<p>Astronaut diets on the International Space Station (ISS) depend on resupplied packaged food. However, missions to Mars of 3-5 years will not accommodate re-supply. In addition, many human macro and micronutrients degrade during long-term storage. Thus, growing nutritional plants aboard ISS is essential for providing astronauts with fresh, healthy produce. NASA is usingan experimental vegetable- production unit called VEGGIE to grow fresh salad crops aboard ISS to provide astronauts with healthy diets. VEGGIE is a small plant-growth chamber designed as a garden for astronauts that is low in mass and has a low power requirement. Veggie is equipped with light-emitting diodes (LEDs) but is exposed to the ISS cabin environment. Plants are grown with roots in a baked-ceramic substrate (arcillite) incorporating controlled-release fertilizer (Nutricote) and wicks delivering water by capillary action from a reservoir.</p><br><div>The fertilizerprills release nutrients into arcillite slowly over time. Different controlled-release types have the same amount of fertilizer but release it over different time periods. The Purdue Mitchell lab in collaboration with NASA is testing growth of salad crops within VEGGIE analogs under ISS-like environments in a growth chamber. Specifically, we are evaluating effects of different controlled-release fertilizer treatments as well as different substrate particle sizeson “cut-and-come-again” harvest scenarios, comparing productivity and quality of Lettuce as well as anAsian salad crop called Mizuna.<br></div><div><br></div><div>SS environments being mimicked include temperature: 24/21°C D/N, CO2: 2800 PPM D/N, RH: 45-50% D/N, and photoperiod: 16hours.Arcillitemedium contained one oftwo different fertilizer mixes: 7.5g18-6-8 T 70 + 7.5g 18-6-8 T100, or 7.5g18-6-8 T70 +7.5g 18-6-8 T180fertilizer/liter medium. LED Light treatment provides atotal PPFDof 330µmol m--2s-1 PAR; with 270µmol m--2s-1Red(R), 30µmol m--2s-1Blue (B), and 30µmol m--2s-1Green (G). Plants are grown under those conditions for 8 weeks, and harvested three times at 28, 42, and 56 days from planting. At each harvest, yield parameters as well as tissue mineral content have been measured for optimum fertilizer treatment selection.<br></div><div><br></div><div>Lettuce and Mizuna plants grown in a mix of 100% fine substrate particles (Profile) and fertilizer treatment of 50% T100:50%T70 had the higher yield as well as nitrogen contentcompared to those grown in 50%T180:50%T70. Growing mizuna plants in 100% profile resulted in higher shoot fresh weight; although no significant differences occurred for shoot dry weight. In addition, there was no significant interaction between substrate and fertilizer, which is reported by other research as one of the advantages of using controlled-release fertilizer<br></div>

<div>The demand for customized farm management prescription is increasing in order to maximize crop yield and minimize environmental risks under a changing climate. One great challenge to modeling crop growth and production is spatial and temporal variability. The goal of this dissertation research is to use publicly available Landsat imagery, ground samples and historical yield data to establish methodologies to spatially quantify cover crop growth and in-season maize yield. First, an investigation was conducted into the feasibility of using satellite remote sensing and spatial interpolation with minimal ground samples to rapidly estimate season-specific cover crop biomass and N uptake in the small watershed of Lake Bloomington in Illinois. Results from this study demonstrated that remote sensing indices could capture the spatial pattern of cover crop growth as affected by various cover crop and cash crop management systems. Soil adjusted vegetation index (SAVI), enhanced vegetation index (EVI) and triangular vegetation index (TVI) were strongly correlated with cover crop biomass and N uptake for low and moderate biomass and N uptake ranges (0-3000 kg ha-1 and 0-100 kg N ha-1). The SAVI estimated cover crop biomass and N uptake were +/- 15% of observed value. Compared to commonly used spatial interpolation methods such as ordinary kriging (OK) and inverse distance weighting (IDW), using the SAVI method showed higher prediction R2 values than that of OK and IDW. An additional advantage for these remote sensing vegetation indices, especially in the context of diverse agronomic management practices, is their much lower labor requirements compared to