Hydrologic and water quality impacts from perennial crop production on marginal lands

Marginal lands are proposed as a viable option for producing biofeedstocks as these lands are not heavily engaged in agricultural production or may not be suitable for intensive row-crop food/feed production. However, meeting biofeedstock production goals will require large amount of marginal lands and the unintended consequences of producing biofeedstocks on marginal lands are not fully clear. The overall goal of this study was to evaluate the productivity of biofeedstocks on marginal lands and the potential impacts on hydrologic and water quality processes from the land use conversion. This study was conducted in the Upper Mississippi River Basin (UMRB). First, the suitability of marginal lands in this region was evaluated for the growth of three candidate biofeedstock crops, switchgrass, Miscanthus and hybrid poplar. The evaluation was conducted using a fuzzy logic based land suitability evaluation method. Then, the simulation of switchgrass and Miscanthus growth during their establishment periods in the Soil and Water Assessment Tool (SWAT) model was improved. Finally, the model was used to evaluate the impacts on hydrologic and water quality processes due to production of switchgrass and Miscanthus on marginal lands in the UMRB region. The results indicated that 23% of the UMRB area included marginal lands. Among these lands, 40% of them were poorly suitable for the production of biofeedstock crops. Biofeedstocks produced from these marginal lands could be converted to biofuels that contributed 14 to 25% of the 132 billion liter biofuel goals set by the Energy Independence and Security Act (EISA) 2007. The model simulation results indicated that producing perennial biofeedstock crops on marginal land would reduce annual stream flow by 20% and 29% and sediment load by 26% to 35% at the watershed outlet. The reduction was less during the establishment periods of perennial grasses (first 2 to 3 years of switchgrass and 2 to 4 years of Miscanthus) than during the post establishment periods. The results of this study indicated that marginal land in the UMRB region could be a viable choice of land resources for biofuel development and could be used to produce almost one quarter of biofuel production goals. At the same time, water quality in the watershed could be improved. The information could be used by stakeholders to create regional biofuel development and watershed management plans

Evaluating the suitability of marginal land for a perennial energy crop on the Loess Plateau of China

Abstract With a large marginal land area, the Loess Plateau in China holds great potential for biomass production and environmental improvement. Identifying suitable locations for biomass production on marginal land is important for decision‐makers from the viewpoint of land‐use planning. However, there is limited information on the suitability of marginal land within the Loess Plateau for biomass production. Therefore, this study aims to evaluate the suitability of the promising perennial energy crop switchgrass (Panicum virgatum L.) on marginal land across the Loess Plateau. A fuzzy logical model was developed and validated based on field trials on the Loess Plateau and applied to the marginal land of this region, owing to its ability of dealing with the continuous nature of soil, landscape variations, and uncertainties of the input data. This study identified that approximately 12.8–20.8 Mha of the Loess Plateau as available marginal land, of which 2.8–4.7 Mha is theoretically suitable for switchgrass cultivation. These parts of the total marginal land are mainly distributed in northeast and southwest of the Loess Plateau. The potential yield of switchgrass ranges between 44 and 77 Tg. This study showed that switchgrass can grow on a large proportion of the marginal land of the Loess Plateau and therefore offers great potential for biomass provision. The spatial suitability maps produced in this study provide information to farmers and policymakers to enable a more sustainable development of biomass production on the Loess Plateau. In addition, the fuzzy‐theory‐based model developed in this study provided a good framework for evaluating the suitability of marginal land

Quantitative construction of climatic suitability maps through multi-criteria evaluation and Geographic Information Systems

El presente trabajo tiene como objetivo caracterizar la aptitud climática de la provincia de Catamarca para la producción de nuez pecán en función de tres variables climáticas utilizando Sistemas de Información Geográfica (SIG). Se emplearon como fuentes de datos las variables BIO5, BIO6 y BIO12 del modelo WorldClim. Se realizó una clasificación fuzzy de las variables para determinar su aptitud en función de los requerimientos del cultivo y un promedio de las tres capas para obtener el mapa promedio climático. La metodología aplicada permitió determinar que la provincia de Catamarca cuenta con aproximadamente un 19% de tierras aptas para la producción de nogal pecán.The aime of this work is to characterize the climatic suitability of the province of Catamarca for the production of pecan nuts based on three climatic variables using Geographic Information Systems (GIS). The variables BIO5, BIO6 and BIO12 of the WorldClim model were used as data sources. A fuzzy classification of the variables was carried out to determine their suitability based on thecrop requirements and an average of the three layers to obtain the average climatic map. The methodology applied allowed to determine that the province of Catamarca has approximately 19% of land suitable for the production of pecan walnut.Fil: Trabichet, Florencia Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Luján; Argentin

