Precision irrigation trends and perspectives: a review / Tendências e perspectivas da irrigação de precisão: uma revisão

In recent decades, research on precision irrigation driven by climate change has developed a multitude of strategies, methods and technologies to reduce water consumption in irrigation projects and to adapt to the increasing occurrence of water scarcity, agricultural droughts and competition between agricultural and industrial sectors for the use of water. In this context, the adoption of water-saving and application practices implies a multidisciplinary approach to accurately quantify the water needs of crops under different water availability and management practices. Thus, this review article presented a review of technologies and new trends in the context of precision irrigation, future perspectives and critically analyze notions and means to maintain high levels of land and water productivity, which minimize irrational water consumption at the field level.

Nas últimas décadas pesquisas voltadas à irrigação de precisão, impulsionadas pelas mudanças climáticas, desenvolveram uma infinidade de estratégias, métodos e tecnologias para reduzir o consumo de água em projetos de irrigação, para adaptação à crescente ocorrência de escassez de água, secas agrícolas e competição entre os setores agrícolas e industriais pelo uso da água. Nesta conjuntura, a adoção de práticas de economia e aplicação de água, implica em uma abordagem multidisciplinar para a quantificação precisa das necessidades de água das culturas, sob diversas práticas de disponibilidade e manejo da água. Dessa forma, este artigo de revisão tem como objetivo apresentar uma revisão sobre as tecnologias e novas tendências no contexto da irrigação de precisão, as perspectivas futuras e analisar criticamente noções e meios para manter altos índices de produtividade da terra e da água, que minimizem o consumo de água irracional a nível de campo.

Detection of coffee fruits on tree branches using computer vision

Coffee farmers do not have efficient tools to have sufficient and reliable information on the maturation stage of coffee fruits before harvest. In this study, we propose a computer vision system to detect and classify the Coffea arabica (L.) on tree branches in three classes: unripe (green), ripe (cherry), and overripe (dry). Based on deep learning algorithms, the computer vision model YOLO (You Only Look Once), was trained on 387 images taken from coffee branches using a smartphone. The YOLOv3 and YOLOv4, and their smaller versions (tiny), were assessed for fruit detection. The YOLOv4 and YOLOv4-tiny showed better performance when compared to YOLOv3, especially when smaller network sizes are considered. The mean average precision (mAP) for a network size of 800 × 800 pixels was equal to 81 %, 79 %, 78 %, and 77 % for YOLOv4, YOLOv4-tiny, YOLOv3, and YOLOv3-tiny, respectively. Despite the similar performance, the YOLOv4 feature extractor was more robust when images had greater object densities and for the detection of unripe fruits, which are generally more difficult to detect due to the color similarity to leaves in the background, partial occlusion by leaves and fruits, and lighting effects. This study shows the potential of computer vision systems based on deep learning to guide the decision-making of coffee farmers in more objective ways.

Methodology to filter out outliers in high spatial density data to improve maps reliability

The considerable volume of data generated by sensors in the field presents systematic errors; thus, it is extremely important to exclude these errors to ensure mapping quality. The objective of this research was to develop and test a methodology to identify and exclude outliers in high-density spatial data sets, determine whether the developed filter process could help decrease the nugget effect and improve the spatial variability characterization of high sampling data. We created a filter composed of a global, anisotropic, and an anisotropic local analysis of data, which considered the respective neighborhood values. For that purpose, we used the median to classify a given spatial point into the data set as the main statistical parameter and took into account its neighbors within a radius. The filter was tested using raw data sets of corn yield, soil electrical conductivity (ECa), and the sensor vegetation index (SVI) in sugarcane. The results showed an improvement in accuracy of spatial variability within the data sets. The methodology reduced RMSE by 85 %, 97 %, and 79 % in corn yield, soil ECa, and SVI respectively, compared to interpolation errors of raw data sets. The filter excluded the local outliers, which considerably reduced the nugget effects, reducing estimation error of the interpolated data. The methodology proposed in this work had a better performance in removing outlier data when compared to two other methodologies from the literature.

Methodology to filter out outliers in high spatial density data to improve maps reliability

The considerable volume of data generated by sensors in the field presents systematic errors; thus, it is extremely important to exclude these errors to ensure mapping quality. The objective of this research was to develop and test a methodology to identify and exclude outliers in high-density spatial data sets, determine whether the developed filter process could help decrease the nugget effect and improve the spatial variability characterization of high sampling data. We created a filter composed of a global, anisotropic, and an anisotropic local analysis of data, which considered the respective neighborhood values. For that purpose, we used the median to classify a given spatial point into the data set as the main statistical parameter and took into account its neighbors within a radius. The filter was tested using raw data sets of corn yield, soil electrical conductivity (ECa), and the sensor vegetation index (SVI) in sugarcane. The results showed an improvement in accuracy of spatial variability within the data sets. The methodology reduced RMSE by 85 %, 97 %, and 79 % in corn yield, soil ECa, and SVI respectively, compared to interpolation errors of raw data sets. The filter excluded the local outliers, which considerably reduced the nugget effects, reducing estimation error of the interpolated data. The methodology proposed in this work had a better performance in removing outlier data when compared to two other methodologies from the literature.(AU)