the high density ground samples needed for a spatial interpolation analysis. </div><div>In the second study, a new approach using the multivariate spatial autoregressive (MSAR) model was developed at 10-m grid resolution to forecast maize yield using historical grain yield data collected at farmers’ fields in Central Indiana, publicly available Landsat imagery, top 30 cm soil organic matter and elevation, while accounting for yield spatial autocorrelation. Relative mean error (RME) and relative mean absolute error (RMAE) were used to quantify the model prediction accuracy at the field level and 10-m grid level, respectively. The MSAR model performed reasonably well (absolute RME < 15%) for field overall yield predictions in 32 out of 35 site-years on the calibration dataset with an average absolute RME of 6.6%. The average RMAE of the MSAR model predictions was 13.1%. It was found that the MSAR model could result in large estimation error under an extreme stressed environment such as the 2012 drought, especially when grain yield under these stressed conditions was not included in the model calibration step. In the validation dataset (n=82), the MSAR model showed good prediction accuracy overall (± 15% of actual yield in 56 site-years) in new fields when extreme stress was not present. The novel approach developed in this study demonstrated its ability to use elevation and soil information to interpret satellite observations accurately in a fine spatial scale. </div><div>Then we incorporated the MSAR approach into a process-based N transformation model to predict field-scale maize yield in Indiana. Our results showed that the linear agreement of predicted yield (using the N Model in the Mapwindow GIS + MMP Tools) to actual yield improved as the spatial aggregation scale became broader. The proposed MSAR model used early vegetative precipitation, top 30 cm soil organic matter and elevation to adjust the N Model yield prediction in 10-m grids. The MSAR adjusted yield predictions resulted in more cases (77%) that fell within 15% of actual yield compared to the N Model alone using the calibration dataset (n=35). However, if the 2012 data was not included in the MSAR parameter training step, the MSAR adjusted yield predictions for 2012 were not improved from the N Model prediction (average RME of 24.1%). When extrapolating the MSAR parameters developed from 7 fields to a dataset containing 82 site-years on 30 different fields in the same region, the improvement from the MSAR adjustment was not significant. The lack of improvement from the MSAR adjustment could be because the relationship used in the MSAR model was location specific. Additionally, the uncertainty of precipitation data could also affect the relationship. </div><div>Through the sequence of these studies, the potential utility of big data routinely collected at farmers’ fields and publicly available satellite imagery has been greatly improved for field-specific management tools and on-farm decision-making. </div>

[ES] Aunque la quinua (Chenopodium quinoa W.) se venía cultivando por los pueblos andinos desde la época preincaica, durante la segunda mitad del siglo XX, por diversas razones, fue postergado, siendo abandonado y casi olvidado en los lugares en los que se domesticó originalmente, originando así una serie de consecuencias sobre los sistemas productivos y comportamiento sociocultural de los pobladores andinos. Sin embargo, las cualidades intrínsecas de su grano como alimento, unido a cambios en los hábitos de consumo en países desarrollados, provocaron un interés creciente por su cultivo, pasando de ser un producto de autoconsumo, en un ámbito geográfico y cultural muy limitado, a ser un producto estrella de exportación; el rápido incremento de la demanda ha provocado un ajuste en los modelos productivos de la región y generando así impactos multidimensionales. En la presente investigación se ha analizado el impacto generado por el boom de quinua en los sistemas productivos agrarios de las zonas en las que su cultivo se había mantenido de forma tradicional. De acuerdo con los objetivos planteados, se ha realizado, en primer lugar, un diagnóstico y desarrollo de la importancia del cultivo sobre la población andina peruana y el papel desempeñado en los sistemas agrarios tradicionales del ande peruano. En segundo lugar, se ha realizado un análisis de la evolución de las variables que determinan la producción y distribución del grano en los últimos 70 años; se han identificado