Using a Crop Model to Benchmark Miscanthus and Switchgrass

Crop yields are important items in the economic performance and the environmental impacts of second-generation biofuels. Since they strongly depend on crop management and pedoclimatic conditions, it is important to compare candidate feedstocks to select the most appropriate crops in a given context. Agro-ecosystem models offer a prime route to benchmark crops, but have been little tested from this perspective thus far. Here, we tested whether an agro-ecosystem model (CERES-EGC) was specific enough to capture the differences between miscanthus and switchgrass in northern Europe. The model was compared to field observations obtained in seven long-term trials in France and the UK, involving different fertilizer input rates and harvesting dates. At the calibration site (Estrées-Mons), the mean deviations between simulated and observed crop biomass yields for miscanthus varied between −0.3 t DM ha−1 and 4.2 t DM ha−1. For switchgrass, simulated yields were within 1.0 t DM ha−1 of the experimental data. Observed miscanthus yields were higher than switchgrass yields in most sites and for all treatments, with one exception. Overall, the model captured the differences between both crops adequately, with a mean deviation of 0.46 t DM ha−1, and could be used to guide feedstock selections over larger biomass supply areas

Multi-Criteria Evaluation Model for Classifying Marginal Cropland in Nebraska Using Historical Crop Yield and Biophysical Characteristics

Marginal cropland is suboptimal due to historically low and variable productivity and limiting biophysical characteristics. To support future agricultural management and policy decisions in Nebraska, U.S.A, it is important to understand where cropland is marginal for its two most economically important crops: corn (Zea mays) and soybean (Glycine max). As corn and soybean are frequently planted in a crop rotation, it is important to consider if there is a relationship with cropland marginality. Based on the current literature, there exists a need for a flexible yet robust methodology for identifying marginal land at different scales, which takes advantage of high spatial and temporal resolution data and can be applied by researchers and outreach professionals alike. This research seeks to individually identify where cropland is marginal for corn and soybean as well as classify the extent of marginality that exists. This research also seeks to classify cropland as being part of a long-term corn-soybean crop and see if marginality differs between this cropland and the remainder of cropland. Two crop-specific multi-criteria evaluations (MCE), consisting of crop production, climate, and soil criteria, was performed using Google Earth Engine to identify and classify marginal cropland. Criteria were individually thresholded before addition to the MCEs. Cropland that was classified as part of a long-term corn-soybean crop rotation was identified by factoring in the balance of corn and soybean occurrence on long established cropland. Most cropland in Nebraska has at least some marginality for corn while most has no marginality for soybean. Marginality classification is spatially distributed with increasing marginality from the northeast to the southwest. Cropland under a long-term crop rotation shows much less marginality compared to non-rotation cropland. This study improves upon previous attempts to identify marginal cropland in Nebraska by increasing spatial and temporal resolution, providing a programmatic and replicable methodology, and confining the classification to existing cropland. The implications of these findings are useful for policy makers and agricultural extension efforts in Nebraska to identify opportunities for conservation, solar energy capture, and biofuel production on cultivated land. Advisor: Yi Q

Soil moisture patterns and hydraulic properties associated with alternative biomass cropping systems across a landscape gradient

Predicting the hydrologic consequences of biomass cropping systems requires an understanding of how different crops and management practices affect soil hydraulic properties across space and time. To inform such predictions, I investigated the impacts of five biomass cropping systems on the hydraulic properties of soils across a landscape gradient in wet, dry, and average rainfall years. I used data from 2010 - 2012 on monthly volumetric soil moisture content and data from 2009 - 2013 on changes in saturated hydraulic conductivity to measure significant differences in mean soil moisture content among five cropping systems across five landscape positions. My results suggest moisture content was most broadly controlled by the amount of rainfall within a year, but there were also significant differences with landscape positions, cropping systems, cropping system by landscape position, and soil clay content; biomass yield was not a significant predictor of soil moisture. I also found a significant change in saturated hydraulic conductivity among cropping systems from 2009 to 2013, and different saturated conductivity among cropping systems at different landscape positions in 2013. Differences in hydraulic conductivity among cropping systems were commonly found at floodplain and footslope positions; there were very few significant differences among cropping systems at the summit, shoulder, and backslope positions. Changes over time within cropping systems are attributed to conversion to either perennial cropping systems or to no-till soil management in annual systems. My results support the hypothesis that different biomass cropping systems will have different hydrological impacts depending on landscape position. This knowledge can be used to parameterize or improve physically-based hydrologic models of biomass production and understand the potential environmental impacts bioenergy crop production