Methodology to filter out outliers in high spatial density data to improve maps reliability

ABSTRACT The considerable volume of data generated by sensors in the field presents systematic errors; thus, it is extremely important to exclude these errors to ensure mapping quality. The objective of this research was to develop and test a methodology to identify and exclude outliers in high-density spatial data sets, determine whether the developed filter process could help decrease the nugget effect and improve the spatial variability characterization of high sampling data. We created a filter composed of a global, anisotropic, and an anisotropic local analysis of data, which considered the respective neighborhood values. For that purpose, we used the median to classify a given spatial point into the data set as the main statistical parameter and took into account its neighbors within a radius. The filter was tested using raw data sets of corn yield, soil electrical conductivity (ECa), and the sensor vegetation index (SVI) in sugarcane. The results showed an improvement in accuracy of spatial variability within the data sets. The methodology reduced RMSE by 85 %, 97 %, and 79 % in corn yield, soil ECa, and SVI respectively, compared to interpolation errors of raw data sets. The filter excluded the local outliers, which considerably reduced the nugget effects, reducing estimation error of the interpolated data. The methodology proposed in this work had a better performance in removing outlier data when compared to two other methodologies from the literature.

Hairy vetch role to mitigate crop yield gap in different yield environments at field level

The spatial variability of soil organic matter, plant water availability, and soil nitrogen (N) availability are drivers of crop response to mineral N fertilizer. The complex interaction of these factors is responsible for within-field corn and wheat yield variability. The hairy vetch (HV) cover crop is an economic, environmentally friendly, and efficient N source capable of conciliating crop yield and soil health. Nevertheless, the HV effects to mitigate yield gap of management zone (MZ) have not yet been documented. This study evaluated the effects of HV and mineral N fertilization, in single or combined input, on alleviating crop yield gap. The study was carried out in two croplands southern Brazil. The experimental design was a complete randomized block in a split plot having MZ (high, medium, and low yield) and N fertilizer rates. Wheat and corn N uptake and grain yield had a quadratic adjustment with N fertilizer input, but there was a significant MZ effect, where low yield zone (LYZ) was less responsive. Consequently, mineral N fertilization as a single input to mitigate the yield gap in this MZ was not efficient. On the other hand, HV increased corn N uptake and grain yield mainly in LYZ compared to MYZ and HYZ. HV fully mitigated the yield gap between MYZ and HYZ. The combined use of HV and mineral N fertilization rate adjusted according to N legume credit and MZ was an efficient strategy to boost yield, favoring soil health and environmental protection.

Sugarcane yield and quality using soil magnetic susceptibility

The concept of production environments, which is widely used to classify the yield potential of soils, and magnetic susceptibility (MS), is emerging as an important tool for mapping ultra-detailed areas. Given this background, this paper aims to evaluate the use of MS as a tool for the identification of areas with different potential the enhancing of sugarcane yield and quality, and the allocation of varieties. An area of 445 ha was sampled at 1 point every 7 ha, and 14 points were determined for stratified sampling following the top of the landscape. Particle size and MS of samples at depths of 0.0-0.2 and 0.2-0.4 m were analyzed. The data on yield and quality of raw material were obtained from a nine crop season database and biometry performed in the 2018/19 crop season. The multivariate analysis of historical results showed the formation of three groups with different yield and quality potential, with a difference of up to 17.28 mg of cane per hectare between the group with the highest and lowest potential, based on soil MS. An analysis of the performance of the varieties involved showed that MS is effective in identifying areas with different potential for sugarcane yield and quality, differentiating by up to 34.5 % the performance of the same variety in different MS classes and by up to 38.5 % the performance of different varieties in similar MS classes. Thus, MS is an effective tool for identifying areas with different potential for sugarcane yield and quality, and can be used for allocating varieties in the field.

Citrus orchards under formation evaluated by UAV-Based RGB Imagery

Few studies have investigated the biometric attributes of citrus orchards under formation that use RGB sensors on board unmanned aerial vehicles (UAV) and the challenges are great. This study aimed to develop and validate a method of using aerial UAV images by automated routines to evaluate the biometric attributes of a crop of 'Tahiti' acid lime under formation. We used a multirotor UAV, programmed to capture images at three different map scales, with a frontal and side overlap of 80 %. Geoprocessing was carried out both with and without ground control points on each scale. An automated routine was developed in an open-source environment, consisting of three processing phases: i) Estimation of the plant biometric attributes, ii) Statistical analysis, and iii) Statistical Report Map (SRM). The use of the developed routine allowed to delimit and estimate the crown projection area with an accuracy of more than 95 % as well as identify and quantify the plants with an accuracy of over 97 %. The use of ground control points during the processing stage does not increase accuracy in estimating the biometric attributes under evaluation. On the other hand, map scale is strongly correlated with the quality of the estimates, especially plant height. The results allowed to define a method for the acquisition and analysis of aerophotogrammetric data using a UAV, which can be used to measure the plant biometric attributes under analysis and the method can be easily adapted to perennial crops.