periodos de subutilización, recuperación y expansión en el contexto nacional y a nivel de los departamentos productores; hemos centrado nuestro análisis en dos escenarios extremos de superficie. Con las variables de comercialización se ha determinado la importancia económica de las zonas productoras, la penetración en nuevo mercados y su contribución sobre la economía nacional y, algunos cambios estructurales, para lo que hemos realizado un estudio de caso en las comunidades andinas de Cusco; los resultados de la encuesta nos han permitido observar la situación actual de los agricultores en el contexto del auge de quinua desde la dimensión social, tecnológica y económica; mostrando un panorama limitado y precario por las condiciones geográficas y medios productivos para el mantenimiento del cultivo en las condiciones que garanticen la sostenibilidad y el mantenimiento de la diversidad propia de los sistemas agrarios tradicionales de la zona andina del Perú. Así mismo, el indicador de sustentabilidad ambiental y social son similares y superiores al umbral, mientras que el económico es inferior, influyendo sobre el índice general de sustentabilidad. El análisis de los resultados económicos en términos absolutos de las distintas alternativas de tecnología productiva en diversos escenarios económicos muestra que, antes del boom de quinua, los agricultores prácticamente carecían de alicientes para introducir transformaciones tecnológicas importantes en sus procesos productivos tradicionales, conservando la tecnología productiva que permitía la sostenibilidad del cultivo, mientras que hoy en día los retornos de un sistema productivo basado en la productividad de variedades mejoradas genéticamente e insumos externos de las explotaciones son muy elevados y suponen un aliciente importante para el abandono de los sistemas productivos tradicionales, más o menos evolucionados, que corren el riesgo de ser abandonados, perdiéndose parte de la capacidad de estos sistemas tradicionales de ser sostenibles y adaptarse a los retos del cambio climático. El análisis de simulación efectuado sobre los resultados monetarios en diversos escenarios económicos y tecnológicos nos permite disponer de una base cuantitativa sobre la que estimar un modelo de compensación equilibrada que permita el mantenimiento de los sistemas tradicionales de producción con su demostrada capacidad para adaptarse a diversos escenarios agroclimáticos. / [CA] Encara que la quinoa (Chenopodium quinoa W.) s'havia cultivat pels pobles andins des de l'època preincaica, durant la segona meitat del segle XX, per diverses raons va ser postergat, sent abandonat i quasi oblidat als llocs en els quals es va domesticar originàriament, causant així una sèrie de conseqüències sobre els sistemes productius i comportament sociocultural dels pobladors andins. No obstant això, les qualitats intrínseques del seu gra com a aliment, unit a canvis en els hàbits de consum en països desenvolupats van provocar un interés creixent pel seu cultiu, passant de ser un producte d'autoconsum, en un àmbit geogràfic i cultural molt limitat, a ser un producte estrela d'exportació; el ràpid increment de la demanda ha provocat un ajust en els models productius de la regió i generant així impactes multidimensionals. En la present investigació s'ha analitzat l'impacte generat pel boom de quinoa en els sistemes productius agraris de les zones en les quals el seu cultiu s'havia mantingut de manera tradicional. D'acord els objectius plantejats, s'ha realitzat, en primer lloc, un diagnòstic i desenvolupament de la importància del cultiu sobre la població andina peruana i el paper exercit en els sistemes agraris tradicionals de la serralada dels Andes del Perú. En segon lloc s'ha elaborat una anàlisi de l'evolució de les variables que determinen la producció i distribució del gra en els últims 70 anys; s'han identificat períodes de subutilizació, recuperació i expansió en el context nacional i a escala dels departaments productors; hem centrat la nostra anàlisi en dos escenaris extrems de superfície. Amb les variables de comercialització s'ha determinat la importància econòmica de les zones productores, la penetració en nou mercats i la seua contribució sobre l'economia nacional i, alguns canvis estructurals, per al que hem realitzat un estudi de cas en les comunitats andines de Cusco; els resultats de l'enquesta ens han permés observar la situació actual dels agricultors en el context de l'auge de quinoa des de la dimensió