Multi-criteria suitability analysis for neglected and underutilised crop species in South Africa

Several neglected and underutilised species (NUS) provide solutions to climate change and creating a Zero Hunger world, the Sustainable Development Goal 2. Several NUS are drought and heat stress-tolerant, making them ideal for improving marginalised cropping systems in drought-prone areas. However, owing to their status as NUS, current crop suitability maps do not include them as part of the crop choices. This study aimed to develop land suitability maps for selected NUS [sorghum, (Sorghum bicolor), cowpea (Vigna unguiculata), amaranth and taro (Colocasia esculenta)] using Analytic Hierarchy Process (AHP) in ArcGIS. Multidisciplinary factors from climatic, soil and landscape, socio-economic and technical indicators overlaid using Weighted Overlay Analysis. Validation was done through field visits, and area under the curve (AUC) was used to measure AHP model performance. The results indicated that sorghum was highly suitable (S1) = 2%, moderately suitable (S2) = 61%, marginally suitable (S3) = 33%, and unsuitable (N1) = 4%, cowpea S1 = 3%, S2 = 56%, S3 = 39%, N1 = 2%, amaranth S1 = 8%, S2 = 81%, S3 = 11%, and taro S1 = 0.4%, S2 = 28%, S3 = 64%, N1 = 7%, of calculated arable land of SA (12 655 859 ha). Overall, the validation showed that the mapping exercises exhibited a high degree of accuracies (i.e. sorghum AUC = 0.87, cowpea AUC = 0.88, amaranth AUC = 0.95 and taro AUC = 0.82). Rainfall was the most critical variable and criteria with the highest impact on land suitability of the NUS. Results of this study suggest that South Africa has a huge potential for NUS production. The maps developed can contribute to evidence-based and site-specific recommendations for NUS and their mainstreaming. Also, the maps can be used to design appropriate production guidelines and to support existing policy frameworks which advocate for sustainable intensification of marginalised cropping systems through increased crop diversity and the use of stress-tolerant food crops

Evaluation of land suitability methods with reference to neglected and underutilised crop species: A scoping review

In agriculture, land use and land classification address questions such as “where”, “why” and “when” a particular crop is grown within a particular agroecology. To date, there are several land suitability analysis (LSA) methods, but there is no consensus on the best method for crop suitability analysis. We conducted a scoping review to evaluate methodological strategies for LSA. Secondary to this, we assessed which of these would be suitable for neglected and underutilised crop species (NUS). The review classified LSA methods reported in articles as traditional (26.6%) and modern (63.4%). Modern approaches, including multi-criteria decision-making (MCDM) methods such as analytical hierarchy process (AHP) (14.9%) and fuzzy methods (12.9%); crop simulation models (9.9%) and machine learning related methods (25.7%) are gaining popularity over traditional methods. The MCDM methods, namely AHP and fuzzy, are commonly applied to LSA while crop models and machine learning related methods are gaining popularity. A total of 67 parameters from climatic, hydrology, soil, socio-economic and landscape properties are essential in LSA. Unavailability and the inclusion of categorical datasets from social sources is a challenge

Corn, Wheat, And Switchgrass Biomass Production In The Northern Plains: Evaluating Opportunities And Tradeoffs

The US government has mandated the annual usage of 61 GL of cellulosic biofuel by 2022. Cellulosic residues from annual crops, such as corn (Zea mays L.) and wheat (Triticum aestivum L. ssp. aestivum) represent a potential source of cellulosic biomass. Another source is the production of cellulosic bioenergy crops. Switchgrass (Panicum virgatum L.) was identified as a model biomass crop by the US Department of Energy in 1992, features the most advanced agronomic development among herbaceous perennial bioenergy feedstock candidates, and is widely adapted across North America. In three interconnected studies considering a 99-county area of the eastern Dakotas and western Minnesota, this dissertation characterizes the existing resource base of corn and wheat cellulosic biomass, estimates the biomass prices necessary for switchgrass to be competitive with collection of existing corn and wheat biomass, and estimates the necessary incentives for switchgrass to supplant sufficient corn or wheat area to offset recent grassland-to-cropland conversions observed within the study region. An improved parameterization of upland switchgrass ecotypes for the ALMANAC (Agricultural Land Management Alternative with Numerical Assessment Criteria) model was shown to predict multiyear-average yields with an RMSE of 1.95 Mg ha-1 and PBIAS of 7.2%. Using moderate-resolution regional inputs, ALMANAC estimated county-scale multiyear-average corn yields with an RMSE of 0.71 Mg ha-1 and PBIAS of 1.9%, and corresponding wheat yields with an RMSE of 0.28 Mg ha-1 and PBIAS of 2.8%. Corn and wheat can supply up to 16.48 Tg of biomass annually within estimated biorefinery collection areas, at a biomass price of $60 Mg-1 or less. Switchgrass would require biomass prices of$60 to $180 Mg-1 to supplant corn or wheat production, dependent on establishment and production cost assumptions. Annual payments of$120 to $290 million would encourage sufficient switchgrass production to offset recent grassland-to-cropland conversions in the study region, and can be strategically directed to maximize the environmental benefits of switchgrass production