Correlation between vegetation indexes generated at Vitis Vinifera L. and soil, plant and production parameters for emergency application in decision making / Correlação entre indices de vegetação gerados em Vitis Vinifera L. e parâmetros de solo, planta e produção para aplicação emergencial na tomada de decisão

Correlation between proximal sensing techniques and laboratory results of qualitative variables plus agronomic attributes was evaluated of a 3,0 ha vineyard in the county of Muitos Capões, Northeast of Rio Grande do Sul State, Brazil, in Vitis vinifera L. at 2017/2018 harvest, aiming to evaluate the replacement of conventional laboratory analysis in viticulture by Vegetation Indexes, at situations were laboratory access are unavailable. Based on bibliographic research, looking for vegetative indexes developed or used for canopy reflectance analysis on grapevines and whose working bands were within the spectral range provided by the equipment used, a total of 17 viable candidates were obtained. These chosen vegetation indices were correlated, through Pearson (5%), with agronomic soil attributes (apparent electrical conductivity, clay, pH in H2O, phosphorus, potassium, organic matter, aluminum, calcium, magnesium, effective CTC, CTC at pH 7.0, zinc, copper, sulfur and boron) for depths 0 -20 cm and 20-40 cm, and plant tissue (Nitrogen, phosphorus, potassium, calcium, magnesium, sulfur, copper, zinc, iron, manganese and boron) , in addition to some key oenological and phytotechnical parameters for the quantification of wine production and quality. One hundred and thirty ninesignificant correlations were obtained from this cross, with 36 moderate coefficients between 19 parameter variables versus 12 of the indexes. We concluded that in cases where access or availability of laboratory analyzes is difficult or impracticable, the use of vegetation indices is possible if the correlation coefficients reach, at least, the moderate magnitude, serving as a support to decision making until the lack analytical structure to be remedied.

Avaliou-se a correlação entre as técnicas de sensoriamento proximal e os resultados laboratoriais de variáveis qualitativas, mais os atributos agronômicos do solo de um vinhedo de 3,0 ha no município de Muitos Capões, região nordeste do estado do Rio Grande do Sul, Brasil, na safra 2017/2018. Objetivou avaliar a substituição das análises laboratoriais convencionais em viticultura por Índices de Vegetação, em situações de indisponibilidade de acesso ao laboratório. Com base em pesquisa bibliográfica, buscaram-se índices vegetativos desenvolvidos ou utilizados para análise de refletância de dossel em videiras e cujas bandas de trabalho estavam dentro do intervalo espectral fornecido pelo equipamento utilizado, obtendo-se um total de 17 candidatos viáveis. Esses índices de vegetação escolhidos foram correlacionados, por meio de Pearson (5%), com atributos agronômicos do solo (condutividade elétrica aparente, argila, pH em H2O, fósforo, potássio, matéria orgânica, alumínio, cálcio, magnésio, CTC efetivo, CTC em pH 7,0, zinco, cobre, enxofre e boro) para profundidades de 0 - 20 cm e 20 - 40 cm, e tecido vegetal (nitrogênio, fósforo, potássio, cálcio, magnésio, enxofre, cobre, zinco, ferro, manganês e boro), além de alguns parâmetros enológicos e fitotécnicos essenciais para a quantificação da produção e qualidade do vinho. Deste cruzamento foram obtidas 139 correlações significativas, resultando 36 coeficientes moderados entre 19 variáveis de parâmetros versus 12 dos índices. Concluímos que nos casos em que o acesso ou disponibilidade de análises laboratoriais é difícil ou impraticável, a utilização de índices de vegetação é possível, desde que os coeficientes de correlação atinjam, pelo menos, a magnitude moderada, servindo como suporte para a tomada de decisão até a falta de estrutura analítica ser remediada.

Spatial variability of soil penetration resistance in the lowland area cultivated with soybean / Variabilidade espacial da resistência do solo à penetração em área de várzea cultivada com soja

This study determined the spatial variability of soil penetration resistance and yield of the soybean crop in lowland areas. The soil resistance to penetration at four different depths (0 to 0.10 m; 0.11 to 0.20 m; 0.21 to 0.30 m and 0.31 to 0.40 m), volumetric humidity of the soil at two depths (0 to 0.20 m and 0.21 to 0.40 m) and soybean yield were determined in an area of 1.13 hectares, using a sample mesh of 10 x 10 m. The corresponding data were subjected to descriptive statistical analysis. Pearsons simple linear correlation analysis (p0.05) was conducted and the spatial dependence was assessed by analyzing the isotropic semivariograms using spherical, exponential, linear, and Gaussian models. The results showed that the soil penetration resistance increased with depth, with restrictive values to root growth between 0.05 and 0.35 m. There was no correlation between yield and soil penetration resistance, and the semivariograms did not show a defined ascending region regarding the soil penetration resistance data. For the conditions under which the experiment was conducted, the sample 10 x 10 m mesh was suitable for assessing the spatial variability of soil resistance to penetration in depths exceeding 0.10 m.(AU)

