. Cabbage is high in vitamins A, C, K, B 1 , B 2 , B 6 , calcium, dietary fiber, and protein whether eaten fresh in salads or boiled or cooked in stews and soups. Cabbage is commonly consumed as a cooked or stir-fried vegetable, or as a raw element in salads such as coleslaw and mixed salads. Cabbage growth and yield are known to be influenced by a variety of cultural practices and growing settings. Nutrition is a major determinant of cabbage growth and production. Production of cabbage could be enhanced through efficient use of applied both organic and inorganic fertilizer. When compared to chemical fertilizers, organic fertilizers (cow dung, poultry manure, and vermin-compost) resulted in better nutrient uptake. Due to decreasing soil fertility, vegetable crop yields decreased. Organic manure can help to keep soil fertility and crop production in check. It is true that the use of inorganic fertilizers for crops has a negative impact on soil health, but organic fertilizers do not have this issue. Neither chemical fertilizers nor organic manure can help achieve long-term crop yield on their own. Because of deterioration in soil physical, chemical, and biological qualities, high yield levels could not be sustained using simply balanced chemical fertilizers over time. Integrated nutrient management is the most effective method for ensuring long-term soil fertility and productivity of cabbage. The objective of this review is to review the effect of combined application of organic and inorganic fertilizer on cabbage and its economic importance’s. Keywords: cabbage, organic, inorganic, combined, fertilizer DOI: 10.7176/JBAH/11-19-02 Publication date: October 31 st 2021

Evaluation of potential amounts of crop straw available for bioenergy production and bio-technology spatial distribution in China under ecological and cost constraints

A comprehensive evaluation model is built to analyze the potential of straw resources and suitability distribution of technology in China under environment and cost constraints. First, from the perspective of soil erosion prevention, soil organic carbon maintenance, and long-term crop yield, the ecological return amount of straw in different provinces is calculated using scenario design, literature review, and analytic hierarchy process. These processes, combined with technical and economic parameters of different technologies, are employed to analyze the appropriate spatial distribution of straw-fired generation (SFC), biomass gasification power generation (BGPG), cellulose ethanol technology (CEP), and biomass briquette fuel (BBF) under different policy and technological progress scenarios from the project level. The changes in planting area, crop unit yield, and wage are also considered in the evaluation; the obtained results indicate the following. First, if priority is given to agricultural ecological environment, the amount of straw available is less than that determined by the existing research, i.e., approximately 2.48 × 108, 1.55 × 108, and 8.68 × 107 t under low, medium, and high return scenarios, respectively. Second, Inner Mongolia is not suitable for planning any projects, and planned projects should compensate for the local agricultural ecological environment. Third, under the current policy and technical level scenario, resources for the BBF and CEP projects should be employed to prioritize the SFC and BGPG projects. Fourth, in combination with China’s current poverty alleviation policies, the planning of biomass energy projects in Gansu, Qinghai, Sichuan, Chongqing, and Guizhou should be prioritized because of their lower labor cost.

Impact of forty-seven years of long-term fertilization and liming on soil health, yield of soybean and wheat in an acidic Alfisol

ABSTRACT Fertilization with suitable amendment in acidic soil is crucial to achieve long-term high crop yield without deteriorating soil health in intensive cropping system. We studied the chemical and biological properties of soil, soybean, and wheat yield after 47 years of long-term fertilization in an acidic Alfisol. Treatments were: unfertilized (control); recommended N dose (N); recommended N and P (NP); recommended N, P and K (NPK); NPK + hand weeding (NPK + HW); NPK + 10 Mg ha−1 farmyard manure (NPK + FYM) and NPK + 0.40 Mg ha−1 lime (NPK + lime). Long-term application of NPK + FYM significantly enhanced the soil cation exchange capacity (CEC), organic carbon, available NPK and microbial functions compared to NPK. Application of NPK + lime increased soil pH, dehydrogenase activity (DHA) and bacterial population by 15.7%, 38.8% and 145% respectively, over NPK. Continuous inorganic-P fertilizer application leads to substantial build-up of available-P in soil. Fertilization of FYM/lime with NPK recorded higher crops yield compared to NPK. Soil DHA, bacterial population and CEC were most influencing factors governing grain yield. Overall, long-term application of NPK with FYM/lime improved soil health and crop yield, but rate of inorganic-P input should be curtailed in an Alfisol.