social, tecnològica i econòmica; mostrant un panorama limitat i precari per les condicions geogràfiques i mitjans productius per al manteniment del cultiu en les condicions que garantisquen la sostenibilitat i el manteniment de la diversitat pròpia dels sistemes agraris tradicionals de la zona andina del Perú. Així mateix, l'indicador de sustentabilitat ambiental i social són similars i superiors al llindar, mentre que el d'econòmic és inferior, influint sobre l'índex general de sostenibilitat. L'anàlisi dels resultats econòmics en termes absoluts de les diferents alternatives de tecnologia productiva en diversos escenaris econòmics mostra que, abans del boom de quinoa, els agricultors pràcticament mancaven d'al·licients per a introduir transformacions tecnològiques importants en els seus processos productius tradicionals, conservant la tecnologia productiva que permetia la sostenibilitat del cultiu, mentre que hui dia els retorns d'un sistema productiu basat en la productivitat de varietats millorades genèticament i inputs externs de les explotacions són molt elevats i suposen un al·licient important per a l'abandó dels sistemes productius tradicionals, més o menys evolucionats, que corren el risc de ser abandonats, perdent-se part de la capacitat d'aquests sistemes tradicionals ser sostenibles i adaptar-se als reptes del canvi climàtic. L'anàlisi de simulació efectuada sobre els resultats monetaris en diversos escenaris econòmics i tecnològics ens permet disposar d'una base quantitativa sobre la qual estimar un model de compensació equilibrada que permeta el manteniment dels sistemes tradicionals de producció amb la seua demostrada capacitat per a adaptar-se a diversos escenaris agroclimàtics. / [EN] Although quinoa (Chenopodium quinoa W.) had been cultivated by the Andean Peoples since pre-Inca times, its farming was postponed during the second half of the 20th century for various reasons. In fact, it was abandoned and almost forgotten in the places where it was originally domesticated what has caused a series of consequences on the productive systems and sociocultural behaviour of the Andean inhabitants. Nevertheless, the intrinsic qualities of its grain as a food supply, together with changes in consumption habits in developed countries, has provoked a growing interest in its cultivation. From being a product of self-consumption in a very limited geographical and cultural scope to becoming a star product to export. The quick increase in its demand has caused an adjustment in the productive models of the region and that has generated multidimensional impacts. In the present research, the impact generated by the quinoa's boom on the agricultural production system of the traditionally-maintained cultivation areas has been analysed. According to the objectives considered on this work, a diagnosis and development of the importance of the crop on the Peruvian population has firstly been made, same with the role played in the traditional agrarian systems of the Peruvian Andes. Secondly, an analysis of the evolution of the variables that determine the production and distribution of the grain in the last 70 years has been carried out. The periods of underutilization, recovery and expansion have been also identified in both the national context and at the level of the producing departments. We have focused our analysis on two extreme surface scenarios. With the commercial variables, the economic importance of the producing areas has been determined, same with the penetration of new markets and their contribution to the national economy and some structural changes, for which we have conducted a case study in the Andean communities of Cusco. The results of the conducted survey have allowed us to observe the current farmers' situation in the context of the quinoa's boom. This has showed a limited and precarious panorama due to geographical conditions and productive means which keep the crop in the conditions that guarantee sustainability and maintenance of the diversity of the traditional agrarian systems of the Andes in Peru. Moreover, the environmental and social sustainability indicators are similar at the same time they both exceed the threshold whereas the economic indicator is lower influencing on the overall sustainability index. The analysis of the economic results in absolute terms of the different alternatives of productive technology in various economic scenarios shows that, before the quinoa's boom, farmers had virtually no incentive