Este trabalho teve como objetivo identificar a variabilidade espacial da resistência do solo à penetração e na produtividade da cultura da soja, em área de várzea. Foram realizadas determinações de resistência do solo à penetração, em quatro profundidades (0 a 0,10 m; 0,11 a 0,20 m; 0,21 a 0,30 m e 0,31 a 0,40 m); umidade volumétrica do solo, em duas profundidades (0 a 0,20 m e 0,21 a 0,40 m); e produtividade da soja em uma área de 1,13 hectares, utilizando-se malha amostral de 10 x 10 m. Os dados foram submetidos à análise estatística descritiva. Realizou-se análise de correlação linear simples de Pearson (p0,05) e a dependência espacial foi avaliada pela análise de semivariogramas isotrópicos, utilizando os modelos: esférico, exponencial, linear e gaussiano. Os resultados indicaram que, a resistência do solo à penetração aumentou em profundidade, com valores restritivos ao crescimento radicular entre 0,05 e 0,35 m. Não se obteve correlação entre produtividade e resistência do solo à penetração sendo que, para os dados de resistência do solo à penetração, os semivariogramas não apresentaram uma região ascendente definida. Para as condições em que o experimento foi realizado, a malha amostral de 10 x 10 m utilizada foi adequada para avaliar a variabilidade espacial da resistência do solo à penetração em profundidades superiores a 0,10 m.(AU)

Evaluation of the nutritional status of corn by vegetation indices via aerial images / Avaliação do estado nutricional do milho por índices de vegetação de imagens aéreas

ABSTRACT: The objective of this study is to determine the vegetation indices (IV) as a means of identifying the nutritional status of corn, with respect to the soil nitrogen and potassium, using the aerial images received through an RGB camera loaded on an unmanned aerial vehicle. The images were obtained for an experiment of the nitrogen levels (0, 60, 120 and 180 kg ha-1) and potassium levels (0, 50, 100 and 150 kg ha-1), in the random block design, with a factorial scheme of 4 x 4, having three repetitions. Ten leaves were plucked per plot during the flowering phase to assess the total N (NF) and K+ leaf contents. The Pearson's correlation analysis, as well as the analyses of variance and regression between the IV and the concentrations of N and K2O. NF, K+ and the grain yield, responded only to the soil N levels. A significant correlation was observed for the indices of Red Index, Normalized Difference Index and Visible Atmospherically Resistant Index with the NF, which endorses them as favorable in identifying the nutritional standing of corn, with respect to the N level. Not even a single one of the indices evaluated could detect the nutritional ranking of corn in the context of the potassium level.

RESUMO: O estudo teve como objetivo avaliar índices de vegetação (IV) para detecção do status nutricional do milho com relação ao nitrogênio e potássio por meio de imagens aéreas obtidas por câmera RGB embarcada em veículo aéreo não tripulado. As imagens foram adquiridas em ensaio de níveis de nitrogênio (0, 60, 120 e 180 kg ha-1) e potássio (0, 50, 100 e 150 kg ha-1), em blocos ao acaso, fatorial 4 x 4, com três repetições. Coletaram-se dez folhas por parcela na fase de florescimento para avaliação do teor foliar de N total (NF) e K+. Efetuou-se análise de correlação de Pearson, análise de variância e de regressão entre os IV e os níveis de N e de K2O. NF, K+ e a produtividade de grãos responderam apenas aos níveis de N no solo. Houve correlação significativa para os índices Excess Red Index, Normalized Difference Index e Visible Atmospherically Resistant Index com o NF, que os credencia como promissores na detecção do status nutricional do milho em relação ao N. Nenhum dos índices avaliados foi capaz de detectar o status nutricional do milho com relação ao potássio.

Diagrammatic scales for the estimation of black node disease severity in common bean / Escalas diagramáticas para a estimativa da gravidade da doença do nó negro no feijão comum

The objective of this study was to design two diagrammatic scales for the evaluation of black node disease (Stagonosporopsis hortensis and Boeremia spp.) in common bean (Phaseolus vulgaris L.). The developed scales corresponded to logarithmic intervals (SADL) of seven levels and equal intervals (SADE) of nine levels. These scales were compared to the scale developed by the International Center for Tropical Agriculture (CIAT), which corresponds to a descriptive scale (NoSAD). In the scale validation the accuracy, precision, and concordance of repeatability were determined using Lins concordance correlation coefficient, whereas inter-rater reproducibility was determined by the overall concordance correlation coefficient (OCCC). It was observed that with the use of the proposed scales, reproducibility and repeatability were significantly improved for inexperienced raters, and reproducibility was improved in the case of experienced raters. Thus, the designed standard area diagrams with equal and logarithmic intervals are a useful tool for estimating severity under field and experimental conditions as part of the study of this patho-system.(AU)