Effects of a One-Time Organic Fertilizer Application on Long-Term Crop and Residue Yields, and Soil Quality Measurements Using Biointensive Agriculture

Abstract Background: Closing the yield gap, especially in Africa, using environmentally appropriate and sustainable methods is important to meet current and future food demands. Grow Biointensive Sustainable Agriculture (GBSA) is an organic method that combines eight sustainable practices that may close the yield gap, increase food production, improve soil quality and reduce soil nutrient loss. Method: Utilizing the GBSA method, four Kenyan farms over four years tested the effect of a one-time application of organic fertilizers (recommended based on soil testing) on crop and residue yields and soil parameter levels. Results: Yearly crop edible yields increased overall for maize, sorghum and sweet potatoes. Amaranth showed no significant change. Yearly crop residue yields increased for sweet potatoes, and increased marginally for amaranth, maize, and sorghum. Harvesting practice changes in cowpeas and lablab made interpretation more complex, but both edible and residue yields for these crops varied from unchanged to significantly increased. Fourteen soil parameters were tested. Boron, electrical conductivity, magnesium, organic matter, phosphorus, potassium, sulfur and zinc increased significantly. Calcium, copper, iron, pH, and sodium showed no significant changes, and only manganese levels decreased significantly. Conclusion: Combining a single application of recommended organic fertilizers and the GBSA method, farmers may be able to improve their crop yields and maintain soil fertility in a manner that is more sustainable.

Assessment of blackberry varieties suitable for growing in the Right-Bank Forest-Steppe part of Ukraine

The research aimed to study the productivity of blackberry foreign varieties (Rubus subgenus Rubus Watson) in the Right-Bank Forest-Steppe part of Ukraine as they are attractive for production due to their fruit taste and appearance. The task was to establish the beginning and duration of fruiting of blackberry varieties, to determine the yield level and the quality of the berries. A fi eld experiment was conducted on the experimental fi eld of the Scientific and Research Center of BNAU to assess the productivity of blackberry varieties. The studies were started in the spring of 2017 on blackberry seedlings with growing the culture in vitro in the biotechnological laboratory of the university. The tests included 5 varieties with climbing shoots: Smoothstem, Thornfree, Black Satin, Triple Crown, Arapaho. According to the results of observations, it was found that for two years of fruiting among the studied varieties, the earliest production was received in the Arapaho variety, in the first decade of July. Triple Crown, with the fi rst fruits ripened on August 2, was characterized by late fruiting. The Triple Crown variety diff ered by the longest period of berry coming – 35 days. The yield obtained in a year after planting is not an indicator of long-term crop yields. However, the Triple Crown variety diff ered by the highest yield in the fi rst year of fruiting, with an average of 3.7 kg of berries obtained from a bush. High yields were also observed in the Smoothstem variety. Black Satin variety was characterized by the high level of yield growth for over two years of fruiting, which amounted to 80 %. The largest in the fi rst years of fruiting were Triple Crown fruits with an average weight of 11.1 g. The maximum weight of individual berries of this variety reached 17.7 g. The Arapaho variety also stood out with its large fruits of 8.5 g on average. Some fruits reached a maximum weight of 12.1 g. Key words: blackberry, the beginning of fruiting, productivity, average weight of berries, duration of products supply.