Crop suitability mapping for underutilized crops in South Africa.

Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.Several neglected and underutilised species (NUS) provide solutions to climate change and create a Zero Hunger world, the Sustainable Development Goal 2. However, limited information describing their agronomy, water use, and evaluation of potential growing zones to improve sustainable production has previously been cited as the bottlenecks to their promotion in South Africa's (SA) marginal areas. Therefore, the thesis outlines a series of assessments aimed at fitting NUS in the dryland farming systems of SA. The study successfully mapped current and possible future suitable zones for NUS in South Africa. Initially, the study conducted a scoping review of land suitability methods. After that, South African bioclimatic zones with high rainfall variability and water scarcity were mapped. Using the analytic hierarchy process (AHP), the suitability for selected NUS sorghum (Sorghum bicolor), cowpea (Vigna unguiculata), amaranth and taro (Colocasia esculenta) was mapped. The future growing zones were used using the MaxEnt model. This was only done for KwaZulu Natal. Lastly, the study assessed management strategies such as optimum planting date, plant density, row spacing, and fertiliser inputs for sorghum. The review classified LSA methods reported in articles as traditional (26.6%) and modern (63.4%). Modern approaches, including multicriteria decision-making (MCDM) methods such as AHP (14.9%) and fuzzy methods (12.9%), crop simulation models (9.9%) and machine-learning-related methods (25.7%), are gaining popularity over traditional methods. The review provided the basis and justification for land suitability analysis (LSA) methods to map potential growing zones of NUS. The review concluded that there is no consensus on the most robust method for assessing NUS's current and future suitability. South Africa is a water-scarce country, and rainfall is undoubtedly the dominating factor determining crop production, especially in marginal areas where irrigation facilities are limited for smallholder farmers. Based on these challenges, there is a need to characterise bioclimatic zones in SA that can be qualified under water stress and with high rainfall variation. Mapping high-risk agricultural drought areas were achieved by using the Vegetation Drought Response Index (VegDRI), a hybrid drought index that integrates the Standardized Precipitation Index (SPI), Temperature Condition Index (TCI), and the Vegetation Condition Index (VCI). In NUS production, land use and land classification address questions such as “where”, “why”, and “when” a particular crop is grown within particular agroecology. The study mapped the current and future suitable zones for NUS. The current land suitability assessment was done using Analytic Hierarchy Process (AHP) using multidisciplinary factors, and the future was done through a machine learning model Maxent. The maps developed can contribute to evidence-based and site-specific recommendations for NUS and their mainstreaming. Several NUS are hypothesised to be suitable in dry regions, but the future suitability remains unknown. The future distribution of NUS was modelled based on three representative concentration pathways (RCPs 2.6, 4.5 and 8.5) for the years between 2030 and 2070 using the maximum entropy (MaxEnt) model. The analysis showed a 4.2-25% increase under S1-S3 for sorghum, cowpea, and amaranth growing areas from 2030 to 2070. Across all RCPs, taro is predicted to decrease by 0.3-18 % under S3 from 2050 to 2070 for all three RCPs. Finally, the crop model was used to integrate genotype, environment and management to develop one of the NUS-sorghum production guidelines in KwaZulu-Natal, South Africa. Best sorghum management practices were identified using the Sensitivity Analysis and generalised likelihood uncertainty estimation (GLUE) tools in DSSAT. The best sorghum management is identified by an optimisation procedure that selects the optimum sowing time and planting density-targeting 51,100, 68,200, 102,500, 205,000 and 300 000 plants ha-1 and fertiliser application rate (75 and 100 kg ha-1) with maximum long-term mean yield. The NUS are suitable for drought-prone areas, making them ideal for marginalised farming systems to enhance food and nutrition security