O objetivo deste estudo foi delinear duas escalas diagramáticas para avaliação da doença do nó preto (Stagonosporopsis hortensis e Boeremia spp.) em feijoeiro (Phaseolus vulgaris L.). As escalas desenvolvidas corresponderam a intervalos logarítmicos (SADL) de sete níveis e intervalos iguais (SADE) de nove níveis. Essas escalas foram comparadas com a escala desenvolvida pelo Centro Internacional de Agricultura Tropical (CIAT), que corresponde a uma escala descritiva (NoSAD). Na validação da escala, a exatidão, precisão e concordância da repetibilidade foram determinadas usando o coeficiente de correlação de concordância de Lin, enquanto a reprodutibilidade entre avaliadores foi determinada pelo coeficiente de correlação de concordância geral (OCCC). Observou-se que, com o uso das escalas propostas, a reprodutibilidade e a repetibilidade foram significativamente melhoradas para os avaliadores inexperientes, e a reprodutibilidade foi melhorada no caso dos avaliadores experientes. Assim, as escalas diagramáticas projetadas são uma ferramenta útil para estimar a severidade em condições experimentais e de campo como parte do estudo desse sistema patológico.(AU)

Genome editing: propelling the next generation of crop improvement / Edição genômica: impulsionando a nova geração do melhoramento de plantas

Climate change and population size records threaten food security. Therefore, the call for a more sustainable and efficient crop production has never been more urgent. Traditional plant breeding was one of the first successful approaches to expand cultivation areas and crop yield. Later, biotechnological tools and their products, such as genetically modified organisms containing exogenous DNA, further broadened the limits of agricultural results, yet bringing huge financial, bureaucratic, and public rejection hurdles. In the 90s, scientific advances brought the opportunity to drive mutations using engineered nucleases, and since 2013 CRISPR-Cas has emerged as the most practical toolkit to edit genomes. One of the most striking possibilities is to generate edited and non-transgenic plants. In this review, we present the working mechanism behind CRISPR-induced mutations and pinpoint the latest techniques developed, as well as its myriad of applications in agriculture. The enhancing scope of CRISPR ranges from introducing traits of agronomic interest – such as herbicide resistance, resistance/tolerance to biotic and abiotic stresses, and quality and durability of products – to accelerating plant breeding processes, including haploid induction, generating male-sterile lines, fixating hybrid vigor, and overcoming self-incompatibility. We also discuss regulatory issues surrounding edited plants and derived products around the world, challenges that must be overcome, and future prospects to harness all the potential of this amazing tool to guarantee the new crop production revolution.

As mudanças climáticas e números recordes de população ameaçam a segurança alimentar. Portanto, o apelo por uma produção agrícola mais sustentável e eficiente nunca foi tão urgente. O melhoramento genético tradicional foi uma das primeiras abordagens bem-sucedidas para expandir as áreas de cultivo e o rendimento das safras. Posteriormente, ferramentas biotecnológicas e seus produtos, como organismos geneticamente modificados contendo DNA exógeno, ampliaram ainda mais os limites dos resultados agrícolas, apesar de ainda carregarem enormes obstáculos financeiros, burocráticos e de rejeição pública. Na década de 90, os avanços científicos trouxeram a oportunidade de conduzir mutações usando nucleases projetadas e, desde 2013, CRISPR-Cas surgiu como o kit de ferramentas mais prático para editar genomas. Uma das possibilidades mais marcantes é gerar plantas editadas e não transgênicas. Nesta revisão, apresentamos o mecanismo de ação por trás das mutações induzidas por CRISPR, identificando as últimas técnicas desenvolvidas, bem como sua miríade de aplicações na agricultura. O escopo de aprimoramento do CRISPR varia desde introduzir características de interesse agronômico – como resistência a herbicidas, resistência/tolerância a estresses bióticos e abióticos e qualidade e durabilidade de produtos – até acelerar processos de melhoramento genético de plantas, incluindo indução de haploidia, geração de linhagens macho-estéreis, fixação de vigor híbrido e superação da autoincompatibilidade. Também discutimos questões regulatórias em torno de plantas editadas e produtos derivados mundialmente, desafios que devem ser superados e perspectivas futuras para aproveitar todo o potencial desta ferramenta incrível para garantir a nova revolução na produção de culturas agrícolas.