Long-Term Evidence Shows that Crop-Rotation Diversification Increases Agricultural Resilience to Adverse Growing Conditions in North America

Summary A grand challenge facing humanity is how to produce food for a growing population in the face of a changing climate and environmental degradation. Although empirical evidence remains sparse, management strategies that increase environmental sustainability, such as increasing agroecosystem diversity through crop rotations, may also increase resilience to weather extremes without sacrificing yields. We used multilevel regression analyses of long-term crop yield datasets across a continental precipitation gradient to assess how temporal crop diversification affects maize yields in intensively managed grain systems. More diverse rotations increased maize yields over time and across all growing conditions (28.1% on average), including in favorable conditions (22.6%). Notably, more diverse rotations also showed positive effects on yield under unfavorable conditions, whereby yield losses were reduced by 14.0%–89.9% in drought years. Systems approaches to environmental sustainability and yield resilience, such as crop-rotation diversification, are a central component of risk-reduction strategies and should inform the enablement of policies.

41 Management effects on long-term crop yield stability in central and eastern nebraska

Abstract The sustainability and resilience of cropping systems will depend on how different management practices impact yield stability over the long-term. Here, we summarize the effects of crop rotation, tillage, crop residue removal, and nitrogen fertilizer rate on corn and soybean yield responses from several long-term research experiments maintained by the USDA-Agricultural Research Service in eastern and central Nebraska. Across all study sites, we found that yield stability is affected most by crop rotation, then tillage, then residue management, where stability can be further improved in all systems by using the optimum N fertilizer rate. While crop rotation and rotation complexity tend to have the greatest impact on long-term yield stability in the western Corn Belt, nearly all combinations of management can produce stable yields if well managed.

Strategic valorization of de-oiled microalgal biomass waste as biofertilizer for sustainable and improved agriculture of rice (Oryza sativa L.) crop

Intensive use of chemical fertilizer results in environmental pollution that disturbs the local ecosystem and causes reduction in the long-term crop yield. There is a need to explore the alternative source of plant nutrition such as de-oiled microalgal biomass as biofertilizer for sustainable production of food crops in a relatively pollution free environment. This study reports sustainable and improved agriculture of rice crop (cv. IR 36) by valorizing de-oiled microalgal biomass waste (DOMBW) of Scenedesmus sp., as eco-friendly fertilizer. The microalga (MA) was cultivated in open raceway pond using wastewater and flue gas. Performance evaluation and comparison of DOMBW with respect to growth and yield of rice plants vis-à-vis commercial chemical fertilizers (CF) and vermicompost (VC) applied individually or together, established the superiority of the former. The experiment comprised of five nutrient management treatments (CF100, VC100, MA100, MA50+CF50, and MA50+VC50) meeting 100% nitrogen (N) recommendation either through a single source or combined application in the soil. Combining the application of microalgal based organic fertilizer with chemical fertilizer (MA50+CF50), showed the highest performance in terms of plant height, tiller number, biomass, and grain yield. At the harvest stage, MA50+CF50 also resulted in maximum plant dry weight, panicle weight, and 1000-grain weight in comparison to other treatments. This study revealed that the application of DOMBW as a biofertilizer is potentially sustainable and effective in improving the yields of rice crop with reduced use of chemical fertilizer.

Long-term simulated runoff and water quality from grain cropping systems on restrictive layer soils

Evaluation of the sustainability of grain production systems requires knowledge of long-term crop yields, and edge-of-field runoff volumes and water quality. When measured data are limited, computer simulation models —calibrated and validated with the available data— are well suited for this type of inquiry. In this study, the Agricultural Policy Environmental Extender (APEX) model was used to investigate how management affects crop yields, runoff volume, and water quality on claypan soils. Specific objectives were to determine the ability of APEX to simulate the variability of corn and soybean yields on claypan soils and the transport of dissolved-phase atrazine and nutrients via surface runoff; and how management affects long-term runoff and associated loss of these constituents. In spite of satisfactory model performance for corn yields and acceptable relative differences for soybean yields, the model was not capable of simulating the effects of management or landscape position on crop yields. Model performance was very good for runoff, and mostly satisfactory for dissolved constituents. Long-term simulation along with uncertainty analysis indicated that a no-till 3-year corn-soybean-wheat rotation with cover crops provides the most benefits to water quality by reducing nutrient transport in runoff. This system appeared to mitigate the negative effects of no-till on the surface transport of non-incorporated dissolved chemicals because of improved fertilizer management in terms of rate, placement, and timing of application. Cover crops appeared to reduce nitrate leaching compared to a 2-year no-till system. This research highlights the need for further research in crop growth modeling as inconsistent crop yield results undermine the usefulness of the model for making recommendations about management. Overall, the long-term simulations support the concept of multiple practices addressing tillage, fertilizer management, longer crop rotations, and use of cover crops to achieve desired environmental and production goals.