to introduce major technological transformations into their traditional production processes preserving the productive technology that allowed the sustainability of the crop whereas nowadays, the returns of a production system based on the productivity of genetically-improved varieties and external inputs from farms are really higher. They represent an important incentive for abandoning traditional production systems, more or less evolved, which run the risk of being abandoned what provokes a loss of some of the capacity of these traditional systems to be sustainable and to be adapted to the climate change. The simulation analysis carried out on the monetary results in various economic and technological scenarios allows us to have a quantitative basis on which estimate a balanced compensation model allowing the maintenance of traditional production systems with its proven ability to be adapted to various agroclimatic scenarios. / Huillca Quispe, J. (2022). Valoración socioeconómica del impacto de la expansión del cultivo de Quinua (Chenopodium quinoa W.) sobre la competitividad y sostenibilidad de la diversidad en las explotaciones tradicionales del Perú [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/181509 / TESIS

<p>Cereal rye (<i>Secale cereale</i>, L., CR) is the most commonly utilized cover crop species within the United States. Yet, the total land area planted to CR on an annual basis remains relatively low despite its numerous proven environmental benefits. The relatively low rates of CR adoption could be due to a dearth of knowledge surrounding certain agronomic and economic components of CR adoption. Currently, there exists knowledge gaps within the scientific literature regarding CR performance, N cycling, and associated economic risk. <a>Thus, to address the above-mentioned knowledge gaps, three individual studies were developed to: i) investigate the fate of scavenged CR nitrogen (N) amongst soil N pools, ii) assess the suitability of visible-spectrum vegetation indices (VIs) to predict CR biomass and nutrient accumulation (BiNA), and iii) characterize the economic risk of CR adoption at a regional scale over time.</a></p> <p>In the first study, ¹⁵N, a stable isotope of N, was used in an aerobic incubation to track the fate of CR root and shoot N among the soil microbial biomass, inorganic, and organic N pools, as well as explore CR N bioavailability over a simulated corn growing season. In this study, the C:N ratio of the shoot residues was 16:1 and the roots was 31:1 and differences in residue quality affected the dynamics of CR N release from each residue type. On average, 14% of whole plant CR N was recovered in the soil inorganic N pool at the final sample date. Correspondingly, at the final sampling date 53%, 33%, and less than 1% of whole plant CR N was recovered as soil organic N, undecomposed residue, and as microbial biomass N, respectively. Most CR N remained unavailable to plants during the first cash crop growing season subsequent to termination. This knowledge could support the advancement of N fertilizer management strategies for cropping systems containing cereal rye.</p> <p>In the second study, a commercially available unmanned aerial vehicle (UAV) outfitted with a standard RGB sensor was used to collect aerial imagery of growing CR from which visible-spectrum VIs were computed. Computed VIs were then coupled with weather and geographic data using linear multiple regression to produce prediction models for CR biomass, carbon (C), N, phosphorus (P), potassium (K), and sulfur (S). Five visible-spectrum VIs (Visible Atmospherically Resistant Index (VARI), Green Leaf Index (GLI), Modified Green Red Vegetation Index (MGRVI), Red Green Blue Vegetation Index (RGBVI), and Excess of Green (ExG)) were evaluated and the results determined that MGRVI was the best predictor for CR biomass, C, K, and S and that RGBVI was the best predictor for CR N and P. Furthermore, the final prediction models for the VIs selected as the best predictors developed in this study performed satisfactorily in the prediction of CR biomass, C, N, P, K, and S producing adjusted R² values of 0.79, 0.79, 0.75, 0.81, 0.81, and 0.78, respectively. The results of this study have the potential to aid producers in making informed decisions regarding CR and fertility management. </p> <p>In the final study, agronomic data for corn and soybean cropping systems with and without CR was collected from six states (Illinois, Indiana, Iowa, Minnesota, Missouri, and