Genome editing: propelling the next generation of crop improvement / Edição genômica: impulsionando a nova geração do melhoramento de plantas

Climate change and population size records threaten food security. Therefore, the call for a more sustainable and efficient crop production has never been more urgent. Traditional plant breeding was one of the first successful approaches to expand cultivation areas and crop yield. Later, biotechnological tools and their products, such as genetically modified organisms containing exogenous DNA, further broadened the limits of agricultural results, yet bringing huge financial, bureaucratic, and public rejection hurdles. In the 90s, scientific advances brought the opportunity to drive mutations using engineered nucleases, and since 2013 CRISPR-Cas has emerged as the most practical toolkit to edit genomes. One of the most striking possibilities is to generate edited and non-transgenic plants. In this review, we present the working mechanism behind CRISPR-induced mutations and pinpoint the latest techniques developed, as well as its myriad of applications in agriculture. The enhancing scope of CRISPR ranges from introducing traits of agronomic interest such as herbicide resistance, resistance/tolerance to biotic and abiotic stresses, and quality and durability of products to accelerating plant breeding processes, including haploid induction, generating male-sterile lines, fixating hybrid vigor, and overcoming self-incompatibility. We also discuss regulatory issues surrounding edited plants and derived products around the world, challenges that must be overcome, and future prospects to harness all the potential of this amazing tool to guarantee the new crop production revolution.(AU)

As mudanças climáticas e números recordes de população ameaçam a segurança alimentar. Portanto, o apelo por uma produção agrícola mais sustentável e eficiente nunca foi tão urgente. O melhoramento genético tradicional foi uma das primeiras abordagens bem-sucedidas para expandir as áreas de cultivo e o rendimento das safras. Posteriormente, ferramentas biotecnológicas e seus produtos, como organismos geneticamente modificados contendo DNA exógeno, ampliaram ainda mais os limites dos resultados agrícolas, apesar de ainda carregarem enormes obstáculos financeiros, burocráticos e de rejeição pública. Na década de 90, os avanços científicos trouxeram a oportunidade de conduzir mutações usando nucleases projetadas e, desde 2013, CRISPR-Cas surgiu como o kit de ferramentas mais prático para editar genomas. Uma das possibilidades mais marcantes é gerar plantas editadas e não transgênicas. Nesta revisão, apresentamos o mecanismo de ação por trás das mutações induzidas por CRISPR, identificando as últimas técnicas desenvolvidas, bem como sua miríade de aplicações na agricultura. O escopo de aprimoramento do CRISPR varia desde introduzir características de interesse agronômico como resistência a herbicidas, resistência/tolerância a estresses bióticos e abióticos e qualidade e durabilidade de produtos até acelerar processos de melhoramento genético de plantas, incluindo indução de haploidia, geração de linhagens macho-estéreis, fixação de vigor híbrido e superação da autoincompatibilidade. Também discutimos questões regulatórias em torno de plantas editadas e produtos derivados mundialmente, desafios que devem ser superados e perspectivas futuras para aproveitar todo o potencial desta ferramenta incrível para garantir a nova revolução na produção de culturas agrícolas.(AU)

Evaluation of the nutritional status of corn by vegetation indices via aerial images / Avaliação do estado nutricional do milho por índices de vegetação de imagens aéreas

The objective of this study is to determine the vegetation indices (IV) as a means of identifying the nutritional status of corn, with respect to the soil nitrogen and potassium, using the aerial images received through an RGB camera loaded on an unmanned aerial vehicle. The images were obtained for an experiment of the nitrogen levels (0, 60, 120 and 180 kg ha-¹) and potassium levels (0, 50, 100 and 150 kg ha-¹), in the random block design, with a factorial scheme of 4 x 4, having three repetitions. Ten leaves were plucked per plot during the flowering phase to assess the total N (NF) and K+ leaf contents. The Pearson’s correlation analysis, as well as the analyses of variance and regression between the IV and the concentrations of N and K2O. NF, K+ and the grain yield, responded only to the soil N levels. A significant correlation was observed for the indices of Red Index, Normalized Difference Index and Visible Atmospherically Resistant Index with the NF, which endorses them as favorable in identifying the nutritional standing of corn, with respect to the N level. Not even a single one of the indices evaluated could detect the nutritional ranking of corn in the context of the potassium level.

O estudo teve como objetivo avaliar índices de vegetação (IV) para detecção do status nutricional do milho com relação ao nitrogênio e potássio por meio de imagens aéreas obtidas por câmera RGB embarcada em veículo aéreo não tripulado. As imagens foram adquiridas em ensaio de níveis de nitrogênio (0, 60, 120 e 180 kg ha-¹) e potássio (0, 50, 100 e 150 kg ha-¹), em blocos ao acaso, fatorial 4 x 4, com três repetições. Coletaram-se dez folhas por parcela na fase de florescimento para avaliação do teor foliar de N total (NF) e K+. Efetuou-se análise de correlação de Pearson, análise de variância e de regressão entre os IV e os níveis de N e de K2O. NF, K+ e a produtividade de grãos responderam apenas aos níveis de N no solo. Houve correlação significativa para os índices Excess Red Index, Normalized Difference Index e Visible Atmospherically Resistant Index com o NF, que os credencia como promissores na detecção do status nutricional do milho em relação ao N. Nenhum dos índices avaliados foi capaz de detectar o status nutricional do milho com relação ao potássio.