Long-term yields of oilseed rape and winter wheat in a short rotation alley cropping agroforestry system

Alley cropping agroforestry systems (ACS) are ascribed to have manifold positive ecological effects; nevertheless their application is still limited due to uncertain productivity of the agricultural crop, especially in the tree-crop competition zone. Therefore, this study investigated the variability of oilseed rape and winter wheat yield, respectively, at different distances from the tree strip edge in 2016 and 2017 in an ACS established in 2008 in northern Germany. The ACS consisted of strips of fast-growing poplars alternating with narrow (48 m) and wide (96 m) crop alleys, each with a crop rotation including winter oilseed rape and winter wheat. Each tree strip contained 6 rows of poplars with a density of 10,000 trees per ha. Moreover, multi-year (2009–2016) crop yield data of oilseed rape and winter wheat in the narrow and wide crop alleys were compared with those of a corresponding non-agroforestry control field. In general, crop yields observed in 2016 and 2017 in the narrow crop alleys at 1 m from the tree strip edges were on average 77% (oilseed rape) and 55% (winter wheat) lower than in the middle of the crop alley. One reason for low yield close to the tree strips might be the leaf litter coverage of the seedlings in autumn. Leaf litter deposition was highest at 1 m on the windward and the leeward side of the tree strips in 2015 and on the leeward side in 2016, respectively. However, the average long-term crop yields of the narrow crop alley, the wide crop alley and the control field did not differ substantially among each other. Although oilseed rape and winter wheat yields were lower close to the tree strips, this yield reduction did not negatively influence the average long-term crop yields of the ACS.

Subsurface drainage for promoting soil strength for field operations in southern Manitoba

Successful field operations depend on the ability of the soil to provide traction and support for agricultural traffic and be workable in a desirable manner. This minimizes the risk of soil structural damage to ensure soil conservation and long-term crop yield. In the present study, the objective was to evaluate the performance of subsurface drainage in promoting soil strength for field operations in southern Manitoba. Data obtained from a field study and modeling exercise were used to achieve the objective. The field data include soil water content and watertable depth, which were measured in a potato field during the 2011 growing season. Soil samples were also collected from the field to determine the lower plastic limit (LPL) of the soil. Seventeen-year weather data were used to estimate the reference crop evapotranspiration. The soil strength of drained and undrained sandy-loam fields was compared to evaluate subsurface drainage in promoting soil strength for field operations. The subsurface tile drain was installed at 0.9-m depth and at 15-m spacing. A validated HYDRUS (2D/3D) model was used to extend the study by simulating soil water content dynamics due to different drain spacings (8 m, 10 m, 12 m, 15 m, 20 m, and 30 m) for different years and weather conditions. The soil strength to allow field operations was assessed based on soil water content corresponding to 90% of the LPL in the top 0.3-m depth of the soil, and soil water content corresponding to the LPL in the soil layer between 0.3-m to 0.5-m depth of the soil profile. The soil strength was sufficient to allow field operations when the two criteria were met. In the top 0.3-m depth, the soil strength was sufficient to allow field operations for all the years with or without drainage. Drainage impact was found to be more significant within the 0.3-m to 0.5-m depth of the soil profile throughout the years. Drain spacing less or equal to 12 m promoted soil strength to allow field operations without any significant impact on the number of field workable days during the growing season.