Wisconsin) and used within a Monte-Carlo stochastic simulation to characterize the economic risk of adopting CR at a regional scale over time. The results of this study indicate that average net returns to CR are always negative regardless of CR tenure primarily due to added costs and increased variability in cash crop grain yields associated with CR adoption. Further, the results demonstrate that the additional risk assumed by adopting CR is not adequately compensated for with current CR adoption incentive programs and that the risk premium necessary can be 1.7 to 15 times greater than existing incentive payments. Knowledge gained from this study could be used to reimagine current incentive programs to further promote adoption of CR.</p>

<p> Soybean seed cost has dramatically increased in recent decades which has led to producer interest in lowering input cost through reductions in seeding rate. Fifty-eight seeding rate trials of soybean were conducted at field-scale in Indiana from 2010 to 2021 to update recommendations of seeding rates and plant population. The objectives were to determine the agronomic optimal seeding rate (AOSR) and plant population (AOPP) based on planting equipment, tillage practices, and planting date. Economic optimal seeding rate (EOSR) was also determined based on these field scenarios. Harvest AOPP was not influenced by planting equipment (~212,000 plants ha-1) or tillage (~239,000 plants ha-1), but AOSR varied. Soybean seeded with a row-crop planter optimized grain yield with 352,600 seeds ha-1; whereas, the grain drill required 75,200 more seeds ha-1. Soybean seeded into conventional tillage maximized grain yield at 380,400 seeds ha-1; whereas, under no-till conditions 41,400 more seeds ha-1 were required. Timely planting required 417,300 seeds ha-1 to optimize grain yield, which resulted in harvest AOPP of 216,700 plants ha-1. Conversely, late plantings required 102,800 fewer seeds ha-1 but 36,200 more plants ha-1 than timely planting. Depending on seed cost and soybean market price, seeding rates could be reduced 13,700 to 92,800 seeds ha-1 below AOSR to maximize profit.</p> <p>Secondly, digital imagery with high spatial resolution was collected with an unmanned aerial vehicle (UAV) to develop a simple and practical method to segment soybean from non-plant pixels. The best vegetation indices were selected to segment young soybean plants (VC to V6). Two field-scale trials of soybean were planted in 2020 with the agronomic trial design of two varieties x five seeding rates with three replications. The imagery was collected during this period as it coincides with the time for determining whether a soybean stand should be replanted. Five relative vegetative indices based on the red, green, and blue (RGB) imagery were evaluated: excess greenness index (ExG), excess redness index (ExR), green leaf index (GLI), normalized green-red difference index (NGRDI) and visible atmospheric resistance index (VARI). Both GLI and ExG were superior in overall accuracy compared to all other vegetative indices with very small soybean plants (VC to V1 growth stages). VARI and NGRDI had relatively poor overall accuracy at VC and V1, but had similar overall accuracy to GLI as soybean plants grew larger (V2 to V6 growth stages). Across all growth stages and locations, ExR performed the poorest. Moreover, GLI had consistent performance across the range of growth stages, suggesting its suitability for early soybean stand assessment methods.</p> <p>Six field-scale trials were established in 2020 and 2021 in Indiana with two varieties seeded from 123,000 to 618,000 seeds ha-1. Canopy cover was calculated using GLI to create binary segmentation of plant pixels and non-plant pixels. UAV-derived canopy cover measurements were correlated with plant population of soybean from VC to V4 and growing degree days (GDD) after planting. Yield potential (75, 80, 85, 90, 95, 100%) was correlated with canopy cover from VC to V4 and GDD after planting. Canopy cover of 2.1, 5.0, 8.9 and 13.8% by 150, 250, 350, and 450 GDD°C after planting, respectively, would maximize yield. Canopy cover for 75% yield potential was one-fourth as much as the 100% yield potential. Recommended threshold for replant decisions should be based on canopy cover to attain 95% yield potential. Field observations below a canopy cover of 1.8, 4.2, 7.4, and 11.5% canopy cover by 150, 250, 350, and 450 GDD°C after planting respectively, would consider replanting. </p>