Evaluation of the nutritional status of corn by vegetation indices via aerial images / Avaliação do estado nutricional do milho por índices de vegetação de imagens aéreas

The objective of this study is to determine the vegetation indices (IV) as a means of identifying the nutritional status of corn, with respect to the soil nitrogen and potassium, using the aerial images received through an RGB camera loaded on an unmanned aerial vehicle. The images were obtained for an experiment of the nitrogen levels (0, 60, 120 and 180 kg ha-¹) and potassium levels (0, 50, 100 and 150 kg ha-¹), in the random block design, with a factorial scheme of 4 x 4, having three repetitions. Ten leaves were plucked per plot during the flowering phase to assess the total N (NF) and K+ leaf contents. The Pearsons correlation analysis, as well as the analyses of variance and regression between the IV and the concentrations of N and K2O. NF, K+ and the grain yield, responded only to the soil N levels. A significant correlation was observed for the indices of Red Index, Normalized Difference Index and Visible Atmospherically Resistant Index with the NF, which endorses them as favorable in identifying the nutritional standing of corn, with respect to the N level. Not even a single one of the indices evaluated could detect the nutritional ranking of corn in the context of the potassium level.(AU)

O estudo teve como objetivo avaliar índices de vegetação (IV) para detecção do status nutricional do milho com relação ao nitrogênio e potássio por meio de imagens aéreas obtidas por câmera RGB embarcada em veículo aéreo não tripulado. As imagens foram adquiridas em ensaio de níveis de nitrogênio (0, 60, 120 e 180 kg ha-¹) e potássio (0, 50, 100 e 150 kg ha-¹), em blocos ao acaso, fatorial 4 x 4, com três repetições. Coletaram-se dez folhas por parcela na fase de florescimento para avaliação do teor foliar de N total (NF) e K+. Efetuou-se análise de correlação de Pearson, análise de variância e de regressão entre os IV e os níveis de N e de K2O. NF, K+ e a produtividade de grãos responderam apenas aos níveis de N no solo. Houve correlação significativa para os índices Excess Red Index, Normalized Difference Index e Visible Atmospherically Resistant Index com o NF, que os credencia como promissores na detecção do status nutricional do milho em relação ao N. Nenhum dos índices avaliados foi capaz de detectar o status nutricional do milho com relação ao potássio.(AU)

Dissimilarity of parameterized maps generated in geostatistics and the assumption of an automatic software model in precision agriculture / Dissimilaridade de mapas gerados na geoestatística parametrizada e seu pressuposto modelo automático de software na agricultura de precisão

Geostatistics is the main technique used to efficiently determine spatial variability. The objective of this study was to evaluate the applicability of the principles of geostatistics in the use of semivariograms elaborated through parametric monitoring and the assumption automatically made by software in the map preparation of soil chemical attributes. Available phosphorus (P), potassium (K+), calcium (Ca²+), magnesium (Mg²+), base saturation (V%), sulfur (SO4²-), and pH were compared from the soil chemical attributes of 60 samples of a Typical Oxisol collected at a 0-20 cm depth and a distance of 300 m betweenthe points. The maps were compared using error matrices and evaluated by the Global Accuracy (GA), Kappa (K), and Tau (T) indexes. The parameterized semivariograms and the automatic software model assumption did not present a high coincidence for the available P and Mg²+, making it necessary to adjust the semivariogram variables in the spatial analysis as a function of the outliers, sum of squares of residuals, coefficient of determination, and cross-validation to better represent the variability of the data and thus avoid distortions of the sample point range that would affect the adequate representativeness of the attributes, which contrasts with the automatic model generated by the software.

A geoestatística é uma das principais técnicas utilizadas para determinar a variabilidade espacial dos atributos do solo. O objetivo deste trabalho foi comparar o uso de semivariogramas elaborados através do acompanhamento paramétrico da geoestatística e seu pressuposto modelo realizado automaticamente por software na confecção dos mapas de fertilidade do solo. Foram comparados os mapas dos valores de pH, fósforo (P), potássio (K+), cálcio (Ca²+), magnésio (Mg²+), enxofre (SO4²-) e saturação por bases (V%), provenientes de 60 amostras coletadas de área de Latossolo Vermelho Distrófico coletadas na profundidade 0-20 cm e malha de 300 m de distância entre os pontos. Os mapas foram comparados por matrizes de erro e avaliadas pelos índices Exatidão Global, Kappa e Tau. O uso de semivariogramas parametrizados e seu pressuposto modelo automático do software não apresentaram alta coincidência para os teores de P disponível e Mg²+, sendo necessário ajustes das variáveis dos semivariogramas na análise espacial em função dos outliers, soma dos quadrados dos resíduos, coeficiente de determinação e validação cruzada para melhor representação da variabilidade dos dados e assim, evitar distorções do alcance do ponto amostral para uma boa representatividade dos atributos estudados, o que contraria o modelo automático gerado pelo software.

Dissimilarity of parameterized maps generated in geostatistics and the assumption of an automatic software model in precision agriculture / Dissimilaridade de mapas gerados na geoestatística parametrizada e seu pressuposto modelo automático de software na agricultura de precisão

Geostatistics is the main technique used to efficiently determine spatial variability. The objective of this study was to evaluate the applicability of the principles of geostatistics in the use of semivariograms elaborated through parametric monitoring and the assumption automatically made by software in the map preparation of soil chemical attributes. Available phosphorus (P), potassium (K+), calcium (Ca²+), magnesium (Mg²+), base saturation (V%), sulfur (SO4²-), and pH were compared from the soil chemical attributes of 60 samples of a Typical Oxisol collected at a 0-20 cm depth and a distance of 300 m betweenthe points. The maps were compared using error matrices and evaluated by the Global Accuracy (GA), Kappa (K), and Tau (T) indexes. The parameterized semivariograms and the automatic software model assumption did not present a high coincidence for the available P and Mg²+, making it necessary to adjust the semivariogram variables in the spatial analysis as a function of the outliers, sum of squares of residuals, coefficient of determination, and cross-validation to better represent the variability of the data and thus avoid distortions of the sample point range that would affect the adequate representativeness of the attributes, which contrasts with the automatic model generated by the software.(AU)

A geoestatística é uma das principais técnicas utilizadas para determinar a variabilidade espacial dos atributos do solo. O objetivo deste trabalho foi comparar o uso de semivariogramas elaborados através do acompanhamento paramétrico da geoestatística e seu pressuposto modelo realizado automaticamente por software na confecção dos mapas de fertilidade do solo. Foram comparados os mapas dos valores de pH, fósforo (P), potássio (K+), cálcio (Ca²+), magnésio (Mg²+), enxofre (SO4²-) e saturação por bases (V%), provenientes de 60 amostras coletadas de área de Latossolo Vermelho Distrófico coletadas na profundidade 0-20 cm e malha de 300 m de distância entre os pontos. Os mapas foram comparados por matrizes de erro e avaliadas pelos índices Exatidão Global, Kappa e Tau. O uso de semivariogramas parametrizados e seu pressuposto modelo automático do software não apresentaram alta coincidência para os teores de P disponível e Mg²+, sendo necessário ajustes das variáveis dos semivariogramas na análise espacial em função dos outliers, soma dos quadrados dos resíduos, coeficiente de determinação e validação cruzada para melhor representação da variabilidade dos dados e assim, evitar distorções do alcance do ponto amostral para uma boa representatividade dos atributos estudados, o que contraria o modelo automático gerado pelo software.(AU)

Estimation of soil phosphorus availability via visible and near-infrared spectroscopy

Spectroscopic techniques have great potential to evaluate soil properties. However, there are still questions regarding the applicability of spectroscopy to analyze soil phosphorous (P) availability, especially in tropical soils with low nutrient contents. Therefore, this study evaluated the possibility to estimate P availability in soil and its pools (labile, moderately labile and non-labile) via Vis-NIR spectroscopy based on intra-field calibration. We used soils from two different locations, a plot experiment that received application of phosphate fertilizers (Field-A) and a cultivated field where a grid soil sampling was performed (Field-B). We used the technique of diffuse reflectance in the visible and near-infrared (Vis-NIR) to obtain the spectra of soil samples. Predictive modeling for P availability and labile, moderately labile and non-labile pools of P in soil were obtained via partial least squares (PLS) regression; classification modeling was performed via Soft Independent Modeling of Class Analogy (SIMCA) on three P availability levels in order to overcome the limitation on quantifying P via Vis-NIR spectroscopy. We found that isolating P contents as the only variable (Field-A), Vis-NIR spectroscopy does not allow estimating P pools in the soil. In addition, quantification of P available in the soil via predictive modeling has limitations in tropical soils. On the other hand, estimating P content in soil through classes of availability is a feasible and promising alternative.

Estimation of soil phosphorus availability via visible and near-infrared spectroscopy

Spectroscopic techniques have great potential to evaluate soil properties. However, there are still questions regarding the applicability of spectroscopy to analyze soil phosphorous (P) availability, especially in tropical soils with low nutrient contents. Therefore, this study evaluated the possibility to estimate P availability in soil and its pools (labile, moderately labile and non-labile) via Vis-NIR spectroscopy based on intra-field calibration. We used soils from two different locations, a plot experiment that received application of phosphate fertilizers (Field-A) and a cultivated field where a grid soil sampling was performed (Field-B). We used the technique of diffuse reflectance in the visible and near-infrared (Vis-NIR) to obtain the spectra of soil samples. Predictive modeling for P availability and labile, moderately labile and non-labile pools of P in soil were obtained via partial least squares (PLS) regression; classification modeling was performed via Soft Independent Modeling of Class Analogy (SIMCA) on three P availability levels in order to overcome the limitation on quantifying P via Vis-NIR spectroscopy. We found that isolating P contents as the only variable (Field-A), Vis-NIR spectroscopy does not allow estimating P pools in the soil. In addition, quantification of P available in the soil via predictive modeling has limitations in tropical soils. On the other hand, estimating P content in soil through classes of availability is a feasible and promising alternative.(AU)